CN111780384A - Central air-conditioning control system - Google Patents
Central air-conditioning control system Download PDFInfo
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- CN111780384A CN111780384A CN202010543654.4A CN202010543654A CN111780384A CN 111780384 A CN111780384 A CN 111780384A CN 202010543654 A CN202010543654 A CN 202010543654A CN 111780384 A CN111780384 A CN 111780384A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
- F24F2110/22—Humidity of the outside air
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Abstract
The invention provides a central air-conditioning control system, comprising: the sensor equipment is used for acquiring the operation data of the central air conditioner; the local intelligent PLC control center is used for acquiring the operation data acquired by the sensor equipment and forwarding the operation data to the cloud intelligent control center; the cloud intelligent control center is used for receiving the equipment operation data, processing the operation data, generating a control instruction and sending the control instruction to the local intelligent PLC control center; and the local intelligent PLC control center is used for controlling the central air conditioner according to the control instruction. This central air conditioning control system can carry out accurate control to every equipment through PLC control center. The optimized operation scheme can be analyzed and calculated according to the operation records accumulated by the central air-conditioning system, the optimized central air-conditioning operation scheme is matched in advance according to the current condition and the air-conditioning terminal requirement forecast, real-time optimization is realized, the system operation energy efficiency is improved, and the energy consumption is reduced.
Description
Technical Field
The invention relates to the technical field of air conditioner control, in particular to a central air conditioner control system.
Background
National policy, energy conservation is a key and most direct and effective important measure at present. The air-conditioning energy-saving project becomes a national key transformation and research strategy.
At present, the domestic standard has clear energy efficiency requirements for each device, but the clear requirements for the energy efficiency of the whole system are lacked.
Generally, the automatic control of a general central air conditioner mainly focuses on the control of functions, but a control system aiming at overall energy efficiency is lacked, and an efficient system which can be really realized and is implemented in a project is lacked.
Disclosure of Invention
In view of this, the technical problem to be solved by the present invention is to provide a central air conditioning control system, which can perform overall energy efficiency control of a central air conditioner, improve the utilization efficiency of the central air conditioning system, and reduce energy consumption.
The technical scheme of the invention is realized as follows:
a central air conditioning control system comprising:
the sensor equipment is used for acquiring the operation data of the central air conditioner;
the local intelligent PLC control center is used for acquiring the operation data acquired by the sensor equipment and forwarding the operation data to the cloud intelligent control center;
the cloud intelligent control center is used for receiving the equipment operation data, processing the operation data, generating a control instruction and sending the control instruction to the local intelligent PLC control center;
and the local intelligent PLC control center is used for controlling the central air conditioner according to the control instruction.
Preferably, the sensor device is used for acquiring one or more of refrigeration host, chilled water pump, cooling tower, air conditioning unit, valve, water treatment and service condition data of the interior of the building.
Preferably, the cloud intelligent control center comprises a receiving module, a storage module and a data processing module;
the receiving module is used for receiving and reading the equipment operation data; respectively storing the equipment operation data to corresponding positions of a storage module through compiling and classifying;
the data processing module is used for calling the data in the storage module, performing score and processing, and realizing data modeling, self-optimization and trend control.
Preferably, the data modeling comprises:
performing data analysis and modeling according to the host energy efficiency, the chilled water pump energy efficiency, the cooling water pump energy efficiency and the cooling tower energy efficiency;
with the aim of minimizing the total energy consumption of the air conditioning system, the original independent and controllable factors are determined: the operation number and frequency of the chilled water pumps, the operation number and frequency of the cooling towers, the supply water temperature of the chilled water, and the outdoor temperature and humidity of the external factor environment;
determining the mathematical model relation of the total energy consumption of the system and the factors:
the total energy consumption of the system is equal to the energy efficiency of the main engine, the energy efficiency of the refrigerating water pump, the energy efficiency of the cooling water pump and the energy efficiency of the cooling tower are equal to f (the running number and frequency of the refrigerating water pump, the running number and frequency of the cooling tower, and the outdoor temperature and humidity of the environment with the supply water temperature of the refrigerating water).
Preferably, the establishing of the mathematical model relationship further includes:
s1, establishing a database; storing and sorting operation data generated in the operation process of the air conditioning system into a cloud database;
s2, establishing a rule; and performing fuzzification processing in a cloud database through the collected data, and establishing a function mapping relation according to the established function model.
S3, reversely analyzing the optimal solution set; when a new operation starts, the most solution set is solved according to the established function mapping relation and fed back to the operation in the system.
S4, feedback correction and rolling optimization; the functional mapping relationships are continuously adjusted and optimized according to the new operational data generated.
Preferably, the database is established;
the data acquisition is 1 group/minute, the accumulated data volume is more than or equal to 10000 groups of data, and the data acquisition comprises continuous operation working condition data.
Preferably, the local intelligent PLC control center comprises a central air-conditioning host control module, a water flow control module, a cooling system control module and a tail end control module.
According to the central air-conditioning control system provided by the invention, the sensor equipment is used for acquiring the operation data of the central air-conditioning, the cloud intelligent control center is used for receiving the equipment operation data, processing the operation data and generating a control instruction to be sent to the local intelligent PLC control center, so that the local intelligent PLC control center can control the central air-conditioning according to the optimized control scheme given by the cloud intelligent control center, the use efficiency of the central air-conditioning system is improved, and the energy consumption is reduced.
Drawings
Fig. 1 is a block diagram of a central air conditioning control system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of factors influencing the operation of an air conditioner in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a central air conditioning control system, including:
the sensor device 1 is used for acquiring the operation data of the central air conditioner;
the local intelligent PLC control center 2 is used for acquiring the operation data acquired by the sensor equipment and forwarding the operation data to the cloud intelligent control center;
the cloud intelligent control center 3 is used for receiving the equipment operation data, processing the operation data, generating a control instruction and sending the control instruction to the local intelligent PLC control center;
and the local intelligent PLC control center 2 is used for controlling the central air conditioner according to the control instruction.
Therefore, the central air-conditioning control system provided by the embodiment of the invention collects the operation data of the central air-conditioning through the sensor equipment, receives the equipment operation data through the cloud intelligent control center, processes the operation data, generates the control instruction and sends the control instruction to the local intelligent PLC control center, so that the local intelligent PLC control center can control the central air-conditioning according to the optimized control scheme given by the cloud intelligent control center, the use efficiency of the central air-conditioning system is improved, and the energy consumption is reduced.
The operation energy consumption of the air conditioning system is related to a plurality of factors, including 1) outdoor temperature, 2) outdoor humidity, 3) outdoor fresh air volume, 4) orientation of a building, 5) a building enclosure, 6) window-wall ratio, 7) personnel density inside the building, 8) lighting, 9) equipment, 10) indoor temperature, 11) indoor humidity, 12) chilled water supply and return water temperature of the air conditioning system, 13) chilled water flow, 14) chilled water supply and return water temperature, 15) chilled water flow, 16) water pump rotating speed, 17) cooling tower fan rotating speed, 18) air conditioning air supply volume and 19) air supply temperature.
For a particular building, the outdoor factors (1 to 3 above) and the factors of the building itself (4 to 11 above) cannot be directly used as targets for adjustment to adjust the air conditioning system, but the factors 1 to 11 have a direct influence on the energy consumption of the air conditioner. Therefore, the adjustment of the air conditioning system needs to take both external conditions and internal factors into consideration, and the simultaneous action of the factors needs to be considered.
In the conventional central air conditioning control, due to uncertainty of operation and functional relation, an air conditioning system is often controlled according to one of factors (such as chilled water flow) and assuming that all other variables are fixed. The primary purpose of such control is to ensure normal operation of the air conditioning system, not the energy consumption index of the air conditioning system operation.
In order to measure the performance level of the air conditioning system, the energy efficiency ratio (kW/RT) of the system is used as an index for measuring the performance of the system, and the energy efficiency ratio (kW/RT) is the electric quantity (kW) consumed by the air conditioning system every time 1 cold ton (RT) cold quantity is generated by the air conditioning system and comprises a refrigeration host, a freezing water pump, a cooling tower and the power consumption of the tail end of an air conditioner. When the numerical value is lower, the performance of the system is better, and the energy consumption is lower.
The factors influencing the energy consumption of the air conditioning system are numerous, and a simplified method is generally adopted in the traditional control method, namely, one of the influencing factors is selected as a main variable, and the control is carried out in a mode of assuming other factors as fixed values. The control method proposes to take all the influencing factors into account in this control process. But the extent and even direction of the effect on the operational result varies due to various factors. Therefore, the cloud large-scale computing center analyzes and classifies all the factors and assigns a weight ratio to find out main influence factors, particularly independent and original factors, of each stage by using an intelligent algorithm.
Among the factors affecting the central air conditioning system, there are external factors and internal factors of the system, and the factors affect each other to determine the total energy consumption of the air conditioner. For a particular building, we simplify the relationship between the factors that influence, as shown in FIG. 1. Aiming at minimizing the total energy consumption of the air conditioning system, we find out an original independent and controllable factor: number of running chilled water pumps NchpFrequency FchpAnd the running number N of the cooling water pumpscwpFrequency FcwpNumber of cooling towers NctFrequency FctSupply temperature T of chilled waterchwsAnother 2 important external factors: ambient outdoor temperature ToutHumidity Hout。
The factors affecting the operation of the air conditioner are shown in fig. 2.
There is a mathematical model relationship between the total system energy consumption and these 9 factors:
Etotal＝Ech+Echp+Ecwp+Ect＝f(Nchp,Fchp,Ncwp,Fcwp,Nct,Fct,Tchws,Tout,Hout)
because these factors and system energy consumption are not simple functional relationships, it is also difficult to find a simple mathematical model to accurately establish the relationship. Therefore, a big data calculation method is introduced, and a corresponding function mapping relation is found in the interval by establishing an array model for a large amount of historical data. This process can be broken down into four steps:
and (5) establishing a database. The database is established by storing and sorting the operation data generated in the operation process of the air conditioning system and storing the operation data into the cloud database. The collection of historical data is once per minute (set as one group), the accumulated data amount is not less than 10000 groups of data, and the collection of data comprises various working conditions of continuous operation.
And (5) establishing a rule. And performing fuzzification processing in a cloud database through the collected data, and establishing a function mapping relation according to the established function model.
And reversely analyzing the optimal solution set. When new operation starts, an optimal solution set is solved according to the established function mapping relation and fed back to the operation in the system.
Feedback correction and roll optimization. Since the air conditioning system changes with the change of the density of different indoor people, the function mapping relation needs to be continuously adjusted and optimized according to the generated new operation data, so that the whole operation process is a continuous rolling optimization process.
In a preferred embodiment of the present invention, the cloud intelligent control center includes a receiving module, a storage module, and a data processing module;
the receiving module is used for receiving and reading the equipment operation data; respectively storing the equipment operation data to corresponding positions of a storage module through compiling and classifying;
the data processing module is used for calling the data in the storage module, performing score and processing, and realizing data modeling, self-optimization and trend control.
The regulation and feedback of the air conditioning system have obvious hysteresis, so that the traditional air conditioning system mostly adopts a fuzzy control method to counteract the hysteresis of the operation variables in the air conditioning operation process. Under the condition of establishing a functional relation and an optimal solution, the trend control is feasible, and energy consumption waste caused by much hysteresis can be reduced.
In big data statistics, the probability trends of different seasons and different time periods are obtained by analyzing external factors such as outdoor temperature, outdoor humidity and building personnel density, and the air conditioning system is accurately controlled in advance through the probability trends. For example, when the outdoor temperature of a certain period of time starts to drop from 33 ℃, according to big data analysis, the approximate rate event is that the air temperature drops according to the change rate of one degree centigrade per hour and finally is kept at 30 degrees centigrade. The cloud intelligent control center makes a judgment in advance, determines the external factor outdoor temperature after 15 minutes, finds the operation parameter of the optimal mapping relation in the big data and the function relation, starts to adjust towards the optimal operation parameter, and makes a judgment and executes an action in advance. In the progressive process of judging and executing actions in advance, the cloud intelligent control center continuously monitors and analyzes data collected by the sensors. Since the external factors have a gradual change and stability, the trend control can be smoothly performed. Outdoor humidity and building personnel density are also used as reference indexes for trend control, the demand of a building air conditioner can be greatly predicted in advance, and the purpose of supply according to the demand is achieved. The trend control can not only reduce unnecessary energy consumption waste, but also greatly improve the comfort of the building.
Along with the continuously accumulated operation data, from the operation data of each project under different conditions at different time intervals to the operation parameters of different buildings in the same area, the cloud intelligent control center can find an operation mode with the optimal overall energy efficiency ratio through mass data storage, analysis and comparison, and replace the original scheme in the system with the optimized scheme, so that the process of continuously self-updating is realized.
The cloud big data analysis center comprises a data receiving unit, a storage unit and an analysis processing unit; the PLC equipment control center comprises a central air-conditioning host control module, a water flow control module, a cooling system control module and a tail end control module; the tail end sensing equipment comprises a sensor and a control actuator, wherein the sensor and the control actuator are used for collecting data at the tail ends of the machine room and the air conditioner, and the sensor and the control actuator comprise a temperature sensor, a humidity sensor, a pressure sensor, a flow sensor, an electric energy meter and a valve actuator. The system architecture is shown in fig. 1.
The cloud intelligent control center consists of hardware and software. The hardware part is all electronic equipment for providing support for the cloud server and the big data, and the software mainly comprises a database, statistical logic, an intelligent algorithm and the like. The cloud intelligent control center is a central part of the whole control system and is in charge of logic judgment, analysis and feedback of the control system.
The PLC intelligent control center is a control execution part in the system, and the PLC intelligent control center executes an operation strategy of the cloud control center while realizing function control over the host, the water pump and the cooling tower, so that the operation of each device is effectively coordinated, and the energy efficiency ratio of the system is optimized.
The terminal sensing equipment comprises a plurality of sensor equipment, the operation of the host, the water pump, the cooling tower and the terminal equipment and the interior of the building are quantified, and the quantified relation is fed back to the decision layer on the upper layer.
In the embodiment of the invention, the central air-conditioning control system comprises a cloud intelligent control center, a local intelligent PLC control center and a terminal sensing device. The cloud big data analysis center comprises a data receiving unit, a storage unit and an analysis processing unit; the PLC equipment control center comprises a central air-conditioning host control module, a water flow control module, a cooling system control module and a tail end control module. The end sensing equipment comprises sensors for data acquisition at the end of the machine room and the air conditioner. The cloud intelligent control center collects relevant parameters of the central air conditioning system in each time period (divided into units) through the tail end sensing equipment, wherein the relevant parameters comprise indoor and outdoor dry and wet bulb temperatures and equipment operation parameters (voltage, current, temperature, pressure, flow and the like), big data collection and storage are formed, the real-time cold (heat) quantity, power consumption and energy efficiency ratio [1] (KW/RT) of the system operation are calculated, the stored operation big data are subjected to statistical analysis, the optimized operation mode of the central air conditioning system under each external condition and requirement is calculated through an intelligent algorithm, the optimized operation temperature, pressure and flow of the central air conditioning system are determined, each equipment is accurately controlled through the local intelligent PLC control center, and the optimal operation of each equipment of the central air conditioning system is controlled under each specific condition. The control method can analyze and calculate the optimized operation scheme according to the operation records accumulated by the central air-conditioning system, and match the optimized central air-conditioning operation scheme in advance aiming at the current situation and predicting the air-conditioning terminal requirement, thereby realizing real-time optimization, improving the system operation energy efficiency and reducing the energy consumption.
Finally, it is to be noted that: the above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (7)
1. A central air-conditioning control system, characterized by comprising:
the sensor equipment is used for acquiring the operation data of the central air conditioner;
the local intelligent PLC control center is used for acquiring the operation data acquired by the sensor equipment and forwarding the operation data to the cloud intelligent control center;
the cloud intelligent control center is used for receiving the equipment operation data, processing the operation data, generating a control instruction and sending the control instruction to the local intelligent PLC control center;
and the local intelligent PLC control center is used for controlling the central air conditioner according to the control instruction.
2. The central air conditioning control system of claim 1, wherein the sensor device is used to collect one or more of refrigeration host, chilled water pump, cooling tower, air conditioning unit, valves, water treatment, and usage data of the interior of the building.
3. The central air-conditioning control system of claim 1, wherein the cloud-based intelligent control center comprises a receiving module, a storage module and a data processing module;
the receiving module is used for receiving and reading the equipment operation data; respectively storing the equipment operation data to corresponding positions of a storage module through compiling and classifying;
the data processing module is used for calling the data in the storage module, analyzing and processing the data, and realizing data modeling, self-optimization and trend control.
4. The central air conditioning control system of claim 3, wherein the data modeling comprises:
performing data analysis and modeling according to the host energy efficiency, the chilled water pump energy efficiency, the cooling water pump energy efficiency and the cooling tower energy efficiency;
with the aim of minimizing the total energy consumption of the air conditioning system, the original independent and controllable factors are determined: the operation number and frequency of the chilled water pumps, the operation number and frequency of the cooling towers, the supply water temperature of the chilled water, and the outdoor temperature and humidity of the external factor environment;
determining the mathematical model relation of the total energy consumption of the system and the factors:
the total energy consumption of the system is equal to the host energy efficiency, the chilled water pump energy efficiency, the cooling water pump energy efficiency and the cooling tower energy efficiency, and is equal to f (the number of the chilled water pumps, the frequency, the number of the cooling tower, the chilled water supply temperature, the ambient outdoor temperature and the ambient outdoor humidity).
5. The central air conditioning control system of claim 4, wherein the establishing of the mathematical model relationship further comprises:
s1, establishing a database; storing and sorting operation data generated in the operation process of the air conditioning system into a cloud database;
s2, establishing a rule; and performing fuzzification processing in a cloud database through the collected data, and establishing a function mapping relation according to the established function model.
S3, reversely analyzing the optimal solution set; when a new operation starts, the most solution set is solved according to the established function mapping relation and fed back to the operation in the system.
S4, feedback correction and rolling optimization; the functional mapping is adjusted and optimized according to the new operational data generated.
6. The central air-conditioning control system according to claim 5, characterized in that the database is established;
the data acquisition is 1 group/minute, the accumulated data volume is more than or equal to 10000 groups of data, and the data acquisition comprises continuous operation working condition data.
7. The central air conditioning control system of claim 1, wherein the local intelligent PLC control center comprises a central air conditioning host control module, a water flow control module, a cooling system control module, and a terminal control module.
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| CN113569364A (en) * | 2021-06-12 | 2021-10-29 | 武汉所为科技有限公司 | Simulation training model for heating and ventilation cloud edge cooperative intelligent system |
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