WO2016197849A1

WO2016197849A1 - Control method, cooling apparatus system, cooling apparatus controller, cooling tower system, cooling tower controller, water pump system, and water pump controller

Info

Publication number: WO2016197849A1
Application number: PCT/CN2016/084341
Authority: WO
Inventors: 姜子炎; 代允闯; 沈启
Original assignee: 邻元科技（北京）有限公司
Priority date: 2015-06-09
Filing date: 2016-06-01
Publication date: 2016-12-15

Abstract

An optimized control method that lowers the total system resource consumption, a water pump system, water pump controller, and control method based on a non-centralized network, a cooling apparatus system, cooling apparatus controller, and control method based on a non-centralized network, and a cooling tower system, cooling tower controller, and control method based on a non-centralized network. Under usual circumstances, the solution method to this type of problems is to collect overall information to a central location, and a superordinate intelligent decision maker considers the overall information and performs optimized task distribution. However, this type of solution has the disadvantages of low decision making efficiency, bad adaptability, and weak expandability. To overcome the disadvantages, the network is formed as a non-centralized network, and different function modules conduct information exchanges therebetween so as to complete system calculations.

Description

Control method, chiller system, chiller controller, cooling tower system, cooling tower controller, pump system and pump controller

This application requires a Chinese patent application with the application date of June 9, 2015, application number 201510314137.9 and titled "Pump system based on centerless network, pump controller and control method". The application date is June 9, 2015. The application number is 201510313694.9 and the Chinese patent application entitled "A Cold System, Cold Controller and Control Method Based on Centerless Network", the application date is June 9, 2015, the application number is 201510314163.1 and the title is The Chinese patent application based on the cooling tower system, cooling tower controller and control method based on the central network, and the application date is June 9, 2015, the application number is 201510314172.0 and the title is “Reducing the total resource consumption of the system”. The priority of the Chinese Patent Application, which is incorporated herein by reference.

Technical field

The invention relates to the field of control, in particular to an optimized control method for reducing the total resource consumption of a system, a pump system based on a centerless network, a water pump controller and a control method thereof, and a cold machine system based on a centerless network , a cold engine controller and control method, and a cooling tower system, a cooling tower controller and a control method based on a centerless network.

Background technique

In the related art, there is a similar technical problem in various technical fields, that is, how to make a group of devices or a group of devices cooperate to make the energy consumed by the devices or devices when the devices or devices complete a certain amount of total task requirements or The resources are the lowest, or some of the qualifications of these devices or devices are met, so this type of technical problem can be referred to as an issue of optimal allocation.

For example, in a group of cold machines in a central cold station, the total amount of cooling provided by these cold machines needs to meet the cooling capacity of the end equipment, that is, the difference between the total cold value provided by the cold machine and the cold demand at the end should be less than A threshold, then there is a technical problem of how to jointly control the cold machine. The performance of the cold machine is different under different cooling output. Usually, the relationship between the performance and the cooling capacity is the shape of an upwardly convex saddle. How to reasonably determine which chillers are specifically opened and how the cooling capacity is distributed among these chillers makes the total energy consumption of these chillers a practical problem, and is also a problem of concern to researchers in the field because The total power consumption of a cold machine is very large in one year. Reducing energy consumption has a very positive significance for reducing the operating costs of buildings and even saving energy and reducing emissions.

Similar to the cold group control, there are group control problems of the water pump, group control problems of the cooling tower, group control problems of the VAV variable air volume box, and the like.

The commonality of this type of problem is that a one-dimensional quantity (task) is reasonably distributed to each functional module in the system so that the total amount of resources (such as power consumption) consumed by these functional modules is the lowest.

Usually, the solution to this kind of problem is to collect the global information to the central head. The intelligent decision maker of the upper layer optimizes the assignment after considering the global information. However, the shortcoming of this solution lies in the decision. The formulation is inefficient, the adaptability is poor, and the scalability is not strong. If the system changes or expands, the global information needs to be collected and described again and the algorithm at the central head is changed, resulting in poor generality, poor robustness, etc. Disadvantages.

Summary of the invention

The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, a first object of the present invention is to provide an optimized control method for reducing the total resource consumption of a system. The method has the advantages of strong versatility, good robustness and good scalability.

A second object of the present invention is to provide an optimization control method for allocating tasks to various functional units in a system, which has the advantages of high flexibility and good expandability.

A third object of the present invention is to provide a water pump system control method based on a centerless network, a cooling tower system control method, and a cold machine system control method. The method has the advantages of high control efficiency, good robustness and good expandability.

A fourth object of the present invention is to provide a water pump system, a cooling tower system and a chiller system based on a centerless network.

A fifth object of the present invention is to provide a water pump controller, a cooling tower controller, and a cold machine controller.

In order to achieve the above object, an embodiment of the first aspect of the present invention discloses an optimized control method for reducing the total resource consumption W of a system, the system comprising a plurality of functional modules, and the total tasks completed by all functional modules are required to satisfy the total task. Task requirement Q0, each function module needs to consume a certain amount of resources Wi while completing the task, and the performance Ei of each function module and its completed task Qi conform to a certain functional relationship, and the resource consumption Wi of each functional module and its The completed task becomes positively correlated with the performance Ei, and the control method includes the steps of assigning the total system task to each functional module, wherein the total resource consumption of the system is the lowest or lower, and the task assignment is specific. The method includes the following steps: performing information interaction between each functional module to complete the system operation, and when the system operation converges, the task undertaken by each functional module is obtained, and the total resource consumption of the system is lowest or lower under the task assignment.

An embodiment of the second aspect of the present invention discloses an optimization control method for allocating tasks to various functional units in a system. The system includes several functional modules, and the total tasks completed by all functional modules are required to meet the total task requirement Q0. functional modules task should be higher than the lower limit of the task Q _low, and running the better functional module, said task assignment comprises the steps of: for information exchange between the various functional modules to complete the system operation when the system operation After completion, the tasks undertaken by each function module are obtained. The function modules that are in the running state under this task allocation scheme are the most and the tasks completed by each function module are higher than the task lower limit Q _low .

An embodiment of the third aspect of the present invention discloses a pump system control method based on a centerless network, comprising the steps of: separately setting a controller for each water pump in the water pump system, and interconnecting all the controllers Forming a centerless network; when the water pump controller determines that a certain trigger condition is reached, the water pump controller initiates an adjustment task; if there is a water pump controller in the system that initiates the adjustment task, the water pump controller in the system begins to Adjacent pump controllers exchange information; after several information interactions, the entire system reaches a predetermined convergence condition to determine the optimized operating parameters of each pump. The controller controls the corresponding water pump to reach a corresponding operating state according to the optimized operating parameter; if there is no water pump controller that initiates the regulating task in the system, the pump operating parameter is kept unchanged.

An embodiment of the fourth aspect of the present invention discloses a water pump system based on a centerless network, comprising: a plurality of water pumps, wherein the plurality of water pumps are arranged in parallel; and a plurality of controllers, the plurality of controllers are correspondingly associated with each other The plurality of water pumps are connected; the water pump system is operated using a control method as described in the above embodiments.

An embodiment of the fifth aspect of the present invention discloses a water pump controller in which all water pump controllers are interconnected to form a centerless network. When the water pump controller determines that a certain trigger condition is reached, the water pump controller is used to initiate an adjustment task. When there is a water pump controller in the system that initiates the adjustment task, the water pump controller starts to exchange information with its adjacent water pump controller, and after a number of information interactions, reaches a predetermined convergence condition, thereby determining each pump optimized. The operating parameters, the water pump controller controls the corresponding water pump to reach the corresponding operating state according to the optimized operating parameters, and keeps the corresponding pump operating parameters unchanged when there is no water pump controller that initiates the regulating task in the system.

An embodiment of the sixth aspect of the present invention discloses a cooling tower system control method based on a centerless network, comprising the steps of: separately providing a cooling tower controller for each cooling tower in the cooling tower system, and The cooling tower controllers are interconnected to form a centerless network; when the cooling tower controller determines that a certain trigger condition is reached, the cooling tower controller initiates an adjustment task; if there is a cooling tower controller in the system that initiates the adjustment task, the The cooling tower controller in the system begins to exchange information with its adjacent cooling tower controller; after several information interactions, the system determines the optimized operating parameters of each cooling tower, and the cooling tower controller is operated according to the optimization. The parameters control the corresponding cooling tower to reach the corresponding operating state; if there is no cooling tower controller in the system to initiate the regulating task, the cooling tower operating parameters are kept unchanged.

An embodiment of the seventh aspect of the present invention further discloses a cooling tower system based on a centerless network, comprising: a plurality of cooling towers, the plurality of cooling towers being arranged in parallel; a plurality of cooling tower controllers, the plurality of cooling Tower controllers are associated with the plurality of cooling towers in a one-to-one correspondence, wherein all of the cooling tower controllers are interconnected to form a centerless network; the cooling tower system utilizes a control method as described in the sixth aspect of the invention above Come run.

An embodiment of the eighth aspect of the present invention also provides a cooling tower controller, wherein all cooling tower controllers are interconnected to form a centerless network, and the cooling tower controller is used when the cooling tower controller determines that a certain trigger condition is reached Initiating an adjustment task, when there is a cooling tower controller that initiates an adjustment task in the system, the cooling tower controller begins to interact with its adjacent cooling tower controller, and determines each cooling tower after several information interactions. After the operating parameters, the cooling tower controller controls the corresponding cooling tower to reach a corresponding operating state according to the optimized operating parameters, and maintains the corresponding cooling tower operating parameters when there is no cooling tower controller that initiates the regulating task in the system. change.

An embodiment of the ninth aspect of the present invention discloses a chiller system control method based on a centerless network, comprising the steps of: separately setting a chiller controller for each chiller in the chiller system, and All the cold controllers are interconnected to form a centerless network; when the cold controller determines that a certain trigger condition is reached, the cooling controller initiates an adjustment task; if there is a cold controller in the system that initiates the adjustment task, The cold controller in the system begins to exchange information with its adjacent cold controller; after several information interactions, the entire system reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each cold machine. The cold machine controller controls the corresponding cold machine to reach the corresponding running state according to the optimized operating parameter; if there is no cold machine controller that initiates the adjusting task in the system, the cold running parameter is kept unchanged;

An embodiment of the tenth aspect of the present invention discloses a cold machine system based on a centerless network, comprising: a plurality of cold machines; a plurality of cold machine controllers, wherein the plurality of cold machine controllers are correspondingly A plurality of cold machines are connected, wherein all of the cold machine controllers are interconnected to form a centerless network; the cold machine system is operated using a control method as described in the above-described ninth aspect of the invention.

An embodiment of the eleventh aspect of the present invention discloses a cold machine controller, all of which are interconnected to form a centerless network. When the cold machine controller determines that a certain trigger condition is reached, the cold machine controller is used for Initiating an adjustment task, when there is a cold controller that initiates an adjustment task in the system, the cold controller begins to exchange information with its adjacent cold controller, and reaches a predetermined convergence condition after several information interactions. Thereby determining the optimized operating parameters of each chiller, the chiller controller controlling the corresponding chiller to reach the corresponding operating state according to the optimized operating parameters, and not initiating the adjusting task in the system When the cold controller is used, keep the corresponding cold running parameters unchanged.

DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

1 is a schematic structural view of a water pump system to which the control method is applied according to an embodiment of the first aspect of the present invention.

Figure 2 is a schematic view showing the position of a pressure difference measurement value of a water pump system of an embodiment of the first aspect of the present invention.

3 is a detailed logic block diagram of a water pump system control algorithm of an embodiment of the first aspect of the present invention.

4 is an iterative calculation process after a certain water pump initiates an adjustment task according to an embodiment of the first aspect of the present invention.

FIG. 5 is a flow diagram of another type of information transfer in an iterative calculation process according to an embodiment of the first aspect of the present invention.

Figure 6 is a schematic diagram showing a state change of the water pump in the iterative calculation process of the embodiment of the first aspect of the present invention.

Figure 7 is a schematic view showing another state change of the water pump in the iterative calculation process of the embodiment of the first aspect of the present invention.

Fig. 8 is a schematic view showing the structure of a pyramid when the water pump opening combination is determined according to the embodiment of the first aspect of the present invention.

9 is a schematic structural view of a chiller system to which the control method is applied according to an embodiment of the second aspect of the present invention.

FIG. 10 is an iterative calculation process after a certain cold machine initiates an adjustment task according to an embodiment of the second aspect of the present invention.

11 is a flow chart showing another type of information transfer in an iterative calculation process according to an embodiment of the second aspect of the present invention.

Figure 12 is a schematic diagram showing a state change of the cold machine in the iterative calculation process of the embodiment of the second aspect of the present invention.

Figure 13 is another embodiment of the cold machine of the second aspect of the present invention in an iterative calculation process A schematic diagram of the state changes.

Figure 14 is a schematic view showing the structure of a cooling tower system to which the control method is applied according to an embodiment of the third aspect of the present invention.

Figure 15 is a schematic view showing the structure of a pyramid when determining a cooling tower opening combination according to an embodiment of the third aspect of the present invention.

Figure 16 is a diagram showing the overall situation of the control method provided by the present invention.

17 is a schematic flow chart of a method for controlling a water pump system based on a centerless network according to an embodiment of the present invention.

18 is a schematic flow chart of a method for controlling a cold system based on a centerless network according to an embodiment of the present invention.

FIG. 19 is a schematic diagram of a pyramid structure when determining a cold start combination according to an embodiment of the present invention.

20 is a schematic flow chart of a method for controlling a cooling tower system based on a centerless network according to an embodiment of the present invention.

21 is a schematic diagram of a calculation process for determining whether a waterway of a cooling tower is turned on according to an embodiment of the present invention.

detailed description

In the following, an optimized control method for reducing the total resource consumption of the system according to an embodiment of the present invention will be described in more detail according to the accompanying drawings.

As shown in FIG. 16, the present invention provides an optimized control method for reducing the total resource consumption W of a system. The system includes several functional modules, and the total tasks completed by all functional modules need to meet the total task requirement Q0, each The function module needs to consume a certain amount of resources Wi while completing the task. The performance Ei of each function module and its completed task Qi conform to a certain functional relationship, and the resource consumption Wi of each function module is positively correlated with its completed task. The performance Ei is negatively correlated. The control method includes the step of allocating the system total task Q0 to each functional module, and the total resource consumption of the system is the lowest or lower under the task assignment, and the task assignment specifically includes the following steps:

Information interaction between various functional modules to complete system operations, when the system calculates After the convergence, the task undertaken by each functional module is obtained, and the total resource consumption of the system is lowest or lower under the task assignment.

The core idea of the control method provided by the present invention is to interconnect functional modules in the system to form a centerless network. The centerless network refers to the equal status of each node in the network, and the entire network is flat. Without the concept of a center or a summit, the network can be chained, ring-shaped, or grid-like or star-shaped, that is, all nodes in the network are interconnected by a certain topological relationship, while in the topology Information is transmitted between nodes that are interconnected in a relationship, and no information is exchanged between nodes that are not associated in a topological relationship.

By forming a plurality of functional modules into a centerless network, the system without a central network constitutes an intelligent system with self-organizing functions, each functional module constitutes an intelligent node, and each node has a completely equal relationship. Completing a self-organizing operation through the process of mutual negotiation and information interaction, so that the global result is optimal; this calculation process does not require a decision at the center of the higher level, completely flat structure, expandability Strong, even if you add new functional modules, or the topological relationship between functional modules changes, you can complete the system operation through self-identification self-adaptation process.

[Example 1]

According to an embodiment of the first aspect of the present invention, the control method can be applied to a water pump system to complete group control of the water pump system to save energy consumption of the water pump system. After being specifically applied to a water pump system, the present invention provides an optimized control method for reducing the power consumption of a water pump system. Each water pump of the water pump system becomes a functional module, and the total task of all pumps is that the amount of water to be delivered needs to meet the end. The total water demand, each pump needs to consume a certain amount of electric power while completing a certain amount of water delivery, and the performance of each pump, that is, the efficiency of the pump, is in a certain functional relationship with its water flow (this functional relationship) It is defined by the performance curve of the pump. The performance curve of the pump is a convex curve. For details, refer to Figure 6 or Figure 7). The electric power of each pump is proportional to the amount of water it delivers, which is inversely proportional to the efficiency. The process of allocating the total water volume of the system to each water pump is completed, and the water pump in the system needs to perform information interaction, and the information interaction includes the following steps:

A. by a certain pump (or the feed pump is installed on the controller, the controller initiates the operation) Initiating the operation, which maximizes the efficiency as the target efficiency value, calculates the flow and power consumption under the target efficiency value, calculates the flow margin based on the total flow demand, and sends the target efficiency value and the flow margin as the delivery information to Adjacent pump.

After the adjacent water pump receives the transmission information, the new target efficiency value is calculated according to the received target efficiency value, and the flow rate is calculated, and the new flow margin is calculated according to the received flow margin;

If the absolute value of the flow margin is greater than a certain threshold, the new target efficiency value and the new flow margin obtained after the calculation are continuously transmitted as the delivery information to the adjacent water pump;

If the flow margin is less than or equal to a certain threshold, it is judged whether the result of the operation is valid, if:

If the operation result is valid, the operation is converged and the delivery information is no longer sent.

If the operation result is invalid, the pump continues to initiate the operation and step A is executed.

If there is no more pump in the system to send or receive the transmission information, the system converges, and each pump distributes the task according to the calculation result of the system;

The criterion for judging whether the operation result is valid is but not limited to: if the total system power consumption is equal to or lower than the operation, the operation is valid, and if the total system power consumption is higher than the operation, the operation is invalid.

More specific explanations are given below.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a schematic view showing the structure of a water pump system according to an embodiment of the present invention. In conjunction with FIG. 1, a water pump system includes n (generally n is a positive integer greater than one) parallel pumps 100, each pump 100 being provided with a controller 200. It should be noted that in practical applications, a plurality of pumps connected in series in the pump system may be encountered. In this case, only a plurality of series water pumps need to be equivalent to a single water pump according to fluid mechanics knowledge, thereby A controller 200 can be configured for the equivalent pump.

Referring again to FIG. 1, each controller 200 can also be coupled to a corresponding differential pressure sensor 400 to obtain a transmitted differential pressure signal from a corresponding differential pressure sensor 400. The controller 200 can also be coupled to the frequency converter 300 of the water pump so that The change of the operating parameters of the water pump 100 is achieved by controlling the corresponding frequency converter 300. That is, the controller 200 can change the operating parameters of the corresponding water pump by controlling the frequency converter connected to the water pump.

Pump control in the system when there is a pump controller that initiates the adjustment task in the system The device begins to interact with its adjacent pump controller.

The process of information interaction is an iterative calculation process. The iterative calculation process of this information interaction is specifically described below according to FIG. 3 to FIG.

3 is a basic logic block diagram of an information interaction process according to an embodiment of the present invention, and FIG. 4 is a specific process of data interaction and iterative calculation between each water pump controller after a certain water pump controller initiates an adjustment task according to an embodiment of the present invention. .

As shown in Figure 4, each long box represents one iteration completed in all pumps. In this example, four pumps are included. The number at the top of the long box is the flow value before each pump iteration (such as the top length) The number at the top of the box is 37.42). The flow value is calculated by the pump itself based on the embedded model (performance curve) and the measured head. The number at the bottom of the long frame is the flow value after the completion of this iteration (eg, the top The number at the bottom of the long box is 54.09); the direction of the arrow indicates the flow of information between the pumps. The value above the arrow is the flow adjustment margin ΔG, and the lower is the efficiency adjustment expectation ε.

In combination with Figure 4 and Figure 5, the initial operating conditions are that all four pumps are running, the initial flow rate is 37.42L / s, at this time there is a situation where the pressure difference does not meet the set value requirements, in any operation The pump discovered this condition and initiated an adjustment task, in which the conditioning task was initiated by the No. 2 pump. The initial target efficiency point of the No. 2 pump at this time is the highest efficiency point, that is, the efficiency adjustment expectation ε=1, and the calculated flow rate is 54.09 L/s when the operating point is adjusted to the efficiency adjustment expectation ε is 1, Figure 6 And Figure 7 shows the performance curve of the pump. The abscissa is the flow of the pump and the ordinate is the efficiency of the pump. According to the performance curve, the pump can calculate the flow of the pump under the efficiency adjustment. As shown in Fig. 6, 1 point is the operating point of the initial operation, and 2 points is the highest efficiency point after the adjustment. At this time, the information transmitted by the controller corresponding to the No. 2 pump is the flow adjustment margin ΔG=-16.67L. /s, efficiency adjustment expectation ε = 1, the so-called flow adjustment margin refers to the difference between the current flow of the pump and the flow set value.

After receiving the information, the No. 3 pump (that is, after the controller corresponding to the No. 3 pump receives the information), compares the expected efficiency adjustment expectation with its previous efficiency adjustment expectation. At this time, the No. 3 pump It is the first adjustment. The last time the efficiency adjustment is expected to be 0, so the expected value of the efficiency adjustment is different. Then the efficiency adjustment expectation passed by the neighboring water pump is used, and the adjusted flow rate is calculated to be 54.09L/s. The flow adjustment margin ΔG=-33.34L/s, that is, its own flow adjustment margin plus the flow adjustment allowance passed to him by the neighbor. The quantity and efficiency adjustment is expected to pass ε = 1 to its neighbor No. 4 pump.

The calculation logic of the No. 4 pump is the same as that of the No. 3 pump, and will not be described here. After the adjustment, the updated information is transmitted to the No. 1 pump.

The flow adjustment margin received by the No. 1 pump is -50.01L/s, which is greater than the current flow rate of 37.42L/s. Therefore, the No. 1 pump calculates that the new flow should be zero and adjusts the updated flow. The balance ΔG=-12.59 L/s and the efficiency adjustment expected ε=1 are transmitted to the No. 2 pump.

After receiving the information, the No. 2 pump compares the expected efficiency adjustment expectation with its previous efficiency adjustment expectation ε=1, which is the same, but the flow adjustment margin does not converge, so the flow adjustment is based on the received flow. The quantity adjusts the efficiency adjustment expectation. As shown in Fig. 6 or Fig. 7, if the flow adjustment margin is negative, that is, the current flow is greater than the flow set value, the efficiency adjustment is expected to be adjusted to the left, and the flow assumed by itself is reduced. If the flow adjustment margin is positive, that is, the current flow is less than the flow set value, the efficiency adjustment is expected to be adjusted to the right to increase the flow assumed by itself;

In the above description, the adjustment of the target efficiency value may be a fixed step size, that is, the step size of each adjustment may be 0.01 or less; of course, the adjustment process may also be a variable step size, and the step size may be based on the flow rate. The size of the adjustment margin is dichotomous or descending, which allows for faster convergence. As shown in FIG. 4, in this example, the adjusted efficiency adjustment is expected to be 0.93, and the corresponding flow adjustment margin ΔG=-9.42 L/s, and the No. 2 pump transmits the two pieces of information as the transmission information to the No. 3 pump. .

By analogy, after each pump receives the information from the neighbor, it adjusts according to the same logic. When the flow adjustment margin satisfies the set convergence condition |ΔG| ≤ δ, the absolute value of the flow adjustment margin is less than Or equal to the preset adjustment margin threshold, the pump controller determines whether the system energy consumption is better than before the adjustment task is initiated. If the adjustment task is superior to the adjustment task, the pump controller considers that the calculation is convergent and effective, thereby No longer transfer information to the outside, the advantage is that the energy consumption of the system is equal to or lower than the adjustment before adjustment; if the system energy consumption is higher than before the adjustment task is initiated, the pump controller resets the target efficiency point. The target efficiency point is lower than the initial target efficiency point, and the transmission information is sent out again, and the calculation is restarted until the system reaches convergence, and the adjustment of the target efficiency point here may be a fixed step adjustment, such as an initial value of 1, The latter is 0.99, 0.98, 0.97. And so on, Of course, the adjustment of the target efficiency point can also be variable step size.

It should be noted that in order to avoid the frequent start and stop of the pump, the controller needs to consider whether it will cause the pump to be frequently turned on when calculating the new pump flow. For example, if the pump is turned on within a certain period of time (such as within half an hour) The pump flow rate will not be zero, that is, the pump that has been turned on in a short time will not make the decision that I want to close, and if it is turned off within a certain period of time, the calculated pump flow will not be greater than zero, that is, once Pumps that have been turned off in a short period of time will not make the decision I want to open.

The information transmission direction of the water pump can take many forms. In the above example, the information transmission direction of the water pump is transmitted from the No. 2 pump to the No. 3 water pump, the No. 3 pump is transmitted to the No. 4 water pump, and the No. 4 pump is transferred to the No. 1 water pump. However, the direction of this information transmission is not fixed and unique. It can also be other information transmission directions. As shown in Figure 5, it can be transmitted from No. 1 pump to No. 2 pump, and No. 2 pump to No. 3 pump. No. 3 pump is transferred to No. 4 pump, No. 4 pump is transferred to No. 3 pump, No. 3 pump is transferred to No. 2 pump, No. 2 pump is transferred to No. 1 pump, etc., that is, the flow direction of this information It is flexible and variable.

When the system reaches the predetermined convergence condition, the optimized operating parameters of each pump are determined, the controller calculates the start and stop state and/or the rotational speed of the water pump according to the determined operating parameters, and the controller changes the water pump to the new operating state. .

The convergence of a single controller is a prerequisite for the convergence of the entire system. When a controller receives the transmitted information from the neighbor, it calculates according to the received information, and the calculated absolute value of the flow adjustment margin is less than the set value. The flow adjusts the margin threshold, and the efficiency of the current system (the update can be calculated in real time during the transfer) is not less efficient than before the adjustment (or the energy consumption of the system is not higher than before the initiation of the adjustment task), then the controller considers the calculation to converge and Valid, so that no more information is passed out; second is the convergence of the system: when a controller in the system receives the delivery information, it starts the timer, if no controller receives the neighbor's delivery within a given period of time Information, that is, without new iterative calculation of the stimulus, the system converges, and the result of the operation currently saved by each controller is the final result, which is output as a control signal.

In the above calculation process, the required pump performance curve or pump performance parameter, differential pressure set value, preset differential pressure deviation threshold, and preset flow adjustment margin threshold can be built into Among the controllers of the water pump, or stored in advance in the control system of the water pump.

The specific method of information exchange between the pumps can also adopt another algorithm, which can first determine the opening combination of the water pump and then determine the specific flow rate of the opened water pump. After mathematical derivation, when the pump combination is determined, the problem of how the total flow is optimally distributed among the pumps becomes very simple, that is, according to the principle that the pump energy consumption function is equal to the flow derivative.

In addition, there are several ways to determine the opening combination of the pump. E.g:

In the first mode, all the water pump controllers are formed into a link through the spanning tree process. The two ends of the chain are the starting node v ₁ and the terminating node v _m , respectively, or the chain head and the chain tail, and then point by point along the chain. Calculate, terminate the node calculation and make the final decision, and then return the result point by point. The entire search process is completed once, without loop iteration. The specific algorithm is:

1) The chain starts the task and propagates the spanning tree (chain) instruction until the end of the chain;

2) Returning the spanning tree instruction point by point from the chain to complete the establishment of the spanning tree;

3) Calculate and pass the following information at the beginning of the chain, the value X ₀ when it is closed and the value X ₀ -x ₁ when it is turned on, x ₁ is the flow when the chain pump works at the optimum efficiency point, and Do the following operations point by point;

4) After each node receives the information (is an array containing k numbers), for each of the numbers, calculate the flow value that it joins and works at the best efficiency point and does not join, and then the new array ( a neighbor node that is sent to it for an array containing 2k numbers;

5) After receiving the data, the end node internally calculates, finds the two sets of combinations closest to X ₀ , and then passes the two sets of combinations back to all nodes.

6) Each node knows if it should be turned on, and then the open node begins to calculate the optimal traffic distribution.

This method adopts point-by-point calculation, and each node considers whether to participate or not to participate in combination at the same time, thereby forming a pyramid-shaped architecture, as shown in FIG. This means that the last node is passed, and after the last node completes the combined calculation, there are 2m combinations, including all cases where they do not participate. So this algorithm lists all the combinations in a limited number of steps and is complete.

The node at the end selects the two combinations closest to X ₀ to actually select the two combinations of the flow values from the previous node that are closest to zero, one of which is greater than zero and one is less than zero. The corresponding combination of the pump is the alternative for further calculation of the optimal flow distribution. After obtaining the alternative, the system will calculate the optimal flow division for each alternative, and then select one of them through arbitration. .

Further, when the end node selects the two groups closest to the distance X ₀ , if the selected combination causes the water pump to frequently start and stop, the combination will not be selected for the safe operation of the device. The end node will retreat to the next level, and the choice will not cause the device to have a frequent start and stop combination, even if such a combination is not closest to X ₀ . The following describes how to distribute the flow in the pump that is turned on.

Further, when the pumping combination is determined, the problem of how the total flow is optimally distributed among the various devices becomes simple, that is, the pump energy consumption function is distributed on the principle that the derivative of the flow is equal. Therefore, the equal derivative is transmitted between the respective water pump controllers. In a certain iteration, each opened water pump calculates its own corresponding flow rate according to the received value (calculated according to the energy consumption-flow relationship curve) And the curve can be obtained by the performance parameter or performance curve of the water pump), and then the flow of each open pump is globally summed. If it is greater than the total demand of the flow demand X ₀ , the derivative value is reduced; if less than the total flow demand X ₀ The requirement is to increase the derivative value. The pumps then recalculate the respective flows based on the new derivative values received and iterate. Since the relationship between the derivative and the energy value is monotonic, the iterative adjustment process converges quickly.

To determine the opening combination of the pump, there is also a second algorithm, mode 2.

The algorithm is that each pump controller has an initial opening probability, such as 0.5. After a pump controller initiates a calculation task, each pump controller (or node v _i ) performs the following rules:

(1) The node v _i initiates a global weighted summation task, that is, the sum of the results of each node's own opening probability multiplied by its own x _i ^* (the flow rate of the pump at the optimal efficiency point);

(2) After the node v _i receives the weighted sum of the nodes other than itself, the result is compared with the result of adding its own opening probability multiplied by its own x _i ^* , if the result after adding is not added The result is closer to X ₀ , indicating that it is better to join, thus increasing your own probability of opening; otherwise, reducing your own probability of opening;

(3) After a certain number of iterations, the opening probability of each node will tend to be stable, and the node whose opening probability converges to 1 or greater than a certain threshold will be turned on; A node whose convergence probability is 0 or less than a certain preset threshold is turned off.

In mode 2, in fact, each node is initiating a global weighted summation calculation task, wherein the rules for adjusting the probability of self-opening and the manner and magnitude of each probability adjustment can be adjusted.

It should be noted that in order to ensure the safe operation of the equipment, the state of frequent start and stop is a criterion with high priority. Therefore, the pump performs the above operation process without frequent start and stop. In other words, each pump They all know whether they have been turned on or off in a short period of time, so they will not make decisions to start and stop frequently during the operation. For example, a pump that has just been shut down may directly participate in the calculation of new adjustment tasks. Put yourself in a closed state without the situation that your own turn-on probability approaches 1.

After determining the opening combination of the water pump by the above algorithm, the algorithm as described above can be used to determine the specific flow rate of the open water pump.

The optimized operating parameters of the pump calculated by the spanning tree algorithm described above are the theoretical optimal solutions, so it is no longer necessary to judge whether the total energy consumption of the system is lower than before the initiation of the adjustment task, but also when determining the opening combination of the water pump. The process of arbitration is required to protect the pump from frequent start and stop. For example, when the pump is turned on, the pump that has been turned on/off in a short time is not set to the off/on state.

According to the control method provided by the invention, the parallel water pumps are independently and independently coordinated to complete the control target, the optimized control scheme is calculated distributedly, the workload of the manual configuration debugging in the traditional control mode is reduced, and the plugging of the control device is realized. Use, improve the control efficiency, robustness and scalability of the system.

[Example 2]

According to an embodiment of the second aspect of the present invention, the control method can be applied to a chiller system to complete group control of the chiller system to save energy consumption of the chiller system. After being specifically applied to a chiller system, the present invention provides an optimized control method for reducing the power consumption of a chiller system. Each chiller of the chiller system becomes a functional module, and the total task of all chillers is completed. The total cooling load value provided must meet the total cooling load demand at the end. Each chiller needs to consume a certain amount of electricity while providing a certain cooling load. The efficiency of each chiller - that is, the overall efficiency of the chiller - Having a certain functional relationship with its cold load value (this The functional relationship is defined by the performance curve of the chiller. The performance curve of the chiller is a convex curve. For details, refer to Figure 12 or 13), and the power consumption of each chiller is proportional to the cooling load it provides. In inverse proportion to efficiency, in order to complete the allocation process of the total cold load value of the system to each cold machine, the cold machine in the system needs to perform information interaction, and the process of information interaction is similar to the information interaction of the embodiment of the first aspect. The difference is that the information passed is different. Specific steps are as follows:

A. The operation is initiated by a cold machine (or the controller is installed on the cold machine, and the controller initiates the operation), and the efficiency is maximized as the target efficiency value, and the cold load value and power consumption under the target efficiency value are calculated. Calculate the cooling allowance based on the total cooling demand, and send the target efficiency value and the cooling margin as delivery information to its adjacent cold machine.

After the adjacent cold machine receives the transfer information, the new target efficiency value is calculated according to the received target efficiency value and the cold amount is calculated, and the new cold amount margin is calculated according to the received cold amount margin;

If the absolute value of the cooling margin is greater than a certain threshold, the new target efficiency value obtained by the calculation and the new cooling margin are continuously transmitted as transmission information to the adjacent cold machine;

If the cooling margin is less than or equal to a certain threshold, it is judged whether the result of the operation is valid, if:

If the operation result is invalid, the cold machine continues to initiate the operation and performs step A.

If there is no more cold machine in the system to send or receive the transmission information, the system converges, and each cold machine assigns tasks according to the calculation result of the system;

The criterion for judging whether the operation result is valid is: if the total power consumption of the system is equal to or lower than the operation, the operation is valid, and if the total power consumption of the system is higher than before the operation, the operation is invalid.

More specific explanations are given below.

Figure 9 is a schematic view showing the structure of a chiller system according to an embodiment of the present invention. As shown in Figure 9, the chiller system includes n (generally n is a positive integer greater than 1) chillers, each chiller is equipped with a chiller controller.

When there is a cold controller in the system that initiates the tuning task, the cold controller in the system begins to interact with its neighboring cold controller.

The process of information interaction is an iterative calculation process, according to Figure 10 to Figure 13 below. The iterative calculation process of this information interaction is specifically explained.

FIG. 10 is a specific process of data interaction and iterative calculation between respective cold machine controllers after a certain cold machine controller initiates an adjustment task according to an embodiment of the present invention.

As shown in Figure 10, each long box represents one iteration completed in all chillers. In this case there are four chillers, and the number at the top of the long box is the cooling load before each chiller iteration (the cold load can be The monitoring program inside the refrigerator is given, or input by external measurement means. The number at the bottom of the long frame is the cooling load after the completion of this iteration; the direction of the arrow indicates the flow of information between the cold machines, above the arrow The value is the cooling load adjustment margin ΔQ, and the following is the efficiency adjustment expected ε.

In combination with Figure 10, the initial operating conditions are two cold machines running, the initial cooling load is 1710kW, but opening two cold machines at this time is not necessarily the optimal operation plan, and any running cold machine initiates the adjustment task. (In this example, the No. 2 cooler initiates the adjustment). No. 2 cold machine calculation When its working point is adjusted to the highest efficiency point, that is, the efficiency adjustment expectation ε=1, its cold load is 1250kW, as shown in Figure 12, 1 point is the initial operating condition point, 2 points The highest efficiency point after the adjustment, the information transmitted outward is the cold load adjustment margin ΔQ=+460kW (the difference between the current cooling load and the cold load setting value), and the efficiency adjustment expectation ε=1.

After receiving the information, the No. 3 chiller will compare the expected efficiency adjustment with its previous adjustment expectation (when the No. 3 chiller is the first adjustment and the last adjustment vacancy is 0), if Different, the efficiency adjustment expectation passed to the neighbor chiller is used to calculate its adjusted cooling load of 1250 kW, and then the cooling load adjustment margin ΔQ=+920 kW at this time (the own cooling load adjustment margin plus neighbors) The cooling load adjustment margin passed to it) and the efficiency adjustment expected ε = 1, passed to its neighbor No. 4 cold machine.

The cold load adjustment margin received by the No. 4 chiller is +920 kW, while the current No. 4 chiller is in the closed state, so the No. 4 chiller will start, and adjust its own working point to the efficiency adjustment expected ε=1, ie its The cooling load undertaken is 1250kW. Then, the updated cooling load adjustment margin ΔQ=-330 kW, and the efficiency adjustment expected ε=1 is transmitted to the No. 1 refrigerator.

The No. 1 chiller is in the closed state, and the cold load adjustment margin ΔQ=-330 kW is received, that is, it is reduced to the assumed cooling load, so no adjustment is made, and this information is continuously transmitted to the No. 2 chiller.

After receiving the information, the No. 2 cooler will receive the expected efficiency adjustment and its last adjustment. The section expects ε=1 to be compared, which is the same, but the cooling load adjustment margin does not converge, and thus the efficiency adjustment expectation is adjusted according to the received cooling load adjustment margin. The adjustment process may be a fixed step length. If the cold load adjustment margin is a negative value, that is, the current cold load is greater than the cold load set value, the efficiency adjustment is expected to be adjusted to the left to reduce the cold load assumed by itself; The load adjustment margin is positive, that is, the current cooling load is less than the cold load setting value, the efficiency adjustment is expected to be adjusted to the right, and the cooling load undertaken by the user is increased, and the step size of each adjustment may be 0.01 or less. Efficiency adjustment The expected adjustment process can also be variable step size. The step size can be adjusted according to the cooling load, and the dichotomy or descent method can be used to achieve a faster convergence speed. In this example, the adjusted efficiency adjustment is expected to be ε = 0.94, and the corresponding cooling load adjustment margin ΔQ = 1150 kW, and these two pieces of information are transmitted to the No. 3 refrigerator.

And so on, after each cold machine receives the information from the neighbor, it is adjusted according to the same logic. When the cooling capacity adjustment meets the set convergence condition |ΔQ| ≤ δ, the cooling capacity adjustment margin When the absolute value is less than or equal to the preset adjustment margin threshold, the chiller controller determines whether the system energy consumption is better than before the adjustment task is initiated. If the adjustment task is superior to the adjustment task, the chill controller considers that The calculation is convergent and effective, so that the information is no longer transmitted outward. The advantage is that the energy consumption of the system after adjustment is equal to or lower than before the adjustment; if the energy consumption of the system is higher than before the initiation of the adjustment task, the cold control The device resets the target efficiency point. The target efficiency point is lower than the initial target efficiency point. The transmission information is sent out again, and the calculation is restarted until the system reaches convergence. The adjustment of the target efficiency point here can be a fixed step. Adjustment, such as the initial 1, followed by 0.99, 0.98, 0.97. By analogy, of course, the adjustment of the target efficiency point can also be variable step size.

It should be noted that in order to avoid the frequent start and stop of the cold machine, the cold controller needs to consider whether it will cause the cold machine to be frequently turned on when calculating the new cold load (cold cold load output), for example: if at a certain time If the cold machine is turned on within (within half an hour), the calculated cold load of the cold machine will not be zero, that is, the cold machine that has been turned on in a short time will not make the decision that I want to close, and if it is certain When the time has been closed, the calculated cold load of the cold machine will not be greater than zero, that is, the cold machine that has been shut down in a short time will not make the decision that I want to open.

The information transfer direction of the cold machine can take many forms. In the above example, the information transfer direction of the cold machine is transmitted from the No. 2 cold machine to the No. 3 cold machine, and the No. 3 cold machine is passed to the No. 4 cold machine. The No. 4 chiller is passed to the No. 1 chiller. However, the direction of this information transmission is not fixed and unique. It can also be other information transmission directions, as shown in Figure 13, which can be transmitted by the No. 1 chiller. No. 2 cold machine, No. 2 cold machine passed to No. 3 cold machine, No. 3 cold machine passed to No. 4 cold machine, No. 4 cold machine passed to No. 3 cold machine, No. 3 cold machine passed to No. 2 cold machine, No. 3 cold machine passed to No. 2 cold machine, The No. 2 chiller is passed to the No. 1 chiller, etc., that is, the flow direction of this information is flexible.

When the system reaches the predetermined convergence condition, the optimized operating parameters of each chiller are determined, and the chiller controller calculates the start-stop state of the chiller and/or the chilled water flow demand and/or the chilled water effluent according to the determined operating parameters. The temperature is set and the cold controller changes the cold machine to the new operating parameters and sends the chilled water flow demand and the chilled water effluent temperature setpoint to the control system so that the corresponding chiller reaches the desired operating state.

The convergence of a single cold controller is a prerequisite for the convergence of the entire system. When a cold controller receives the incoming information from the neighbor, it calculates according to the received information, and the absolute value of the calculated cooling margin is calculated. The value is equal to or less than the set cooling margin threshold, and the efficiency of the current system (the update can be calculated in real time during the transfer) is not less efficient than before the adjustment (or the energy consumption of the system is not higher than before the initiation of the adjustment task), Then the chiller controller considers the calculation to be convergent and valid, so that the information is no longer passed outwards; the second is the convergence of the system: when a cold controller in the system receives the delivery information, the timer is turned on, if given During the time period, no cold controller receives the information transmitted by the neighbors, that is, there is no new iterative calculation stimulus, then the system converges, and the operation result currently saved by each cold controller is the final result, which is output as a control signal.

It is worth noting that the above iterative calculation process is to determine how the total cooling load is distributed among the chillers. After determining the cooling load distribution scheme, there are two ways to make the chiller reach the expected cold. load. The first method is to adjust the flow of chilled water flowing through the chiller. This is easy to implement for a chiller system (referred to as a machine-to-one pump system), as long as the chiller controller will The calculated demand for the chilled water flow value is sent to the controller of the corresponding refrigerating pump, and for a system other than the one-to-one pump, the cold water can be adjusted by adjusting the chilled water outlet temperature setting of the corresponding chiller. Undertake the adjustment of the cold load value.

In the above calculation process, the required cold machine performance curve or cold machine performance parameters, preset The cold regulation margin threshold can be built into the cold controller or pre-fed to the cold control system.

[Example 3]

According to an embodiment of the third aspect of the invention, the control method can be applied to a cooling tower system to complete group control of the cooling tower system. After being specifically applied to a cooling tower system, the present invention provides an optimized control method for distributing cooling water to various cooling towers in a system, the cooling tower system comprising a plurality of functional modules - each cooling tower is a functional module, all The total task of the cooling tower is that the cooling water flow that needs to be cooled needs to meet the total cooling water flow demand of the system. The flow of cooling water flowing through each cooling tower needs to be higher than the lower flow limit, and the number of cooling towers that are turned on is higher. The more the better, the task assignment includes the following steps: information exchange between the cooling towers to complete the system operation, when the system operation is completed, the cooling water flow value assumed by each cooling tower is obtained.

The information interaction includes the following steps:

All the cooling towers of the whole system form a link, and the information is transmitted from the beginning of the chain to the chain tail in turn. The information includes the task values of the cooling towers respectively operating at the lower limit of the flow rate, and the tail task calculates the total task that satisfies the requirements. Combine and pass the data back to the beginning of the chain, at which point each cooling tower knows if it should be running.

A more detailed description will be given below with reference to the accompanying drawings:

Figure 14 is a schematic view showing the structure of a cooling tower system according to an embodiment of the present invention. As shown in Figure 14, the cooling tower system includes n (generally n is a positive integer greater than 1) parallel cooling towers, each of which is equipped with a cooling tower controller.

When a cooling tower controller determines that a certain trigger condition is reached, an adjustment task is initiated by the cooling tower controller.

In a specific example of the present invention, the cooling tower controller corresponding to each cooling tower can initiate an adjustment task. When one of the cooling tower controllers initiates an adjustment task, the remaining cooling tower controllers cooperate with the cooling tower controller. Complete the calculation of the system.

Regarding the trigger condition, in the specific example of the present invention, the trigger condition may take various forms, such as the form of (1), (2) below.

(1) can be determined according to time, for example, one for each cooling tower controller The control cycle initiates an adjustment task by the cooling tower controller every certain time; for example, the control cycle of the cooling tower controller can be set to 5 minutes, and the corresponding cooling tower controller first initiates a request task each time the control cycle is reached. A summation calculation is initiated to the cold station system, for example, a summation calculation is initiated to the controller of the cooling pump to obtain the total flow of the current cooling water, and then the calculation is started.

(2) Cooling pump side condition trigger: When the operating condition of the cooling pump changes, such as the frequency changes or the number of units changes, these will cause the change of the cooling water flow. At this time, the cooling pump group calculates the current The cooling water flow is then sent to the cooling tower actively. After the cooling tower receives the information, it initiates the adjustment task and adjusts.

When there is a cooling tower controller in the system that initiates the conditioning task, the cooling tower controller in the system begins to interact with its adjacent cooling tower controller. The process of information interaction is a chain computing process. The following is a detailed description of the process of information exchange of the cooling tower.

The control of the cooling tower system is divided into two subsystems: cooling tower number control (ie, how many cooling towers are opened) and cooling tower fan control (ie, selecting whether the cooling tower fan is on and at what frequency), which are described below.

The main energy consuming component of the cooling tower system is the cooling tower fan, and its power is relatively small compared with the chiller and the water pump. Therefore, in order to ensure that the chiller works at a higher efficiency, the cooling capacity of the cooling tower system should be fully utilized. Minimizing the water temperature of the cooling water, the increase in energy consumption caused by the fan of the cooling tower system is insensitive to the entire cold station system, and thus the control idea of the number control of the cooling tower system can be determined: from the cold machine The high-temperature cooling water flowing out from the condenser side should be distributed as evenly as possible to each cooling tower, that is, if possible, open the cooling water channel of the cooling tower as much as possible (ie, open the valve on the cooling water pipe) to cool Water flows evenly through the various cooling towers to take full advantage of the heat exchange area of the cooling tower.

The control idea of the fan control of the cooling tower is: the fan of the cooling tower opened by the waterway is uniformly frequency-converted, and the total outlet water temperature of the control cooling tower system reaches the set value.

However, there is a control condition for the control of the cooling tower waterway, that is, the flow rate of the cooling water flowing through each cooling tower should not be too small (for example, not less than 40% of the rated flow rate of the cooling tower), otherwise the cooling water will be cooled in each station. The uneven distribution between the towers adversely affects the cooling of the cooling water, so it is necessary to set a lower limit for the cooling water flow rate of each cooling tower. Whenever the flow rate of each cooling tower is higher than the lower limit, open the water channel of the cooling tower as much as possible.

Therefore, the problem of the number of cooling towers can be converted into the problem of the distribution of the total flow of cooling water, that is, in the case of a given total flow of cooling water ∑G (this value can be the total flow of cooling water transmitted outside), how Select which cooling towers to turn on so that the flow rate of each open is not lower than the lower limit G ^* _i and the number of open is the most.

Combined with Figure 15, to solve this problem, the following algorithm can be used:

First, all the cooling tower controllers are formed into a link through the spanning tree process. The two ends of the chain are the starting node v ₁ and the terminating node v _m , respectively, or the chain head and the chain tail, and then calculate point by point along the chain. After the node is terminated, the final decision is made, and the result is returned point by point. The entire search process is completed once, without loop iteration. The specific algorithm is:

3) Calculate and transfer the following information at the beginning of the chain. The value ∑G (the total cooling water flow of the system) and the value when it is turned on ∑GG ^* ₁ , G ^* ₁ at the time of closing the chain are the working hours of the chain cooling tower. The lower limit of the cooling water flow, and perform the following operations point by point;

4) After each node receives the information (is an array containing k numbers), for each of the numbers, calculate the amount of cooling water remaining when it does not turn on and on and work at the lower limit of the cooling water flow, and then new An array (for an array containing 2k numbers) is sent to its neighbor nodes;

5) After the end node receives the data, it is internally calculated to exclude the number less than zero (less than zero indicates that the sum of the lower flow limits of each cooling tower under this open combination is greater than the total flow of the cooling water, so after each actual opening, each The actual flow divided by the cooling tower is less than the lower limit of the flow rate), and the closest value to zero is found, and the combination of the corresponding cooling tower is the final opening scheme. This combination is then passed back to all nodes.

6) Each node knows if it should start and stop.

This method adopts point-by-point calculation, and each node considers both participation and non-participation in combination to form a pyramid-shaped architecture, as shown in FIG. This means that the last node is passed, and after the last node completes the combined calculation, there are 2m combinations, including all cases where they do not participate. So this algorithm lists all the combinations in a limited number of steps and is complete.

It is worth noting that if the chain tail node has the same value of multiple schemes when calculating, it is selected according to the following principles: In order to avoid frequent start and stop switching, the group with the closest physical distance is preferred.

The following describes the control method of the cooling tower fan:

The fan control rules of the cooling tower are as follows: as long as the water valve of the cooling tower is turned on, that is, the cooling tower is put into use, the fan is turned on; the fan that is turned on is uniformly converted, and the water temperature of the cooling tower is controlled to reach the set value of the water temperature (setting The value is given by an additional control algorithm); if the speed of the inverter fan reaches the lower limit of the speed, a fan is turned off; if the speed of the fan that is turned on reaches the upper limit, and the fan of the cooling tower that is still open is not turned on, Open a fan.

When the system determines the optimized operating parameters of each cooling tower, the cooling tower controller controls the corresponding cooling tower to reach the corresponding operating state according to the optimized operating parameters.

In the above calculation process, the required cooling tower flow lower limit value can be built into the cooling tower controller.

According to the cooling tower system control method of the embodiment of the present invention, the centerless network formed by the cooling tower controllers of each parallel cooling tower with communication function is used, so that each parallel cooling tower determines the start and stop of each cooling tower waterway through independent negotiation. The start and stop and speed of the fan, when the cooling tower's heat dissipation area is fully utilized, the flow rate of each cooling tower is not lower than the lower limit of the flow rate.

The above merely exemplarily gives the specific steps of applying the control method provided by the present invention to the water pump system, the chiller system and the cooling tower system, but this does not mean that the control method of the present invention can only be applied to these several In a specific case, any system that has the same task allocation requirement can adopt the control method provided by the present invention to achieve a more optimized task allocation scheme, so that the total resource consumption of the system is the lowest or lower, or meet certain restrictions of the functional module. Requirements, and gain the advantages of high control efficiency and flexibility.

The control method of the pump system based on the centerless network according to the embodiment of the present invention will be described in more detail below with reference to the accompanying drawings.

Example 4:

As shown in FIG. 17, a method for controlling a water pump system based on a centerless network according to an embodiment of the present invention includes the following steps:

S101: One controller is separately provided for each pump in the water pump system, and all controllers are interconnected to form a centerless network.

Among them, the centerless network means that the status of each node in the network is equal, the whole network is flat, and there is no concept of center or head. The network can be chained, ring-shaped, or grid. The nodes in the network are interconnected by a certain topological relationship, and the nodes that are interconnected in the topological relationship transmit information, and the nodes that are not associated in the topological relationship do not exchange information. For the water pump system of the embodiment of the invention, the status of each water pump in the centerless network is equal, and the parallel water pumps complete the control objectives by correspondingly controlling their equal autonomous coordination, and the optimized control scheme is calculated distributedly. In this way, the workload of manual configuration debugging in the traditional control mode can be reduced, the plug-and-play of the control device can be realized, and the control efficiency, robustness and scalability of the system can be improved. For example, if you add a new pump, you only need to connect it with other pumps, then set up a controller with the same controller for the new pump and add the controller to the above-mentioned centerless network.

As shown in FIG. 1, the pump system includes n (generally n is a positive integer greater than 1) parallel pumps 100, each of which is equipped with a controller 200. It should be noted that in practical applications, a plurality of pumps connected in series in the pump system may be encountered. In this case, only a plurality of series water pumps need to be equivalent to a single water pump according to fluid mechanics knowledge, thereby A controller 200 can be configured for the equivalent pump.

As shown in FIG. 1, the implementation of the centerless network formed by the plurality of controllers may be either or both of a wired network and a wireless network. That is to say, the plurality of controllers 200 can be connected through a wired network, a wireless network, or an integrated network combining wired and wireless forms to form a centerless network. It should be noted that the centerless network in the embodiment of the present invention can communicate by using multiple communication protocols, as long as the real-time and validity of the communication can be ensured, that is, the centerless network in the embodiment of the present invention is not limited. In any specific communication control protocol, as long as the network communication and control that meet the requirements can be performed, the node control network or network herein can be understood and defined in the broader technical field.

Referring again to FIG. 1, each controller 200 can also be coupled to a corresponding differential pressure sensor 400 to obtain a transmitted differential pressure signal from the corresponding differential pressure sensor 400, the controller The 200 can also be coupled to the frequency converter 300 of the water pump to effect a change in the operating parameters of the water pump 100 by controlling the corresponding frequency converter 300. That is, the controller 200 can change the operating parameters of the corresponding water pump by controlling the frequency converter connected to the water pump.

S102: When a certain water pump controller determines that a certain trigger condition is reached, the water pump controller initiates an adjustment task.

In a specific example of the present invention, the controller corresponding to each running water pump can initiate an adjustment task. When one of the water pump controllers initiates an adjustment task, the remaining water pump controllers cooperate with the controller to complete the system operation. .

In addition, since a plurality of controllers in the embodiment of the present invention construct a centerless network, the control procedures for each controller are the same, and therefore, it is possible to control multiple pumps in the same or similar time. In this case, the system needs to set a corresponding processing mechanism. In one embodiment of the present invention, the processing may be performed in the following two manners.

First, calculate the arbitration mechanism of post-processing. Specifically, first, calculate each control task separately. Several non-central controllers are connected to form a non-central computing network, which is equivalent to an operating system. Tasks are processed in parallel, so when multiple controllers initiate adjustment tasks at the same time, the system can compute simultaneously and get multiple results. Then through the arbitration mechanism, select the result of which adjustment task should be performed.

Second, the arbitration mechanism that is processed first, specifically, first determines who will initiate the adjustment task through the arbitration mechanism, and then initiates the adjustment task by it, that is, when multiple controllers are simultaneously initiated to perform the adjustment task, first through arbitration The mechanism selects an initiator and then initiates an adjustment task.

Since the arbitration mechanism calculated after the first processing first determines which controller initiates the adjustment task, the calculation amount is small relative to the first arbitration mechanism that calculates the post-processing first. Therefore, in most cases, the second type can be used. The arbitration mechanism calculated after processing is first processed to reduce the amount of calculation.

In an embodiment of the present invention, the form of the arbitration mechanism is also diverse, including but not limited to the following methods: prioritizing the priority by hand, and determining which operation result is adopted according to the set priority in the running process. , lottery, grab tokens, random assignments, and more.

Among them, the arbitration method is to protect the water pump from starting and stopping frequently, no matter which pump is sent The adjustment tasks obtained may be the best results, but there are usually multiple pumps of the same type in the system, such as T1, T2, T3, and three pumps of the same model. The calculated results are Turn on [T1, T2], [T1, T3], [T2, T3], and the two pumps are currently running T1 and T2. In order to avoid frequent start and stop of the pump and frequent switching, the arbitration result should be selected to open [ T1, T2] corresponding scheme.

Regarding the trigger condition, in the specific example of the present invention, the trigger condition may take various forms, such as the forms of (1), (2), and (3) below.

(1) The absolute value of the deviation between the measured value of the differential pressure of the pump and the set value of the differential pressure collected by the controller exceeds the preset threshold of differential pressure deviation, that is, the working state of the pump cannot meet the pressure difference of the end. The demand, at this time, needs to adjust the working point of the pump to meet the pressure difference of the end water equipment.

As shown in Figure 2, the pressure difference measured by the pump differential pressure measurement can be either the pressure difference ΔH4 between the inlet and outlet of the pump, or the pressure difference ΔH1 at the most unfavorable loop in the end network, or the end. The pressure difference ΔH2 of a branch in the network, or the pressure difference ΔH3 between the manifolds, it can be understood that the specific pressure difference at which position can be flexibly set according to different control requirements.

(2) It can be determined according to time, for example, a control cycle is set for each water pump controller, and an adjustment task is initiated by the water pump controller every certain time;

(3) It can be triggered according to the energy consumption of the system. For example, during the automatic operation of the system, it is subject to external intervention. If the manual adjustment is made manually, it may cause the demand for the pressure difference and the control period has not yet arrived but the operation status It is not the case that the energy consumption is optimal, so it is necessary to trigger the adjustment from the perspective of optimizing the energy consumption. Understandably, there are many ways to judge how to trigger based on energy consumption.

S103: When there is a water pump controller in the system that initiates the adjustment task, the water pump controller in the system starts to exchange information with its adjacent water pump controller.

The process of information interaction is an iterative calculation process, which is the same as the iterative calculation process of the information interaction previously described in connection with FIG. 3 to FIG. 7, and will not be described again here.

S104: When the system reaches a predetermined convergence condition, determine an optimized operating parameter of each water pump, and the controller calculates a start-stop state and/or a rotation speed of the water pump according to the determined operating parameter, and the controller changes the water pump to a new one. Operating status.

In step S103, the convergence of a single controller is mentioned, which is a precondition for the convergence of the entire system. When a controller receives the incoming information transmitted by the neighbor, it calculates according to the received information, and calculates the calculated traffic adjustment. The absolute value of the quantity is less than the set flow adjustment margin threshold, and the efficiency of the current system (the update can be calculated in real time during the transfer) is not less efficient than before the adjustment (or the energy consumption of the system is not higher than before the initiation of the adjustment task), Then the controller considers the calculation to be convergent and valid, so that the information is no longer passed outwards; secondly, the convergence of the system: when a controller in the system receives the delivery information, the timer is turned on, if there is no given time period The controller then receives the information transmitted by the neighbors, that is, if there is no new iterative calculation stimulus, the system converges, and the operation result currently saved by each controller is the final result, which is output as a control signal.

S105: If there is no water pump controller that initiates the adjustment task in the system, the water pump in the system keeps the original operating parameters unchanged.

In the above calculation process, the required pump performance curve or pump performance parameter, differential pressure set value, preset differential pressure deviation threshold and preset flow adjustment margin threshold can be built into the pump controller.

It should be noted that since the pump performance curve or the pump performance parameter is usually the trade secret of the pump manufacturer, it is generally unwilling to be disclosed. In an embodiment of the invention, the pump system manager may first send the controller to the pump manufacturer to have it enter the pump performance parameters in confidence and then bring it back for installation, or the pump system manager invites the pump manufacturer to come to the site. The pump performance parameters are entered confidentially to the controller. This solves the problem of confidentiality. In some cases, the performance of the device changes with the operation of the device, and a certain degree of attenuation occurs. Therefore, the device performance parameters in the controller can be modified. The modification method can be manual calibration of the device performance and manual calibration. Modify; you can also add a self-learning algorithm inside the controller to automatically detect device performance and adjust device performance parameters during device operation.

In addition, the differential pressure setting value can be set by the pump system manager according to the actual installation location and application of the pump. For example, the pressure difference setting of the water pump corresponding to the bathroom faucet on the upper floor should be relatively large; the pressure difference setting of the water pump corresponding to the drip faucet in the green area of the lower floor is relatively small.

Pump system control method based on centerless network according to an embodiment of the present invention, utilizing The non-central network of each parallel pump with communication function controller makes each parallel pump determine the start and stop and speed of each pump through independent negotiation, and makes the parallel pump overall energy efficiency optimal. The method for controlling a water pump system based on a centerless network according to an embodiment of the present invention has at least the following advantages:

1. The pump equipment manufacturer builds the pump performance parameters into the controller to solve the confidentiality problem, thus solving the problem of the communication between the self-control manufacturer and the equipment manufacturer in the traditional centralized control mode, and also the optimal control based on the equipment parameters. It is possible to increase the control efficiency of the pump.

2. In the field, only the controllers corresponding to the adjacent water pumps in the topological field need to be communicatively connected, and the parallel water pump can complete the control target efficiently through independent cooperation, avoiding the cumbersome manual intervention of the self-control manufacturers in the traditional centralized control mode. The configuration and debugging links can also enable the plug-and-play of the device, which enhances the flexibility and scalability of the system.

3. Theoretically ensure that for a given control target, the result of the algorithm convergence is that the overall performance of the pump is optimal, that is, the number of pumps opened and the running speed of the pump are optimal, which can improve the operating efficiency of the pump.

It should be noted that in practical applications, it may happen that only one water pump is installed, that is, the water pump controller shown in FIG. 1 detects that there is no neighbor. In this case, since there is only one water pump, it is inevitably opened during operation, and there is no problem of mutual cooperation between the water pumps. At this time, according to the performance parameters of the water pump and the actual working point of the current water pump, the resistance coefficient of the current pipe network system can be calculated, and the speed of the water pump required to reach the control target can be directly determined.

Example 5:

The specific manner of information exchange between the pumps (ie, step S103 above) may also employ another algorithm, such as an optimized allocation method. Specifically, the water pump system in the embodiment of the present invention has a total constraint that the total flow of the water pump should satisfy the total demand X _{0 of the} end flow, so the problem is transformed into how the flow is reasonably satisfied when the total constraint is satisfied. Assigned to each pump so that the total power consumption of the pump system is the lowest. When determining the optimal allocation scheme, the opening combination of the water pump can be determined first, and then the specific flow rate of the opened water pump can be determined. After mathematical derivation, when the pump combination is determined, the problem of how the total flow is optimally distributed among the pumps becomes very simple, that is, according to the principle that the pump energy consumption function is equal to the flow derivative.

In addition, there are several ways to determine the opening combination of the pump.

In the first mode, all the water pump controllers are formed into a link through the spanning tree process. The two ends of the chain are the starting node v ₁ and the terminating node v _m , respectively, or the chain head and the chain tail, and then along the chain. Point-by-point calculation, the final decision is made after terminating the node calculation, and the result is returned point by point. The entire search process is completed once, without loop iteration. The specific algorithm is:

4) After each node receives the information (is an array containing k numbers), for each of these numbers, calculate the value of its own join and not added, and then send the new array (for an array containing 2k numbers) Give it a neighbor node;

6) Each node knows whether it should start and stop, and then the node that starts will calculate the optimal traffic distribution.

Further, when the pumping combination is determined, the problem of how the total flow is optimally distributed among the various devices becomes simple, that is, the pump energy consumption function is distributed on the principle that the derivative of the flow is equal. Therefore, the equal derivative is transmitted between the respective water pump controllers. In a certain iteration, each opened water pump calculates its own corresponding flow rate according to the received value (calculated according to the energy consumption-flow relationship curve) And the curve can be obtained by the performance parameter or performance curve of the water pump), and then the flow of each open pump is globally summed. If it is greater than the total demand of the flow demand X ₀ , the derivative value is reduced; if less than the total flow demand X ₀ The requirement is to increase the derivative value. Then, each pump recalculates the respective flow rate according to the received new derivative value, and iterates (the above steps still belong to the range of step S103). Since the relationship between the derivative and the energy consumption value is monotonic, the iterative adjustment process converges quickly (i.e., the system described in step S104 reaches a predetermined convergence condition and determines the operational parameters optimized for each pump).

Example 6

The first algorithm for determining the opening combination of the water pump (ie, a part of step S103) is introduced in Embodiment 5, and the second algorithm for determining the opening combination of the water pump is introduced in this embodiment, that is, mode 1 -2.

(3) After a certain number of iterations, the opening probability of each node will tend to be stable. The node with the opening probability converging to 1 or greater than a certain threshold will be enabled; the opening probability will converge to 0 or less than a certain preset. The threshold node will be closed.

In mode 1-2, in fact, each node is initiating a global weighted summation computing task, wherein the rules for adjusting the probability of self-opening and the manner and magnitude of each probability adjustment can be adjusted.

After determining the opening combination of the water pump by the above algorithm, the algorithm as described in Embodiment 5 can be employed to determine the specific flow rate of the opened water pump.

In addition, the optimized operating parameters of the pump calculated by the algorithms described in Embodiment 5 and Embodiment 6 are theoretically optimal solutions, and thus it is no longer necessary to judge whether the total energy consumption of the system is lower than before the initiation of the adjustment task, but In the process of determining the opening combination of the water pump, it is also necessary to go through the arbitration process in order to protect the water pump from starting and stopping frequently. For example, when determining the opening combination of the water pump, the water pump that has been turned on/off in a short time is not set to be turned off/on. status.

The pump system control method based on the centerless network according to the embodiment of the invention enables the parallel water pumps to independently coordinate and complete the control objectives, calculate the optimized control schemes in a distributed manner, and reduce the workload of the manual configuration debugging in the traditional control mode. The plug-and-play control device is realized, which improves the control efficiency, robustness and scalability of the system.

Further, in conjunction with FIG. 1, an embodiment of the present invention discloses a water pump system based on a centerless network, including: a plurality of water pumps 100 and a plurality of controllers 200.

Wherein, the plurality of water pumps 100 are arranged in parallel; the plurality of controllers 200 are connected to the plurality of water pumps 100 in a one-to-one correspondence, and the water pump system is operated by the control method as described in the above embodiments. I won't go into details here.

Further, the present invention also provides a water pump controller in which all water pump controllers are interconnected to form a centerless network. When the water pump controller determines that a certain trigger condition is reached, the water pump controller is used to initiate an adjustment task in the system. When there is a water pump controller that initiates the adjustment task, the water pump controller starts to exchange information with its adjacent water pump controller, and after a number of information interactions, reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each water pump. The pump controller controls the corresponding pump to reach a corresponding operating state according to the optimized operating parameter, and keeps the corresponding pump operating parameters unchanged when there is no pump controller that initiates the regulating task in the system.

It should be noted that the specific implementation manner and method and/or system part of the water pump controller of the embodiment of the present invention are similar to the specific implementation manner of the water pump controller. For details, refer to the description of the method and/or system part, in order to reduce redundancy. Do not repeat them.

The method for controlling a cold system based on a centerless network according to an embodiment of the present invention will be described in more detail below with reference to the accompanying drawings.

Example 7

As shown in FIG. 18, a method for controlling a chiller system based on a centerless network according to an embodiment of the present invention includes the following steps:

S201: respectively set a cold controller for each cold machine in the cold system, and interconnect all the cold controllers to form a centerless network.

Among them, the centerless network means that the status of each node in the network is equal, the whole network is flat, and there is no concept of center or head. The network can be chained, ring-shaped, or grid. The nodes in the network are interconnected by a certain topological relationship, and the nodes that are interconnected in the topological relationship transmit information, and the nodes that are not associated in the topological relationship do not exchange information. For the chiller system of the embodiment of the present invention, the status of each chiller in the centerless network is equal, and each chiller independently and independently coordinates the control target through the corresponding chiller controller, and calculates the distributed calculations in a distributed manner. The control scheme is optimized, so that the workload of manual configuration debugging in the traditional control mode can be reduced, the plug-and-play of the control device can be realized, and the control efficiency, robustness and scalability of the system can be improved. For example, if you add a cold machine, you only need to set up a cold controller with the same cold controller as the other cold controllers, and add the cold controller to the above-mentioned centerless network.

As shown in Figure 9, the chiller system includes n (generally n is a positive integer greater than 1) chillers, each chiller is equipped with a chiller controller.

As shown in FIG. 9, the implementation of the centerless network formed by the plurality of cold controllers may be either or both of a wired network and a wireless network. That is to say, multiple cold controllers can be connected through a wired network, a wireless network, or an integrated network combining wired and wireless forms to form a centerless network. It should be noted that the centerless network in the embodiment of the present invention can communicate by using multiple communication protocols, as long as the real-time and validity of the communication can be ensured, that is, the centerless network in the embodiment of the present invention is not limited. In any specific communication control protocol, as long as the network communication and control that meet the requirements can be performed, the node control network or network herein can be understood and defined in the broader technical field.

S202: When a certain cold controller determines that a certain trigger condition is reached, the cooling controller initiates an adjustment task.

In a specific example of the present invention, the chiller controller corresponding to each running chiller can initiate an adjustment task, and when one of the chiller controllers initiates the adjustment task, the remaining chiller controllers cooperate with the control. The device completes the operation of the system.

In addition, since a plurality of chiller controllers in the embodiment of the present invention construct a centerless network, the control procedures of each chiller controller are the same, and therefore, it is possible to have more or less in the same or similar time. In this case, the system needs to set the corresponding processing mechanism. In one embodiment of the present invention, the processing can be performed in the following two manners.

First, calculate the post-processing arbitration mechanism. Specifically, first, calculate each adjustment task separately. Several non-central cold controllers are connected to form a non-central computing network, which is equivalent to an operating system. Multi-task parallel processing is supported, so when multiple cold controllers initiate adjustment tasks at the same time, the system can simultaneously compute and obtain multiple results. Then through the arbitration mechanism, select the result of which adjustment task should be performed.

Second, the arbitration mechanism that is processed first, specifically, first determines who will initiate the adjustment task through the arbitration mechanism, and then initiates the adjustment task, that is, when multiple cold controllers are encountered simultaneously to initiate the adjustment task, first An initiator is selected through an arbitration mechanism, and then an adjustment task is initiated by it.

Since the arbitration mechanism calculated after the first processing first determines which cold controller initiates the adjustment task, the calculation amount is small compared to the first arbitration mechanism that calculates the post-processing first. Therefore, in most cases, the first Two kinds of arbitration mechanisms that are processed first and then calculated to reduce the amount of calculation.

Among them, the arbitration method is to protect the cold machine from frequent start and stop. Regardless of which cooling machine initiates the adjustment task, the results obtained may be the best results, but there are usually multiple identical models of cold machines in the system, such as T1, T2, T3, three sets of the same type of cold machine, the calculated sets of results are turned on [T1, T2], [T1, T3], [T2, T3], while the current running is T1 and T2 In order to avoid frequent start and stop and frequent switching of the cold machine, the arbitration results should be selected to open the corresponding scheme of [T1, T2].

(1) can be determined according to time, for example, a control cycle is set for each chiller controller, and an adjustment task is initiated by the chiller controller every certain time;

(2) It can be triggered according to the energy consumption of the system. For example, the system is subject to external intervention during the automatic operation. If the manual adjustment is made manually, the control period may not be reached, but the operating condition is not the optimal energy consumption. Therefore, it is necessary to trigger the adjustment from the perspective of optimizing the energy consumption. Understandably, there are many ways to judge how to trigger based on energy consumption.

S203: When there is a cold controller in the system that initiates the adjustment task, the cold controller in the system starts to exchange information with its adjacent cold controller.

The process of information interaction is a process of iterative calculation, which is the same as the iterative calculation process of the information interaction previously described in connection with FIG. 10 to FIG. 13 , and details are not described herein again.

S204: When the system reaches a predetermined convergence condition, determine an optimized operating parameter of each chiller, and the chiller controller calculates a start/stop state of the chiller and/or a chilled water flow demand and/or freeze according to the determined operating parameter. The water outlet temperature setting value is changed by the cold controller to the new operating parameter, and the chilled water flow demand and the chilled water outlet temperature set value are sent to the control system so that the corresponding cold machine reaches the required operating state. .

In step S203, the convergence of a single cold controller is mentioned, which is a precondition for the convergence of the whole system. When a cold controller receives the incoming information transmitted by the neighbor, it calculates according to the received information, and calculates The absolute value of the cooling capacity adjustment margin is equal to or less than the set cooling capacity adjustment margin threshold, and the efficiency of the current system (which can be updated in real time during the transfer process) is not lower than the efficiency before the adjustment (or the energy consumption of the system) Not higher than before the initiation of the adjustment task, the chiller controller considers the calculation to be convergent and valid, so that no more information is passed out; the second is the convergence of the system: when a cold controller in the system receives the delivery information, it is turned on. The timer, if no cold controller receives the information transmitted by the neighbor within a given time period, that is, there is no new iterative calculation stimulus, the system converges, and the operation result currently saved by each cold controller is the final As a result, it is output as a control signal.

S205: If there is no cold controller in the system to initiate the adjustment task, the cold machine in the system keeps the original operating parameters unchanged.

In the above calculation process, the required cold performance curve or cold performance parameter, preset cooling adjustment margin threshold, etc. can be built into the cold controller.

It should be noted that since the cold performance curve or the cold performance parameter is usually the trade secret of the cold machine manufacturer, it is generally unwilling to be disclosed. In an embodiment of the invention, the chiller system manager may first send the chiller controller to the chiller manufacturer to enter the chiller performance parameters in confidence and then bring it back for installation, or the chiller system manager invites After the cold machine manufacturer comes to the site, the cold machine performance parameters are entered confidentially to the cold controller. This solves the problem of confidentiality. In some cases, the performance of the device changes with the operation of the device, and a certain degree of attenuation occurs. Therefore, the device performance parameters in the cold controller can be modified. The modification method may be manual calibration of the performance of the device and manual modification; or a self-learning algorithm may be added inside the cold controller to automatically detect the performance of the device and adjust the performance parameters of the device during the operation of the device.

According to the method for controlling a cold system based on a centerless network according to an embodiment of the present invention, a non-central network formed by a cold controller with a communication function of each cold machine is used, so that each cold machine determines the operation of each cold machine through independent negotiation. The parameters also make the overall efficiency of the cold machine optimal. The method for controlling a cold system based on a centerless network according to an embodiment of the present invention has at least the following advantages:

1. The cold machine equipment manufacturer built the cold machine performance parameters into the cold machine controller to solve the confidentiality problem, thus solving the problem of communication between the self-control manufacturer and the equipment manufacturer in the traditional centralized control mode, and also making the equipment based on Optimized control of the parameters makes it possible to increase the control efficiency of the chiller.

2. In the field, only the cold controller corresponding to the adjacent cold machine in the topology needs to be connected by communication, and the parallel cold machine can complete the control target efficiently through independent cooperation, avoiding the intervention of the automatic control manufacturer in the traditional centralized control mode. The cumbersome manual configuration and debugging links can also enable the plug-and-play of the device, which enhances the flexibility and scalability of the system.

3. Theoretically ensure that for a given control target, the result of the algorithm convergence is that the overall performance of the cold machine is optimal, that is, the number and combination of the cold machine is optimal, which can improve the operating efficiency of the cold machine.

It should be noted that in practical applications, it may happen that only one cold machine is installed, that is, the cold controller shown in FIG. 9 detects that there is no neighbor. In this case, since there is only one cold machine, it is inevitably turned on during work, and there is no problem of mutual cooperation between the cold machines. At this time, according to the performance parameters of the cold machine and the actual working point of the current cold machine, the chilled water flow required to reach the control target can be directly obtained.

Example 8

The specific manner of information exchange between the cold machines, that is, the above step S203, may also adopt another algorithm, such as an optimized allocation method. Specifically, the chiller system in the embodiment of the present invention has a sum constraint, that is, the total cooling load of the chiller should satisfy the total demand X _{0 of the} end to the cold load, so the problem is transformed into how the total constraint is satisfied. These cold loads are reasonably distributed to each chiller so that the total power consumption of the chiller system is minimal. When determining the optimal allocation scheme, the opening combination of the cold machine can be determined first, and then the specific cooling load value of the opened cold machine can be determined. After mathematical derivation, when the combination of the cold machine is determined, the problem of how the total cooling load is optimally distributed among the various cold machines becomes very simple, that is, the distribution of the energy consumption function is equal to the derivative of the cooling load. .

In addition, there are a number of ways to determine the open combination of the chiller.

In mode 2-1, all the cold controllers are first formed into a link through a spanning tree process. The two ends of the chain are a starting node v ₁ and a terminating node v _m , respectively, or a chain head and a chain tail, and then along the chain. The chain is calculated point by point, the final decision is made after terminating the node calculation, and the result is returned point by point. The entire search process is completed once, without loop iteration. The specific algorithm is:

3) Calculate and pass the following information at the beginning of the chain, the value X ₀ when it is closed and the value X ₀ -x ₁ when it is turned on, x ₁ is the cooling load when the chain head cooler works at the best efficiency point. And perform the following operations point by point;

4) After each node receives the information (is an array containing k numbers), for each of these numbers, calculate the remaining cold load value that is not added when it works at the optimal efficiency point, and then the new array (for An array containing 2k numbers) sent to its neighbor nodes;

6) Each node knows whether it should start or stop, and then the opened node begins to calculate the optimal cold load distribution.

The node at the end selects the two combinations closest to X ₀ to actually select the two combinations of the coldest values passed by the previous node, one of which is greater than zero and one less than zero. The open combination of the cold machine corresponding to the value is the alternative for further calculation of the optimal cold distribution. After obtaining the alternative, the system will calculate the optimal cold partition for each alternative and then pass the arbitration. Choose one of these options.

Further, when the end node selects the two groups closest to the distance X ₀ , if the selected combination will cause the cold machine to frequently start and stop, the combination will not be considered for the safe operation of the device. Select, the end node will retreat to the next, choose not to make the device appear frequently start and stop combination, even if this combination is not the closest to X ₀ . The following describes how to distribute the cooling load in the cold unit that is turned on.

Further, when the combination of the cold machine is determined, the problem of how the total cooling load is optimally distributed among the various devices becomes simple, that is, the respective cooling machine energy consumption functions are distributed on the principle that the derivatives of the cooling load are equal. Therefore, the equal derivative is transmitted between the various cold controllers. In a certain iteration, each opened cold machine calculates its own corresponding cold load according to the received value (according to the energy consumption-cooling load relationship) The curve is calculated, and the curve can be obtained by the performance parameter or performance curve of the cold machine, and then the global cooling load of each cold-starting machine is summed. If it is greater than the total demand of the cold load X ₀ , the derivative value is reduced. If the requirement is less than the total demand X ₀ of the cold load, increase the derivative value. Then, each chiller recalculates the respective cold load according to the received new derivative value, and iterates (the above steps still belong to the range of step S203). Since the relationship between the derivative and the energy consumption value is monotonic, the iterative adjustment process converges quickly (i.e., the system described in step S204 reaches a predetermined convergence condition and determines the operational parameters optimized for each pump).

Example 9

The first algorithm (ie, mode 2-1) for determining the turn-on combination of the cold machine (part of step S203) is described in Embodiment 8, and the second algorithm for determining the open combination of the cold machine is introduced in this embodiment. 2-2.

The algorithm is that each chiller controller has an initial turn-on probability, such as 0.5. After a chiller controller initiates a compute task, each chiller controller (or node v _i ) performs the following rules:

(1) The node v _i initiates a global weighted summation task, that is, the sum of the results of each node's own opening probability multiplied by its own x _i ^* (the cold load of the cold machine at the optimal efficiency point);

In mode 2-2, in fact, each node is initiating a global weighted summation computing task, wherein the rules for adjusting the probability of self-opening and the manner and magnitude of each probability adjustment can be adjusted.

It should be noted that in order to ensure the safe operation of the equipment, the state of frequent start and stop is a criterion with high priority. Therefore, the cold machine performs the above operation process without frequent start and stop. In other words, each unit The cold machine knows whether it has been turned on or off in a short period of time, so it will not make decisions to make it start and stop frequently during the operation. For example, a cold-closed machine is involved in the calculation of the new adjustment task. It may be possible to put yourself directly off, without the situation that your own probability of opening is close to 1.

The algorithm as described in Example 8 can be used to determine the value of the specific cold load of the turned-on cold after determining the open combination of the cold by the above algorithm.

In addition, the operating parameters of the optimized cold machine calculated by the algorithms introduced in Embodiment 8 and Embodiment 9 are theoretically optimal solutions, and thus it is no longer necessary to judge whether the total energy consumption of the system is lower than before the initiation of the adjustment task. However, the arbitration process is also required to determine the opening combination of the cold machine, so as to protect the cold machine from frequent start and stop. For example, when determining the open combination of the cold machine, the cold machine that has been turned on/off in a short time will not be set. It is set to the off/on state.

According to the embodiment of the present invention, the cold system control method based on the centerless network enables the cold machines to independently coordinate and complete the control objectives, calculate the optimized control schemes in a distributed manner, and reduce the workload of manual configuration debugging in the traditional control mode. The plug-and-play control device is realized, which improves the control efficiency, robustness and scalability of the system.

Further, in conjunction with FIG. 9, a further embodiment of the present invention further discloses a cold system based on a centerless network, comprising: a plurality of cold machines and a plurality of cold machine controllers, and a plurality of cold machine controllers. Correspondingly connected to a plurality of cold machines, wherein all the cold controllers are mutually To form a centerless network, when the cold controller determines that a certain trigger condition is reached, the cold controller is used to initiate the adjustment task. When there is a cold controller that initiates the adjustment task in the system, the cold machine control in the system The device begins to exchange information with its adjacent chiller controller, and after several information interactions, the entire system reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each chiller, and the chiller controller is operated according to the optimization. The parameter control corresponding chiller reaches the corresponding running state, and when there is no chiller controller that initiates the regulating task in the system, the corresponding chiller operating parameters are kept unchanged. The operation mode of the chiller system is performed according to the foregoing method, and will not be described herein.

Further, the present invention also provides a cold machine controller, all of which are interconnected to form a centerless network. When the cold machine controller determines that a certain trigger condition is reached, the cold machine controller is used to initiate an adjustment task. When there is a cold controller in the system that initiates the adjustment task, the cold controller starts to exchange information with its neighboring cold controller, and after several information interactions, reaches a predetermined convergence condition, thereby determining each cold machine optimization. After the running parameters, the chiller controller controls the corresponding chiller to reach the corresponding running state according to the optimized operating parameters, and keeps the corresponding chiller operating parameters unchanged when there is no chiller controller that initiates the regulating task in the system.

It should be noted that the specific implementation manner and method and/or system part of the cold controller of the embodiment of the present invention are similar to the specific implementation manner of the cold controller. For details, refer to the description of the method and/or system part, in order to reduce Redundant, do not repeat them.

The method for controlling a cooling tower system based on a centerless network according to an embodiment of the present invention will be described in more detail below with reference to the accompanying drawings.

As shown in FIG. 20, a cooling tower system control method based on a centerless network according to an embodiment of the present invention includes the following steps:

S301: One cooling tower controller is separately provided for each cooling tower in the cooling tower system, and all cooling tower controllers are interconnected to form a centerless network.

Among them, the centerless network means that the status of each node in the network is equal, the whole network is flat, and there is no concept of center or head. The network can be chained, ring-shaped, or grid. Shaped or star-shaped, that is, all nodes in the network are interconnected by a certain topological relationship, and information is transmitted between nodes interconnected in a topological relationship. No information is exchanged between nodes that are not associated in the relationship. For the cooling tower system of the embodiment of the present invention, the status of each cooling tower in the centerless network is equal, and each parallel cooling tower is independently and independently coordinated to complete the control target through the corresponding cooling tower controller, and distributedly calculated. The optimized control scheme can reduce the workload of manual configuration and debugging in the traditional control mode, realize the plug-and-play of the control device, and improve the control efficiency, robustness and scalability of the system. For example, if you add a cooling tower, you only need to connect it to other cooling towers, then set up a new cooling tower controller with the same cooling tower controller as the other cooling tower controllers, and add the cooling tower controller to the above. In a central network.

As shown in Figure 14, the cooling tower system includes n (generally n is a positive integer greater than 1) parallel cooling towers, each of which is equipped with a cooling tower controller.

As shown in FIG. 14, the implementation of the centerless network formed by the plurality of cooling tower controllers may be either or both of a wired network and a wireless network. That is to say, a plurality of cooling tower controllers can be connected through a wired network, a wireless network, or an integrated network combining wired and wireless forms to form a centerless network. It should be noted that the centerless network in the embodiment of the present invention can communicate by using multiple communication protocols, as long as the real-time and validity of the communication can be ensured, that is, the centerless network in the embodiment of the present invention is not limited. In any specific communication control protocol, as long as the network communication and control that meet the requirements can be performed, the node control network or network herein can be understood and defined in the broader technical field.

S302: When a cooling tower controller determines that a certain trigger condition is reached, an adjustment task is initiated by the cooling tower controller.

In a specific example of the present invention, the cooling tower controller corresponding to each cooling tower can initiate an adjustment task. When one of the cooling tower controllers initiates an adjustment task, the remaining cooling tower controllers cooperate with the cooling tower controller. Complete the calculation of the system. The cooling tower system follows the same arbitration mechanism as the chiller system and the pump system.

(1) It can be determined according to time, for example, one control cycle is set for each cooling tower controller, and an adjustment task is initiated by the cooling tower controller every certain time; for example, the control cycle of the cooling tower controller can be Set to 5 minutes, each time you reach control week During the period, the corresponding cooling tower controller first initiates a request task, and initiates a summation calculation to the cold station system, for example, initiates a summation calculation to the controller of the cooling pump, obtains the total flow of the current cooling water, and then starts the calculation.

S303: When there is a cooling tower controller in the system that initiates the adjustment task, the cooling tower controller in the system begins to exchange information with its adjacent cooling tower controller.

The process of information interaction is a chain computing process. The following is a detailed description of the process of information exchange of the cooling tower.

However, there is a control condition for the control of the cooling tower waterway, that is, the flow rate of the cooling water flowing through each cooling tower should not be too small (for example, not less than 40% of the rated flow rate of the cooling tower), otherwise the cooling water will be cooled in each station. The uneven distribution between the towers adversely affects the cooling of the cooling water. Therefore, it is necessary to set a lower limit for the cooling water flow rate of each cooling tower, and open the cooling tower as much as possible when the flow rate of each cooling tower is higher than the lower limit. waterway.

Example 10:

6) Each node knows if it should start and stop.

It is worth noting that if the chain tail node has the same value when there are multiple schemes in the calculation, then the root According to the following principles: In order to avoid frequent start and stop switching, the group with the closest physical distance is preferred.

Example 11

This embodiment describes a calculation method of another aspect of distributing the flow rate of the cooling water.

First, the following definition is made: the efficiency ε of the cooling tower is defined as the ratio of the actual cooling water flow rate G of the cooling tower to the minimum cooling water flow rate Gmin set by the cooling tower, ie ε=G/Gmin, and the flow adjustment margin ΔG is the current The difference between the total flow taken by the cooling tower and the total flow demand of the system cooling water.

Assume that there are four parallel cooling towers in a cold station system, namely CT1-CT4. It is assumed that the rated water flow of these cooling towers is 50L/s, that is, 180m3/h, and the lower limit of the flow rate of the cooling tower is set to 25L/s.

In the initial state, there are two cooling towers CT2 and CT3 in the open state. At this time, the total cooling water flow of the system is increased to 90L/s. At this time, three cooling towers should be opened in operation, and the water volume of each cooling tower is 30L/ s, can open the cooling tower as much as possible, using its heat exchange area, each tower is not lower than the lower limit of its flow.

The calculation process of the centerless algorithm provided by the present invention is as follows:

The adjustment process of the centerless algorithm is shown in Figure 21, and the value at the top of each rectangular frame

Indicates the flow value of the intelligent cooling tower before the current adjustment; the corresponding value at the bottom

Indicates the flow value after the current cooling tower adjustment. The arrows in the rectangular box indicate the direction of information transfer between the intelligent cooling towers during the adjustment process; the value on the upper side of the arrow indicates the flow adjustment margin ΔG of the cooling water flow, and the value on the lower side indicates the expected efficiency ε. A centerless adjustment task was initiated by Cooling Tower CT2. In the algorithm adjustment process, each intelligent cooling tower follows the same rules, judges and adjusts based on the received information, verifies whether the convergence is converged, and transmits the adjusted information to the neighbor cooling tower. Specifically, the following steps are included:

1) Cooling tower CT2→3 cooling tower CT3: CT2 initiates the adjustment task, adjusts its working point to the best target efficiency point ε=1, which is the set lower limit of running flow; The quantity and efficiency are expected to be ΔG=+20L/s and ε=1, respectively, and passed to the neighbor No. 3 intelligent cooling tower CT3.

2) Cooling tower No. 3 CT3→4 cooling tower CT4: No. 3 intelligent cooling tower CT3 received After the neighbor's information, follow the external efficiency estimates, and adjust its own efficiency point to ε=1, and calculate the system's flow adjustment margin ΔG=+40L/s at this efficiency point, and then adjust The post-updated information is passed to the neighbor No. 4 intelligent cooling tower CT4, including ΔG=+40L/s and ε=1.

3) Cooling tower CT4→3 cooling tower CT3: After receiving the information of the neighbor, CT4, the intelligent cooling tower No. 4, found that the incoming flow adjustment margin is positive ΔG=+40L/s, which is the current running cooling. The total flow provided by the tower is 40 L/s less than the set flow of the system and additional cooling towers are required to carry these flows. The cooling tower No. 4 was originally turned off, so it set itself to the startup state (in this case, it is in the iterative adjustment, the command is not executed, that is, the cooling tower does not have a real start, but only a flag state in the iterative calculation process. Only when the system adjustment process converges will the flag state be converted into the actual control command, which is actually executed by each intelligent cooling tower), while following the external incoming efficiency expectation, and adjusting its own efficiency point to ε. =1, and calculate the system's flow adjustment margin ΔG=+15L/s at this efficiency point, and then transmit the adjusted updated information to the neighbor No. 3 intelligent cooling tower CT3, including ΔG=+15L/s and ε =1.

4) Cooling tower CT3→ Cooling tower CT2: CT3: After receiving the information of the neighbor, CT3 follows the external incoming efficiency expectation, but the external incoming efficiency is expected to be adjusted after the last time. The efficiency expectation is the same, both are ε=1, and the flow adjustment margin is still not less than the preset convergence threshold (this convergence threshold can be set), indicating that under the efficiency expectation, each intelligent cooling tower cannot meet the external traffic demand. Therefore, it is necessary to adjust the expectation of efficiency. The adjustment method of the efficiency expectation may be a fixed step adjustment, that is, an increase or decrease of 0.01 each time; or a certain descent method may be adopted, and the proportional adjustment may be performed according to the magnitude of the flow deviation. The fixed step adjustment is used in this example, and the adjustment step is 0.1 each time. Thus, Cooling Tower CT3 adjusts the efficiency point to the efficiency expectation ε = 1.1, and calculates a new flow adjustment margin ΔG based on this operating point. Thus, Cooling Tower CT3 passes the information at this time to the neighboring No. 2 cooling tower, including ΔG = +12.5 L/s and ε = 1.1.

5) Cooling tower CT2→1 cooling tower CT1: After receiving the information of the neighbors, the intelligent cooling tower CT2 of 2nd follows the external incoming efficiency expectation, and adjusts its working point to ε=1.1, and calculates Under this working point, the system's flow adjustment margin ΔG=+10L/s, and then the adjusted and updated information is transmitted to the neighbor No. 1 intelligent cooling tower CT1, including Δ G = +10 L/s and ε = 1.1.

6) Cooling tower CT1→2 cooling tower CT2: After receiving the information of the neighbors, the intelligent cooling tower CT1 finds the current flow adjustment margin ΔG=+10L/s, which is the total water flow of the currently operating cooling tower. It is 10L/s less than the system flow requirement, which is less than the lower limit of 25L/s water flow starting from the start-up operation. Therefore, the cooling tower CT1 cannot be turned on. The information ΔG=+10L/s and ε=1.1 are directly transmitted to the neighboring No. 2 cooling tower CT2.

The remaining adjustment process is similar. After the final adjustment of the cooling tower No. 4, the updated information is ΔG=0L/s and ε=1.2, that is, the total flow of the current cooling tower operation is equal to the total cooling water flow demand of the system. Therefore, the No. 4 cooling tower CT4 no longer transmits information to the outside, and the cooling tower in the entire cooling tower system will not receive the external information stimulus calculation again. After waiting for a period of time, the adjustment task ends. Each intelligent cooling tower converts its current state of operation to the result of the control command indication, thereby completing an adjustment task.

After the adjustment of the non-central algorithm is completed, the original two cooling towers are operated and become three operations, each of which undertakes a flow rate of 30 L/s, so as to open as many cooling towers as possible without lowering the minimum flow rate. run.

In an alternative embodiment, the efficiency ε can also be defined as ε = G / Gmin, such that in the centerless calculation process, starting from ε = 1, the value of ε is gradually reduced to find the final result.

In another embodiment, the information transmission direction may have various forms. In the above example, the information transmission direction of the cooling tower is transmitted from CT2 to CT3, CT3 is transmitted to CT4, CT4 is transmitted to CT3, and CT3 is transmitted to CT2. , that is, reciprocating transmission, but the direction of this information transmission is not fixed and unique, but also other information transmission direction, for example, it can be transmitted from CT1 pump to CT2, CT2 to CT3, CT3 to CT4, CT4 Passed to CT1, that is, circular transfer, that is, the flow direction of this information is flexible.

The following describes the control method of the cooling tower fan:

The fan control rules of the cooling tower are as follows: as long as the water valve of the cooling tower is turned on, that is, the cooling tower is put into use, the fan is turned on; the fan that is turned on is uniformly converted, and the water temperature of the cooling tower is controlled to reach the set value of the water temperature (setting The value is given by an additional control algorithm); If the speed of the inverter fan reaches the lower limit of the speed, a fan is turned off; if the fan speed of the open fan reaches the upper limit and the fan of the cooling tower that is still open is not turned on, a fan is added.

S304: When the system determines the optimized operating parameters of each cooling tower, the cooling tower controller controls the corresponding cooling tower to reach a corresponding operating state according to the optimized operating parameters.

S305: If there is no cooling tower controller in the system to initiate the adjustment task, the cooling tower in the system keeps the original operating parameters unchanged.

According to the method for controlling a cooling tower system based on a centerless network according to an embodiment of the present invention, a centerless network formed by cooling tower controllers with communication functions of each parallel cooling tower is used, so that each parallel cooling tower determines each cooling tower through independent negotiation. The start and stop of the waterway and the start and stop and speed of the fan, the flow rate of each cooling tower is not lower than the lower limit of the flow rate when the cooling area of the cooling tower is fully utilized. The method for controlling a cooling tower system based on a centerless network according to an embodiment of the present invention has at least the following advantages:

1. In the field, only the controllers corresponding to the adjacent cooling towers in the topology need to be connected by communication. The parallel cooling towers can achieve the control objectives efficiently and independently, and avoid the cumbersome intervention of the traditional centralized control methods. The manual configuration and debugging links can also enable the plug-and-play of the device, which enhances the flexibility and scalability of the system.

2. Theoretically ensure that for a given control target, the result of the algorithm calculation is to make full use of the cooling area of the cooling tower, and the flow rate of each cooling tower is not lower than the lower limit, thus ensuring the uniformity of cooling water distribution. .

Further, in conjunction with FIG. 14, a further embodiment of the present invention also discloses a cooling tower system based on a centerless network, comprising: a plurality of cooling towers and a plurality of cooling tower controllers.

Wherein a plurality of cooling towers are arranged in parallel; a plurality of cooling tower controllers are connected to the plurality of cooling towers in a one-to-one correspondence, the cooling tower system being operated using a control method as described in the above embodiments of the present invention.

Further, the present invention also proposes a cooling tower controller in which all cooling tower controllers are interconnected to form a centerless network, and when the cooling tower controller determines that a certain trigger condition is reached The cooling tower controller is used to initiate an adjustment task. When there is a cooling tower controller in the system that initiates the conditioning task, the cooling tower controller begins to interact with its adjacent cooling tower controller and after several information interactions. Determine the optimized operating parameters of each cooling tower. The cooling tower controller controls the corresponding cooling tower to reach the corresponding operating state according to the optimized operating parameters, and maintains the corresponding cooling when there is no cooling tower controller that initiates the regulating task in the system. The tower operating parameters are unchanged.

It should be noted that the specific implementation manner and method and/or system part of the cooling tower controller of the embodiment of the present invention are similar to the specific implementation manner of the cooling tower controller. For details, refer to the description of the method and/or system part, in order to reduce Redundant, do not repeat them.

Claims

An optimized control method for reducing the total resource consumption W of the system, the system comprises a plurality of functional modules, and the total tasks completed by all the functional modules are required to meet the total task requirement Q0, and each functional module needs to consume a certain amount while completing the task. The quantity of resources Wi, the performance Ei of each functional module and its completed task Qi are in a certain functional relationship, and the resource consumption Wi of each functional module is positively correlated with its completed task and negatively correlated with the performance Ei, which is characterized by: The control method includes the steps of allocating a total system task to each functional module, wherein the total resource consumption of the system is the lowest or the lower, and the task assignment specifically includes the following steps:

The information interaction between each functional module is completed to complete the system operation. When the system operation converges, the task undertaken by each functional module is obtained, and the total resource consumption of the system is lowest or lower under the task assignment.
The method of claim 1 wherein said information interaction comprises the steps of:

A. A function is initiated by a function module, which maximizes the performance Ei, calculates the task Qi and the consumed resource Wi under the target performance, calculates the task margin according to the total task requirement, and calculates the target performance and task. The margin is sent as a delivery message to its neighboring functional modules.
The method according to claim 2, wherein the function module calculates a new target performance based on the received target performance and obtains its task after receiving the delivery information, and calculates a new task balance according to the received task margin. the amount;

If the absolute value of the task margin is greater than a certain threshold, the new target performance and the new task margin obtained by the calculation are continuously sent as the delivery information to the adjacent functional modules;

If the task margin is less than or equal to a certain threshold, it is judged whether the result of this operation is valid, if:

If the operation result is valid, the operation is converged and the delivery information is no longer sent.

If the operation result is invalid, the function module continues to initiate the operation and performs step A.
The method of claim 3 wherein if there is no longer in the system When the function module sends or receives the delivery information, the system converges, and each function module assigns a task according to the operation result of the system.
The method according to claim 3, wherein the criterion for determining whether the operation result is valid is: if the total resource consumption of the system is equal to or lower than the operation, the operation is valid, and if the total resource consumption of the system is higher than before the operation, The operation is invalid.
The method of claim 2 wherein the maximized value of said performance Ei is variable during the operation.
The method according to claim 1, wherein the information interaction process first determines whether each function module participates in task assignment and then determines a specific task value of the function module participating in the task assignment.
The method according to claim 7, wherein the process of first determining whether each functional module participates in task assignment comprises the following steps:

All functional modules form a link, which transfers information from the beginning of the chain to the end of the chain. The information includes the tasks of each functional module working at the highest performance, and the function of each functional module is calculated at the end of the chain to work at the highest performance or not participate in the task. All the combinations of the system are assigned to the total task, and the two combinations that satisfy the requirements of the total task are selected, and the data is sequentially transmitted back to the chain. At this time, each function module knows whether it should participate in the task assignment.
The method according to claim 7, wherein the process of first determining whether each functional module participates in task assignment comprises the following steps:

Each function module sets an initial participation probability;

Each functional module initiates a global weighted summation task that multiplies the participation probability of each functional module by its own sum of the results of the tasks at the optimal performance;

After each functional module receives the weighted sum of the functional modules other than itself, compares the result with the result of adding its own participation probability multiplied by its own task at the optimal performance, if the result after adding is less than The result is closer to the total task requirements of the system, indicating that it is better to participate in the task assignment, thus increasing the probability of participation; otherwise, the probability of participation is reduced;

After several iterations, the participation probability of each functional module tends to be stable, and the functional modules whose convergence probability is converged to 1 or greater than a certain threshold participate in task assignment; A function module that is concatenated to 0 or less than a predetermined threshold does not participate in task assignment.
The method according to claim 7, wherein the process of determining a specific task value of a function module participating in the task assignment comprises the following steps:

Transfer the equivalent resource consumption between each functional module to the derivative of the task and the corresponding task of each functional module under the derivative. If the total task value of the system is higher than the total task requirement, the derivative is reduced, if the total task of the system The value is lower than the total task requirement and the derivative is increased until the total task of the system meets the total task requirements.
An optimization control method for assigning tasks to various functional units in a system, the system comprising a plurality of functional modules, the total tasks completed by all functional modules are required to satisfy the total task requirement Q0, and the tasks completed by each functional module are higher than the tasks The lower limit Q low , and the more functional modules in the running state, the better: the task assignment includes the following steps: performing information interaction between each functional module to complete the system operation, and each time the system operation is completed, each is obtained. The tasks undertaken by the function module, under which the function modules in the running state are the most and the tasks completed by each function module are higher than the task lower limit Q low .
The method of claim 11 wherein said information interaction comprises the steps of:

All the functional modules of the whole system form a link, and the information is sequentially transmitted from the beginning of the chain to the end of the chain. The information includes the task values of the functional modules respectively working at the lower limit of the task, and the tail task calculates the total task that satisfies the requirements. Combine and pass the data back to the beginning of the chain, at which point each function module knows if it should be running.
The method according to claim 12, wherein the total task satisfies the requirement that the total task value of each functional module is closest to the total task requirement of the system and the task of each functional module is not lower than the lower limit of the preset task. value.
The method of claim 12 wherein the chain header and the chain tail in the link are variable during the information interaction.
A pump system control method based on a centerless network, characterized in that the method comprises the following steps:

Providing a separate controller for each of the pumps in the pump system and interconnecting all of the controllers to form a centerless network;

When the water pump controller determines that a certain trigger condition is reached, the water pump controller initiates an adjustment task;

If there is a water pump controller in the system that initiates the adjustment task, the water pump controller in the system begins to exchange information with its adjacent water pump controller;

After several times of information interaction, the whole system reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each water pump, and the controller controls the corresponding water pump to reach a corresponding operating state according to the optimized operating parameters;

If there is no pump controller in the system to initiate the adjustment task, keep the pump operating parameters unchanged.
The method according to claim 15, wherein each of the running water pump controllers can initiate an adjustment task, and the remaining water pump controllers complete the system operation in conjunction with the adjustment task.
The method according to claim 16, wherein if a plurality of water pump controllers initiate adjustment tasks in the same or similar time, system operations are performed separately for each adjustment task, and then which is determined by an arbitration mechanism. Adjust the operation result of the task.
The method according to claim 16, wherein if the plurality of water pump controllers initiate the adjustment task in the same or similar time, the initiator is first selected by the arbitration mechanism, and then the initiator is initiated. Adjust the task.
The method according to claim 17 or 18, wherein the arbitration mechanism comprises one or more of manually assigning priorities, lottery, grab tokens, and random assignments.
The method according to claim 15, wherein the certain triggering condition is that the absolute value of the deviation between the current differential pressure measurement value and the differential pressure set value of the corresponding water pump collected by the controller exceeds the pre-predetermined value. Set the differential pressure deviation threshold.
The method of claim 15 wherein said certain triggering condition is that said water pump controller has reached a predetermined control period.
The method according to claim 15, wherein the certain triggering condition is that the energy consumption of the system needs to be optimized.
The method of claim 15 wherein the water pump controller that initiates the adjustment task performs the following steps:

A. The controller refers to the current actual working point of the corresponding pump, adjusts the expectation with the target efficiency point as the efficiency, and calculates a new pump flow rate and flow adjustment margin;

B. The controller writes the efficiency adjustment expectation and the flow adjustment margin to the delivery information and sends it to its adjacent water pump controller.
The method of claim 23 wherein the pump controller receiving the transfer information performs the following steps:

C. Comparing the received efficiency adjustment expectation with the current efficiency adjustment expectation of the corresponding pump, referring to the received flow adjustment margin, the operation obtains a new efficiency adjustment expectation;

D. Adjust the expected and received flow adjustment margin based on the new efficiency, calculate the new pump flow and the new flow adjustment margin; and perform one of the following steps:

D1: If the absolute value of the new flow adjustment margin is higher than the preset adjustment margin threshold, indicating that the adjustment target has not been reached, the new efficiency adjustment expectation and the new flow adjustment margin are written into the delivery information and sent to the adjacent Controller

D2: If the absolute value of the new flow adjustment margin is lower than or equal to the preset adjustment margin threshold, then perform one of the following steps:

D2a: If the system energy consumption is equal to or lower than before the adjustment task is initiated, the delivery information is no longer sent;

D2b: If the system energy consumption is higher than before the adjustment task is initiated, the new target efficiency point is set as the efficiency adjustment expectation, and the new pump flow rate and the new flow adjustment margin are calculated; the controller will be new The efficiency adjustment expectation and the new flow adjustment margin are written to the delivery information and sent to its adjacent pump controller.
A method as claimed in claim 23 or 24, wherein said target efficiency point is variable during the calculation.
The method according to claim 23 or 24, characterized in that the water pump controller does not cause frequent start and stop of the water pump when calculating the new water pump flow rate.
The method according to claim 23 or 24, wherein the process of determining whether the predetermined convergence condition is reached comprises the following steps:

All controllers that receive the delivery message start timing when they receive the delivery message and perform one of the following steps:

F1: If the subsequent neighboring controllers are not received in the predetermined convergence period Information, then it is determined that a predetermined convergence condition is reached;

F2: If the transmission signals of other adjacent controllers are subsequently received within the predetermined convergence period, the timing is re-timed.
The method according to claim 15, wherein the process of information interaction first determines whether each water pump is turned on and then determines a specific flow rate of the opened water pump.
The method of claim 28 wherein the step of determining whether each pump is turned on further comprises the steps of:

The controller of the whole system forms a link, which transfers information from the beginning of the chain to the end of the chain. The information includes the flow when each pump works at the highest efficiency point, and the tail end calculates the maximum efficiency of each pump. The total system flow rate under all combinations of points or shutdowns, and the two combinations that satisfy the total flow rate are selected, and the data is sequentially transmitted back to the chain head. At this time, each pump controller knows whether it should be turned on.
The method according to claim 29, wherein said total flow rate most satisfies the requirement that the total flow rate is closest to the total system flow demand in the case where the pump is not frequently started and stopped.
The method according to claim 28, characterized in that: determining whether each pump is turned on performs the following steps on the premise that the pump does not frequently start and stop:

Set an initial turn-on probability for each pump;

Each pump controller initiates a global weighted summation task that is the sum of the results of each pump's own opening probability multiplied by its own flow at the optimal efficiency point;

After each pump controller receives the weighted sum of the pumps other than itself, compares the result with the result of adding its own opening probability multiplied by its own flow at the optimal efficiency point, if the result is added If the result is closer to the total flow demand of the system than the unsuccessful result, it means that it is better to join, so it will increase its own opening probability; otherwise, it will lower its own opening probability;

After several iterations, the opening probability of each pump tends to be stable, and the state of the water pump whose convergence probability is converged to 1 or greater than a predetermined threshold is set to ON; the state of the water pump whose convergence probability converges to 0 or is less than a certain preset threshold Set to off.
The method of claim 28 wherein said determining is turned on The step of the specific flow of the water pump further includes the following steps:

The pump controller of the whole system forms a link, and the transmission information is sent from the chain head to the chain tail in turn, and the transmission information includes the derivative of the equivalent power consumption to the flow rate and the corresponding flow rate of each water pump under the derivative; The total flow of the system is summed. If it is higher than the total flow demand of the system, and the absolute value of the deviation exceeds a predetermined threshold, the derivative of the power consumption in the transmitted information is reduced, and the transmission information is retransmitted in the link; if it is lower than the system The total flow demand, and the absolute value of the deviation is higher than the predetermined threshold, the power consumption/flow derivative in the transmission information is increased, and the transmission information is re-transmitted in the link; if the absolute deviation from the total flow demand of the system is lower than or If it is equal to the predetermined threshold, the delivery information is no longer sent, and the system converges.
Method according to claim 29 or 32, characterized in that the chain header and the chain tail in the link are variable during the information interaction.
The method according to claim 15, wherein when the predetermined convergence condition is reached, all controllers calculate the start and stop state of the water pump and/or the rotational speed of the water pump based on the water pump flow value, and change the corresponding water pump to a new operation. parameter.
The method according to claim 15, wherein the implementation of the centerless network is one of a wired network and a wireless network or a combination of both.
The method of claim 15 wherein the control algorithms of the plurality of controllers are the same.
The method of claim 15 wherein said controller changes operating parameters of the corresponding water pump by controlling a frequency converter coupled to said water pump.
The method according to claim 20, wherein the measurement position of the differential pressure measurement value is specified according to a demand of the control.
The method according to claim 38, wherein said differential pressure measurement value refers to a pressure difference between an inlet and an outlet of the water pump or a pressure difference of a branch of the end system or a manifold. The difference between the pressures.
Method according to claim 23, 24 or 32, characterized in that the flow rate of the water pump is calculated from the performance curve of the water pump.
The method of claim 40 wherein the pump performance curve is input to the water pump controller.
The method according to claim 41, characterized in that the performance of the water pump The line is variable. The way to change includes: manually verifying the performance of the pump and manually modifying it; or adding a self-learning algorithm inside the controller to automatically detect the performance of the pump and adjust the performance curve of the pump during the operation of the pump.
A pump system based on a centerless network, characterized in that it comprises:

a plurality of water pumps, the plurality of water pumps being arranged in parallel;

a plurality of controllers, wherein the plurality of controllers are connected to the plurality of water pumps in a one-to-one correspondence, wherein all of the controllers are interconnected to form a centerless network;

The water pump system operates using the control method as claimed in claims 15 to 42.
A water pump controller is characterized in that all water pump controllers are interconnected to form a centerless network. When the water pump controller determines that a certain trigger condition is reached, the water pump controller is used to initiate an adjustment task, and an adjustment task is initiated in the system. The water pump controller starts to exchange information with its adjacent water pump controller, and after a number of information interactions, reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each water pump. The controller controls the corresponding pump to reach the corresponding operating state according to the optimized operating parameters, and keeps the corresponding pump operating parameters unchanged when there is no pump controller that initiates the regulating task in the system.
A method for controlling a chiller system based on a centerless network, comprising the steps of:

Providing a chiller controller for each chiller in the chiller system and interconnecting all chiller controllers to form a centerless network;

When the cold controller determines that a certain trigger condition is reached, the cooling controller initiates an adjustment task;

If there is a cold controller in the system that initiates the adjustment task, the cold controller in the system begins to interact with the information of the adjacent cold controller;

After several times of information interaction, the entire system reaches a predetermined convergence condition, thereby determining the optimized operating parameters of each chiller, and the chiller controller controls the corresponding chiller to reach a corresponding operating state according to the optimized operating parameters;

If there is no cold controller in the system to initiate the adjustment task, keep the cold running parameters unchanged.
The method of claim 45 wherein each one is in operation The cold controller of the state can initiate the adjustment task, and the remaining cold controllers complete the system operation with the adjustment task.
The method according to claim 46, wherein if a plurality of cold controllers initiate adjustment tasks in the same or similar time, system operations are performed separately for each adjustment task, and then execution is determined by an arbitration mechanism. Which adjustment operation result of the task.
The method according to claim 46, wherein if a plurality of cold controllers initiate an adjustment task in the same or similar time, an initiator is selected by an arbitration mechanism, and then execution is initiated by the initiator. Adjustment task.
The method according to claim 47 or 48, wherein the arbitration mechanism comprises one or more of manually assigning priorities, lottery, robbing tokens, and random assignments.
The method according to claim 45, wherein the certain triggering condition is that the cold controller reaches a preset control period.
The method according to claim 45, wherein the certain triggering condition is that the energy consumption of the system needs to be optimized.
The method of claim 45 wherein the chiller controller initiating the tuning task performs the following steps:

A. The cold machine controller refers to the current actual working point of the cold machine, adjusts the expectation with the target efficiency point as the efficiency, and calculates a new cold machine operating parameter and a cooling capacity adjustment margin;

B. The chiller controller writes the efficiency adjustment expected and the cold regulation margin to the delivery information for transmission to its neighboring chiller controller.
The method of claim 52 wherein the chiller controller receiving the delivery information performs the following steps:

C. Comparing the received efficiency adjustment expectation with the current efficiency adjustment expectation of the corresponding cold machine, referring to the received cooling capacity adjustment margin, the operation obtains a new efficiency adjustment expectation;

D. Adjust the expected and received cooling capacity adjustment according to the new efficiency, calculate the new chiller operating parameters and the new cooling capacity adjustment, and perform one of the following steps:

D1: If the absolute value of the new cooling capacity adjustment margin is higher than the preset adjustment margin threshold, indicating that the adjustment target has not been reached, the new efficiency adjustment expectation and the new cooling capacity adjustment margin are written into the transmission information and sent to the transmission information. Adjacent cold machine controller;

D2: If the absolute value of the new cooling capacity adjustment is lower than or equal to the preset adjustment margin threshold, then perform one of the following steps:

D2a: If the system energy consumption is equal to or lower than before the adjustment task is initiated, the delivery information is no longer sent;

D2b: If the system energy consumption is higher than before the adjustment task is initiated, the new target efficiency point is set as the efficiency adjustment expectation, and the new cold operation parameter and the new cold adjustment margin are calculated, the cold machine The controller writes the new efficiency adjustment expectation and the new cold regulation margin to the delivery information and sends it to its neighboring cold controller.
A method according to claim 52 or 53, wherein said target efficiency point is variable during the calculation.
The method according to claim 52 or 53, wherein the cold controller does not cause frequent restarts of the cold machine when calculating the new cold operating parameters.
The method according to claim 52 or 53, wherein the process of determining whether the predetermined convergence condition is reached comprises the following steps:

All the cold controllers that receive the delivery information start timing at the moment the message is received, and perform one of the following steps:

F1: if the delivery information of other adjacent cold controllers is not received subsequently in the predetermined convergence period, it is determined that the predetermined convergence condition is reached;

F2: If the transmission signal of other adjacent cold controllers is subsequently received within the predetermined convergence period, the timing is re-timed.
The method according to claim 45, wherein the process of information interaction first determines whether each chiller is turned on and then determines a value of a cold load specifically assumed by the chiller that is turned on.
The method of claim 57, wherein the step of determining whether each chiller is turned on further comprises the steps of:

The cold controller of the whole system forms a link, which transfers information from the beginning of the chain to the end of the chain. The information includes the cooling load when each cold machine works at the highest efficiency point, and each cold is calculated at the end of the chain. The machine works at the highest efficiency point or the total cooling load under all combinations when it is not working, and selects the two combinations whose total cooling load best meets the requirements, and the data is sequentially transmitted back to the chain head, at which time each cold controller knows Whether it should be turned on.
The method according to claim 58, wherein said total cooling load is most satisfactory to mean that the total cooling load is closest to the total cooling load demand of the system in the case of meeting the infrequent start and stop of the cold machine.
The method according to claim 57, characterized in that: determining whether each cold machine is turned on performs the following steps on the premise that the cold machine does not frequently start and stop:

Set an initial turn-on probability for each chiller;

Each chiller controller initiates a global weighted summation task that is the sum of the results of each chiller's own turn-on probability multiplied by its own cold load at the optimal efficiency point;

After each chiller controller receives the weighted sum of the chillers other than itself, compares the result with the result of adding its own opening probability multiplied by its own cooling load at the optimal efficiency point, if added The subsequent result is closer to the total cooling load demand of the system than the unadded result, indicating that it is better to join, so it will increase its own opening probability, otherwise, it will lower its own opening probability;

After several iterations, the probability of opening each chiller tends to be stable, and the state of the chiller whose convergence probability converges to 1 or greater than a certain threshold is set to ON, and the probability of convergence converges to 0 or is less than a certain threshold. The status of the machine is set to off.
The method of claim 57 wherein said step of determining a value of a particular cold load of the turned-on cold further comprises the steps of:

The cold controller of the whole system forms a link, and the transmission information is sent from the chain head to the chain tail in turn, and the transmission information includes the derivative of the equivalent power consumption to the cooling load and the corresponding cold load of each cold machine under the derivative. The chain tail sums the total cooling load of the system. If it is higher than the total cooling load demand of the system, and the absolute value of the deviation exceeds the predetermined threshold, the derivative of the power consumption in the transmitted information is reduced to the cold load, and is transmitted again in the link. The information, if it is lower than the total cooling load demand of the system, and the absolute value of the deviation is higher than the predetermined threshold, the power consumption/cool load derivative in the transmitted information is increased, and the transmission information is transmitted again in the link, if it is related to the total system If the absolute value of the deviation of the cold load demand is lower than or equal to the predetermined threshold, the transfer information is no longer transmitted, and the system converges.
Method according to claim 56 or 61, characterized in that the chain header and the chain tail in the link are variable during the information exchange.
The method of claim 52 or 53, wherein said chiller operating parameters comprise a cold load of the chiller.
The method of claim 45 wherein all of the chiller controllers calculate the start and stop status of the chiller and/or the chilled water flow demand based on the optimized operating parameters of the chiller upon reaching a predetermined convergence condition. / or chilled water outlet temperature set value, and change the corresponding cold machine to the new operating parameters.
The method according to claim 45, wherein the implementation of the centerless network is one of a wired network and a wireless network or a combination of both.
The method of claim 45 wherein the control algorithms in the plurality of said chiller controllers are the same.
Method according to claim 52, 53 or 61, characterized in that the operating parameters of the cold machine are calculated from the performance curve of the cold machine.
The method of claim 67 wherein the performance profile of said chiller is input to a chiller controller.
The method according to claim 68, wherein the performance curve of the cold machine is variable, and the manner of changing comprises: periodically performing test calibration on the performance of the cold machine and manually modifying the same, or adding self-learning inside the cold controller. The algorithm automatically detects the performance of the chiller and adjusts the performance curve of the chiller during the operation of the chiller.
A chiller system based on a centerless network, comprising:

Multiple cold machines;

a plurality of cold machine controllers, wherein the plurality of cold machine controllers are connected to the plurality of cold machines in a one-to-one correspondence, wherein all the cold machine controllers are interconnected to form a centerless network;

The chiller system is operated using the control method of any one of claims 45 to 69.
A cold machine controller, characterized in that all the cold machine controllers are interconnected to form a centerless network, and when the cold machine controller determines that a certain trigger condition is reached, the cold machine controller is used to initiate an adjustment task in the system. When there is a cold controller that initiates the adjustment task, the cold controller begins to exchange information with its neighboring cold controller, and after a number of information interactions, reaches a predetermined convergence condition, thereby determining each cold machine optimization. After the operating parameters, the cold controller controls the corresponding cold machine according to the optimized operating parameters When the corresponding operating state is reached and the chiller controller that does not initiate the tuning task in the system is maintained, the corresponding chiller operating parameters are maintained.
A method for controlling a cooling tower system based on a centerless network, comprising the steps of:

Providing one cooling tower controller for each cooling tower in the cooling tower system and interconnecting all cooling tower controllers to form a centerless network;

When the cooling tower controller determines that a certain trigger condition is reached, the cooling tower controller initiates an adjustment task;

If there is a cooling tower controller in the system that initiates the conditioning task, the cooling tower controller in the system begins to interact with its adjacent cooling tower controller;

After several times of information interaction, the system determines the optimized operating parameters of each cooling tower, and the cooling tower controller controls the corresponding cooling tower to reach a corresponding operating state according to the optimized operating parameters;

If there is no cooling tower controller in the system to initiate the conditioning task, keep the cooling tower operating parameters unchanged.
The method of claim 72, wherein each of the cooling tower controllers can initiate an adjustment task, and the remaining cooling tower controllers cooperate with the adjustment task to complete the system operation.
The method according to claim 73, wherein if a plurality of cooling tower controllers initiate adjustment tasks in the same or similar time, respectively performing system operations according to each adjustment task, and then determining execution by an arbitration mechanism Which adjustment operation result of the task.
The method according to claim 73, wherein if a plurality of cooling tower controllers initiate an adjustment task in the same or similar time, an initiator is selected by an arbitration mechanism, and then execution is initiated by the initiator. Adjustment task.
The method according to claim 74 or 75, wherein the arbitration mechanism comprises one or more of manually assigning priorities, lottery, grab tokens, and random assignments.
The method of claim 72 wherein said certain triggering condition is that said cooling tower controller has reached a predetermined control period.
The method of claim 72 wherein said certain trigger The condition is: the cooling water flow has changed, the cooling pump group calculates the current cooling water flow and sends it to the controller of the cooling tower, and the corresponding cooling tower controller receives the information and triggers the adjustment task.
The method according to claim 72, wherein the process of information interaction determines whether the water path of each cooling tower is turned on so as to be as much as possible under the premise that the flow rate of each cooling tower is higher than a preset lower limit value. Turn on the water circuit of the cooling tower.
The method according to claim 79, wherein the step of determining whether the water path of each cooling tower is turned on further comprises the steps of:

The controller of the whole system forms a link, and the information is sequentially transmitted from the beginning of the chain to the end of the chain. The information includes the flow of each cooling tower working at the lower limit of the minimum flow, and the tail of the chain calculates the minimum operation of each cooling tower. The total flow under all combinations of the lower flow limit or the waterway is not open and the combination of the total flow is selected to meet the requirements, and the data is sequentially transmitted back to the chain head. At this time, each cooling tower controller knows whether it should be turned on.
The method according to claim 80, wherein said total flow rate most satisfies the requirement that the total flow rate of all cooling towers is closest to the total cooling water flow demand of the system and the flow rate of each cooling tower is not lower than a preset lower limit. value.
The method of claim 80 wherein the chain header and the chain tail in the link are variable during the information interaction.
The method according to claim 79, wherein the step of determining whether the water path of each cooling tower is turned on further comprises the steps of:

The cooling tower controller that initiates the tuning task performs the following steps:

A. The cooling tower controller refers to the current actual working point of the corresponding cooling tower, adjusts the expectation with the target efficiency point as the efficiency, and calculates a new cooling water flow rate and a flow regulating margin;

B. The cooling tower controller writes efficiency adjustment expectations and flow adjustment margins to the delivery information for transmission to its adjacent cooling tower controller.
The method of claim 83 wherein the cooling tower controller receiving the delivery information performs the following steps:

C. Comparing the received efficiency adjustment expectation with the current efficiency adjustment expectation of the corresponding cooling tower, and referring to the received flow adjustment margin, the operation obtains a new efficiency adjustment expectation;

D. Adjust the expected and received flow adjustment margin according to the new efficiency, and calculate New cooling water flow and new flow adjustment margin; and perform one of the following steps:

D1: If the absolute value of the new flow adjustment margin is higher than the preset adjustment margin threshold, indicating that the adjustment target has not been reached, the new efficiency adjustment expectation and the new flow adjustment margin are written into the delivery information and sent to the adjacent Controller

D2: if the absolute value of the new flow adjustment margin is lower than or equal to the preset adjustment margin threshold, the delivery information is no longer sent;
The method according to claim 83 or 84, wherein the efficiency is calculated as: efficiency = actual flow rate of the cooling tower / lower flow limit of the cooling tower, or efficiency = lower limit of the flow rate of the cooling tower / cooling tower Actual traffic.
A method according to claim 83 or 84, wherein said target efficiency point is variable during the calculation, and the change is fixed step size or variable step size.
The method according to claim 83 or 84, wherein the cooling tower controller does not cause frequent switching, starting and stopping of the cooling tower when calculating the new cooling water flow rate.
The method of claim 72 wherein after the system determines the optimized operating parameters, all of the cooling tower controllers control opening and closing of the valves on the cooling tower waterway so that the cooling tower waterways reach a corresponding start-stop state.
The method of claim 72 wherein said optimized operating parameters include a start-stop state and/or frequency of a cooling tower fan.
The method according to claim 89, characterized in that the start-stop state and/or the rotational speed of the cooling tower fan are optimized according to the following principle: as long as the water valve of the corresponding cooling tower is turned on, the corresponding fan is turned on, and the opened fan is uniformly frequency-converted. Controlling the outlet temperature of the cooling tower to reach the set value of the outlet water temperature. If the speed of the fan reaches the lower limit of the speed, a fan is turned off. If the fan speed of the open fan reaches the upper limit and the fan of the cooling tower that is still open is not turned on, Add a fan.
The method according to claim 72, wherein the implementation of the centerless network is one of a wired network and a wireless network or a combination of both.
The method of claim 72 wherein the control algorithms in the plurality of said cooling tower controllers are the same.
The method of claim 72 wherein said cooling tower control The controller changes the operating parameters of the corresponding cooling tower by controlling the valves on the corresponding cooling tower waterway and the cooling tower fan.
80. The method of claim 80 wherein the lower flow rate lower limit of the cooling tower is input to the cooling tower controller.
A cooling tower system based on a centerless network, comprising:

a plurality of cooling towers, wherein the plurality of cooling towers are arranged in parallel;

a plurality of cooling tower controllers, the plurality of cooling tower controllers being connected to the plurality of cooling towers in a one-to-one correspondence, wherein all of the cooling tower controllers are interconnected to form a centerless network;

The cooling tower system is operated using the control method of any one of claims 72 to 94.
A cooling tower controller characterized in that all cooling tower controllers are interconnected to form a centerless network, and when the cooling tower controller determines that a certain triggering condition is reached, the cooling tower controller is used to initiate an adjustment task in the system. When there is a cooling tower controller that initiates an adjustment task, the cooling tower controller begins to interact with its adjacent cooling tower controller and determines the optimized operating parameters of each cooling tower after several information interactions. The cooling tower controller controls the corresponding cooling tower to reach the corresponding operating state according to the optimized operating parameters, and keeps the corresponding cooling tower operating parameters unchanged when there is no cooling tower controller that initiates the regulating task in the system.