CN113790516B - Global optimization energy-saving control method and system for central air-conditioning refrigeration station and electronic equipment - Google Patents
Global optimization energy-saving control method and system for central air-conditioning refrigeration station and electronic equipment Download PDFInfo
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Abstract
The invention relates to a global optimization energy-saving control method, a system and electronic equipment for a central air-conditioning refrigeration station, which comprises the following steps: the method comprises the steps of periodically obtaining historical data of each device of a central air-conditioning refrigeration station, preprocessing the historical data, and obtaining an operation data sample of each device; identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data sample of each device and the power calculation formula of each device to obtain a power model of each device; acquiring a sample point with minimum system global power in real time based on an event-driven optimization genetic algorithm to obtain optimization control parameters of each device; and adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device. The invention intelligently realizes the overall optimization energy-saving control of the central air-conditioning refrigeration station, simultaneously, the defects of long overall optimization calculation time, more optimization parameters and high-frequency adjustment are avoided by driving the optimization calculation based on event triggering, and the input cost of frequent operation of the system is reduced.
Description
Technical Field
The invention relates to the technical field of central air-conditioning refrigeration stations, in particular to a global optimization energy-saving control method and system for a central air-conditioning refrigeration station and electronic equipment.
Background
The building energy consumption currently accounts for 30 percent of the total energy consumption of the whole social activities, and in the whole building energy consumption, the energy consumption of the central air conditioner accounts for 40 to 50 percent, and the cold and heat sources of the central air conditioner consume 30 to 35 percent of the energy consumption. The refrigeration station is a heart supplied by a cold source of a building, the power consumption of the refrigeration station generally accounts for more than 30% of the total power consumption of the building, and the refrigeration station mainly comprises a water chilling unit, a freezing water pump, a cooling tower, a pipeline system, a control system and the like. The energy-saving effect of the cold station system can directly influence the energy consumption of the whole building.
The system load of the cold station is a variable which changes along with uncertainty factors such as personnel, climate and the like, and the optimal system operation working condition point, namely the working condition point with the minimum total energy consumption of system operation, can change under different load conditions. The set values of the operating parameters of the conventional unit are usually fixed values, and the set values cannot dynamically follow the change of the load due to calculation in the design stage or debugging experience of field personnel. In the group control of the central air-conditioning and refrigerating machine rooms adopted in the current common projects: 1) The central control station monitors the operation of equipment and cannot coordinate the operation of the system in real time; 2) Each device is controlled by independent control, and the integral operation energy conservation of the machine room cannot be considered. In fact, whether the energy-saving effect of the whole air-conditioning system is optimal or not is often achieved, and whether balance and matching are achieved between subsystems and equipment or not is often not determined whether a certain subsystem or equipment is optimized or not. Although some manufacturers adopt some optimization control modes to optimize the set values of the air supply temperature and the like, the optimization is limited to local optimization, the energy-saving effect of the frequency conversion technology cannot be fully exerted, and even the overall energy consumption of the system can be increased. In addition, global optimization is proposed at present, but the practicability and stability of the global optimization are not considered, so that the defects of long calculation time of the global optimization, multiple optimization parameters and high-frequency adjustment occur, and the problem of the global optimization cannot be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a global optimization energy-saving control method, a system and electronic equipment for a central air-conditioning refrigeration station, aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problem is as follows: a global optimization energy-saving control method for a central air-conditioning refrigeration station is constructed, and comprises the following steps:
the method comprises the steps of periodically obtaining historical data of each device of a central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device; each equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower;
identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data samples of each device and the power calculation formula of each device to obtain a power model of each device;
acquiring a sample point with minimum system overall power in real time based on an event-driven optimization genetic algorithm to obtain optimization control parameters of each device;
and adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device.
In the global optimization energy-saving control method for a central air-conditioning refrigeration station, the periodically obtaining historical data of each device of the central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device includes:
sampling data of the real-time data of each device every a preset time period, pushing the data forward for 30 days from the day before the sampling time, and sampling the data with a preset time interval as a sampling frequency to obtain historical data of each device;
and removing the vacancy value, the mutation value, the data with the equipment switch state of 0 and the continuous multiple-point unchanged data of the number of data cards from the historical data of each equipment to obtain an operation data sample of each equipment.
In the global optimization energy-saving control method for a central air-conditioning refrigeration station, the identifying parameters in the power calculation formulas of the devices by a preset method according to the operation data samples of the devices and the power calculation formulas of the devices to obtain the power models of the devices includes:
and identifying parameters in the power calculation formula of each device by adopting a least square method according to the operation data sample of each device and the power calculation formula of each device to obtain a power model of each device.
In the global optimization energy-saving control method for the central air-conditioning refrigeration station, a power calculation formula of each device comprises the following steps: a power calculation formula of the refrigerating unit, a power calculation formula of the refrigerating pump/cooling pump and a power calculation formula of the cooling tower;
the power calculation formula of the refrigerating unit is as follows:
in the formula, Q ch : actual refrigeration capacity, kW; p ch : actual power, kW; t is a unit of chws : freezing the effluent temperature at DEG C; t is cws1 : cooling return water temperature, DEG C; d is a radical of 1 、d 2 、d 3 、d 4 、d 5 Is a parameter to be identified;
the power calculation formula of the refrigerating pump/cooling pump is as follows:
in the formula, Q x : the water pump frequency is the flow rate at x Hz, and m3/h; px is the calculated power of the water pump, kW; h X : the calculated lift of the water pump, m; eta x : efficiency,%, calculated by the water pump; ρ g: defaults to 9.8;
the power calculation formula of the cooling tower is as follows:
in the formula, T cws2 : the temperature of cooling water out of the tower is lower than DEG C; t is a unit of wb : outdoor wet bulb temperature, deg.C; epsilon: calculating the heat exchange efficiency of the cooling tower,%; k: correcting the coefficient; m is a unit of w : cooling water flow rate, m3/h; w: actual electrical power, kW; c. C 1 、c 2 、c 3 、c 4 、c 5 Is the parameter to be identified.
In the global optimization energy-saving control method of the central air-conditioning refrigeration station, Q is x 、H X And η x Satisfies the following conditions:
H x ＝H X1 -H X2 ；
η x ＝(9.8(H X1 -H X2 )·Q X )/(3600·P X )；
in the formula, H X1 : the outlet pressure of the water pump is converted into water column m; h X2 : converting the inlet pressure of the water pump into water column m; h 0 : rated lift of the water pump, m; f. of 50 : the frequency of the water pump is defaulted to 50,Hz when the water pump is fully loaded; fx: actual frequency of the water pump, hz; q 0 -rated flow of the water pump, m3/h; a1, a2, a3, a4, b1, b2, b3, b4 are parameters to be identified.
In the global optimization energy-saving control method for a central air-conditioning refrigeration station, the obtaining of the sample point with the minimum system global power in real time based on the event-driven optimization genetic algorithm and the obtaining of the optimization control parameters of each device include:
determining an event trigger point;
acquiring on-off state signals of all equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point based on the event trigger point;
acquiring the system load and the outdoor wet bulb temperature of the central air-conditioning refrigeration station at the current moment of the event trigger point;
accumulating power models of started equipment in the equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point to obtain a global power model of the central air-conditioning refrigeration station;
determining the interval range of the operation parameters in the power model of each device;
and according to the global power model, the system load, the outdoor wet bulb temperature and the interval range of the operation parameters in the power model of each device, optimizing and calculating by adopting a genetic algorithm and outputting a sample point with the minimum global power of the system to obtain the optimized control parameters of each device.
The invention also provides a global optimization energy-saving control system of the central air-conditioning refrigeration station, which comprises the following components:
the data acquisition module is used for periodically acquiring historical data of each device of the central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device; the equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower;
the power model module is used for identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data samples of each device and the power calculation formula of each device to obtain a power model of each device;
the global optimizing module is used for acquiring a sample point with the minimum global power of the system in real time based on an event-driven optimized genetic algorithm to acquire optimized control parameters of each device;
and the adjusting module is used for adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device.
The present invention also provides an electronic device comprising: a memory and a processor;
the memory is used for storing a computer program;
the processor is used for executing the computer program to realize the global optimization energy-saving control method of the central air-conditioning refrigeration station.
The invention also provides a readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program causes the processor to process the steps of the global optimization energy-saving control method of the central air-conditioning refrigeration station.
The implementation of the global optimization energy-saving control method, the system and the electronic equipment of the central air-conditioning refrigeration station has the following beneficial effects: the method comprises the following steps: the method comprises the steps of periodically obtaining historical data of each device of a central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device; identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data sample of each device and the power calculation formula of each device to obtain a power model of each device; acquiring a sample point with minimum system overall power in real time based on an event-driven optimization genetic algorithm to obtain optimization control parameters of each device; and adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device. The invention intelligently realizes the global optimization energy-saving control of the central air-conditioning refrigeration station, simultaneously avoids the defects of long global optimization calculation time, more optimization parameters and high-frequency adjustment on the basis of event-triggered drive optimization calculation, and reduces the investment cost of frequent operation of the system.
Drawings
The invention will be further described with reference to the following drawings and examples, in which:
fig. 1 is a flowchart of a global optimization energy-saving control method for a central air-conditioning refrigeration station according to an embodiment of the present invention;
fig. 2 is a flowchart of a preferred implementation of a global optimization energy-saving control method for a central air-conditioning refrigeration station according to an embodiment of the present invention;
FIG. 3 is a data preprocessing flow chart of a global optimization energy-saving control method for a central air-conditioning refrigeration station according to an embodiment of the present invention;
FIG. 4 is a scattering diagram of the actual cooling main engine operating data in 2019 cooling season before preprocessing according to the present invention;
FIG. 5 is a time sequence scatter diagram of actual 2019 year cooling season operation data of the preprocessed refrigeration host;
FIGS. 6-9 are refrigeration host training error scatter plots;
FIG. 10 is a scatter diagram of external condition environment acquisition point partitioning for system global optimization calculation;
FIG. 11 is a comparison graph of optimal parameters and actual operating parameters given by global optimization of the system;
FIG. 12 is a comparison graph of the estimated optimization effect and the actual operation adjustment effect after the system global optimization energy-saving control.
Detailed Description
For a more clear understanding of the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a global optimization energy-saving control method for a central air-conditioning refrigeration station according to an embodiment of the present invention is provided.
As shown in fig. 1, the global optimization energy-saving control method for the central air-conditioning refrigeration station comprises the following steps:
and S10, periodically acquiring historical data of each device of the central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device. The equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower.
Optionally, the periodically obtaining historical data of each device of the central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device includes: the method comprises the steps that data sampling is conducted on real-time data of each device at regular intervals of a preset time period, the device is pushed forward for 30 days from the day before sampling time, and data sampling is conducted with a preset time interval as sampling frequency, so that historical data of each device are obtained; and removing the blank value, the mutation value, the data with the equipment switch state of 0 and the continuous multiple-point unchanged data of the number of data cards from the historical data of each equipment to obtain an operation data sample of each equipment. Alternatively, the preset time interval may be 10 minutes.
Specifically, data sampling is performed on real-time data of each device at regular intervals of a preset time period (such as every week, every month, and the like), and sampling is performed at intervals of 10 minutes by advancing 30 days with the latest data acquisition time (i.e., sampling time) as a reference, so as to serve as original data, i.e., historical data of each device.
Wherein, the historical data of each device comprises: each refrigeration host (the refrigeration unit comprises a plurality of refrigeration hosts): the chilled water outlet temperature, the cooling return water temperature, the actual refrigerating capacity and the actual electric power, wherein each refrigerating pump or cooling pump is as follows: water pump inlet pressure, water pump outlet pressure, flow, frequency, actual electric power, each cooling tower: cooling water inlet temperature, cooling water outlet temperature, outdoor wet bulb temperature, actual electric power and cooling water flow.
And S20, identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data samples of each device and the power calculation formula of each device to obtain a power model of each device.
Optionally, identifying parameters in the power calculation formula of each device by using a preset method according to the operation data sample of each device and the power calculation formula of each device, and obtaining the power model of each device includes: and identifying parameters in the power calculation formula of each device by adopting a least square method according to the operation data sample of each device and the power calculation formula of each device to obtain a power model of each device.
And S30, acquiring a sample point with minimum system global power in real time based on an event-driven optimization genetic algorithm to acquire optimization control parameters of each device.
And S40, adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device.
The method provided by the embodiment of the invention is mainly applied to the global optimization energy-saving control process of the central air-conditioning refrigeration station, for example: the conventional refrigeration stations of the central air-conditioning system, namely a refrigeration host, a chilled water pump, a cooling water pump and a cooling tower primary pump system, regularly acquire the operation data of each device, perform identification and parameter acquisition to obtain a power model of each device, give optimal control operation parameters based on event-driven system global optimization, and realize global optimization of the refrigeration station system. The method is not limited to a conventional refrigeration station, but also can be applied to the global optimization of systems such as a cold accumulation system, a secondary pump system and the like, and has certain universality.
In order to enable the effect of the genetic optimization algorithm to be more accurate and the system to be more stable, the method provided by the embodiment of the invention provides the system refrigerating capacity and the wet bulb temperature for determining the event trigger point based on the established event rule and the equipment starting condition, simplifies the parameters and avoids the defects of long calculation time, multiple optimized parameters and high-frequency suboptimal in the conventional global optimization.
According to the method, historical real-time operation data of each device of the refrigeration station are regularly acquired and preprocessed, so that an operation data sample is obtained; applying the operation data sample to power calculation formulas corresponding to a refrigerating unit, a freezing pump, a cooling pump and cooling tower equipment, and identifying parameters in the formulas by a least square method to obtain a power model of each equipment; and finally, on the basis of the equipment power model, searching a sample point with the minimum global power in real time based on an event-driven optimization genetic algorithm SGA to obtain the operation parameters of each piece of equipment. The method comprises the steps of regularly collecting operation data of the equipment in a short period of time at preset intervals (such as every week, every month and the like), identifying parameters in a formula by adopting a least square method based on an equipment power calculation formula to obtain a power model, and finally searching a sample point with minimum global power in real time based on an event-driven optimization genetic algorithm SGA to obtain operation parameters of each equipment. The intelligent realization is to the overall optimization energy-saving control of central air conditioning refrigeration station, has solved the drawback that each equipment independent control of traditional refrigeration station can't accomplish overall energy-saving, has avoided the shortcoming that current overall optimization calculation time is long, optimization parameter is many, high frequency suboptimum simultaneously, has improved overall optimization's promptness and accuracy and practical level, reduces the input of the manpower, material resources that the frequent operation of system brought.
Referring to fig. 2, it is a flowchart of a preferred embodiment of the global optimization energy-saving control method for a central air-conditioning refrigeration station provided by the present invention.
As shown in fig. 2, this embodiment includes the following steps:
step S201, historical data of each device is collected regularly.
And step S202, preprocessing historical data.
And step S203, obtaining a running data sample.
And step S204, calculating by adopting a power calculation formula of each device. Wherein, the power calculation formula comprises: a power calculation formula of the refrigerating unit, a power calculation formula of the refrigerating pump/cooling pump and a power calculation formula of the cooling tower.
And S205, identifying the parameters to be identified in the power calculation formula of each device by adopting a least square method.
And step S206, obtaining a power model of each device.
Step S207, determining the event trigger point, and acquiring the on-off state signals of each device of the central air-conditioning refrigeration station at the current moment of the event trigger point based on the event trigger point.
And S208, acquiring the system load and the outdoor wet bulb temperature of the central air-conditioning refrigeration station at the current moment of the event trigger point.
And S209, accumulating the power models of the started equipment in the equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point to obtain a global power model of the central air-conditioning refrigeration station.
And step S210, determining the interval range of the operation parameters in the power model of each device.
And S211, performing optimization calculation by adopting a genetic algorithm according to the global power model, the system load, the outdoor wet bulb temperature and the interval range of the operation parameters in the power model of each device, and outputting a sample point with the minimum global power of the system to obtain the optimization control parameters of each device.
Further, as shown in fig. 3, after obtaining the history data of each device, the data needs to be preprocessed.
As shown in fig. 3, preprocessing the data includes the following steps:
step S301, acquiring historical data of each device.
And step S302, eliminating data of which the equipment is not started.
And step S303, eliminating data with the vacancy value.
And step S304, rejecting the data with mutation.
And step S305, eliminating the data with the number of the cards.
And S306, obtaining operation data samples of each device.
Alternatively, the data for mutations can be understood as: rejecting data for actual electrical power >1.05 x rated power of the devices for each device; the temperature of the outlet water of the refrigeration host is abnormally large or small (for example, t0 is greater than 20 or t0 is less than 5); the temperature of the cooling return water is abnormally large or small (for example, t1 is greater than 40 or t1 is less than 20); water pump frequency is abnormally large or small (f 0>55 or f0< 25), etc.; and eliminating data with the difference of 10 times (for example, T0/T1 is more than 10 and T2/T1 is more than 10) between the values of the previous time point and the next time point.
The data of the number of cards can be understood as: the point that the actual electric power data is completely the same before and after the moment, namely the card number; the cooling return water temperature and the host machine outlet water temperature are identical in continuous 6 point numerical values, namely the card number.
Optionally, the global optimization energy-saving control method for the central air-conditioning refrigeration station can be implemented on a Python platform, wherein Python is an object-oriented and interpreted computer programming language, has an efficient high-level data structure, and can be used for object-oriented programming in a simple and efficient manner. The grammar is simple, elegant and powerful in interpretability, so that the grammar becomes an ideal language in many fields.
In the embodiment of the invention, the power calculation formula of the refrigerating unit is as follows:
in the formula, Q ch : actual refrigeration capacity, kW; p ch : actual power, kW; t is chws : freezing the effluent temperature at DEG C; t is cws1 : cooling return water temperature, DEG C; d 1 、d 2 、d 3 、d 4 、d 5 Is the parameter to be identified.
Specifically, according to the refrigeration host in the operation data sample: the outlet water temperature of the chilled water, the return water temperature of the cooling water, the actual refrigerating capacity and the actual electric power are brought into the formula (1), and the least square method is adopted to carry out the calculation on the d 1 、d 2 、d 3 、d 4 、d 5 And identifying to obtain a power model of the refrigerating unit.
The power calculation formula for the freeze/cooling pump is:
in the formula, Q x : the flow rate of the water pump is m3/h when the frequency of the water pump is x Hz; px is the calculated power of the water pump, kW; h X : the calculated lift of the water pump, m; eta x : efficiency,%, calculated by the water pump; ρ g: defaults to 9.8.
Q x 、H X And η x Satisfies the following conditions:
H x ＝H X1 -H X2 (2)。
η x ＝(9.8(H X1 -H X2 )·Q X )/(3600·P X ) (3)。
in the formula, H X1 : the outlet pressure of the water pump is converted into water column m; h X2 : converting the inlet pressure of the water pump into water column m; h 0 : rated lift of the water pump, m; f. of 50 : the frequency of the water pump is defaulted to 50,Hz when the water pump is fully loaded; fx: actual frequency of the water pump, hz; q 0 -rated flow of the water pump, m3/h; a1, a2, a3, a4, b1, b2, b3, b4 are parameters to be identified.
Specifically, according to the operation data samples: the water pump inlet pressure and the water pump outlet pressure of the freezing pump/cooling pump are combined with a formula (2) to calculate the lift data, and according to the operation data samples: calculating the calculation efficiency of the water pump by combining the water pump inlet pressure, the water pump outlet pressure, the flow and the actual electric power with a formula (3), and finally calculating the calculated lift H x And efficiency η x And actual frequency f of the water pump x Sum flow rate Q x And substituting the formula (4) and the formula (5), and identifying a1, a2, a3, a4, b1, b2, b3 and b4 by adopting a least square method to obtain a power model of the refrigerating pump/cooling pump.
The power calculation formula of the cooling tower is as follows:
in the formula, T cws2 : the temperature of cooling water out of the tower is lower than the temperature of the cooling water; t is a unit of wb : outdoor wet bulb temperature, deg.C; epsilon: calculating the heat exchange efficiency of the cooling tower,%; k: correcting the coefficient; m is a unit of w : cooling water flow rate, m3/h; w: actual electrical power, kW; c. C 1 、c 2 、c 3 、c 4 、c 5 Is the parameter to be identified.
Further, the calculated heat exchange efficiency of the cooling tower meets the following requirements:
specifically, according to the cooling water inlet temperature, the cooling water outlet temperature and the outdoor wet bulb temperature in the operation data sample, the heat exchange efficiency of the cooling tower can be calculated by combining the formula (7), and finally, the heat exchange efficiency data calculated by the formula (7), the outdoor wet bulb temperature, the cooling water outlet temperature in the operation data sample, the actual electric power and the cooling water flow are brought into the formula (8), and the least square method is adopted for identification to obtain the power model of the cooling tower.
In the embodiment of the invention, the principle of parameter identification by the least square method is as follows:
let (X, y) be a pair of observations, the data matrix being defined as X = [ X = [ ] 1 ，X 2 ，...，X d ] T The following theoretical function is satisfied:
y＝f(X，W) (9)。
wherein W = [ W = 1 ，W 2 ，...，W d ] T Are parameters to be determined. To find the optimum estimate of the parameter W of the y = f (X, W) function, i.e. the parameter W solving the minimum i (i =1, 2.. N), for a given n groups (typically n)>d) Observed data (X) i ,y i ) (i =1, 2.. N), solving an objective function:
for the unconstrained optimization problem, the general form of the least squares method is:
wherein L is i (X) (i =1,2.. N) is referred to as a residual function.
Furthermore, in the embodiment of the invention, in order to find out the optimal operation parameters of the operation of each device of the system, the event-driven-based global power model of the trigger point and the system working condition data of the system are obtained to obtain the global power model of the trigger point. The method comprises the following specific steps:
(1) An event trigger point is determined.
Specifically, real-time data of the operation of the refrigeration station system is monitored, and a state point with a significant change of the system operation condition is determined based on the real-time data, wherein the state point with the significant change is an event trigger point. For example, the set triggering rules are: a refrigeration main machine: the number of the refrigeration hosts is changed > =1, and then the event trigger points of the refrigeration hosts are as follows: the 5 th point after the number of the stations is changed; outdoor wet bulb temperature: the outdoor wet bulb temperature rises or falls by 0.5 ℃ at three continuous monitoring points, and the event trigger points of the outdoor wet bulb temperature are as follows: point 3; a freezing pump: the freezing outlet water temperature rises or falls by 0.3 ℃ at three continuous monitoring points, and the event trigger points of the freezing pump are as follows: and (3) point.
Wherein, the real-time data of the operation of the refrigeration station system comprises but is not limited to: the number of the refrigeration host machines, the outdoor wet bulb temperature, the system load, the system freezing water outlet temperature and the starting data of each device.
(2) And acquiring on-off state signals of all equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point based on the event trigger point. Specifically, on-off state signals of the refrigerating unit, the refrigerating pump, the cooling pump and the cooling tower at the current moment of the event trigger point are obtained. The control strategy module can be combined with the refrigeration station group control system to control the on-off state signal acquisition of each device output by the strategy module.
(3) And acquiring the system load and the outdoor wet bulb temperature of the central air-conditioning refrigeration station at the current moment of the event trigger point.
(4) And accumulating power models of started equipment in the equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point to obtain a global power model of the central air-conditioning refrigeration station. In other words, in the equipment started at the current moment, the power models of the started equipment are accumulated to obtain the accumulated sum of the power models of the started equipment, and the accumulated sum is the global power model of the central air-conditioning refrigeration station.
(5) And determining the interval range of the operation parameters in the power model of each device.
Optionally, the interval range of the power model operating parameter of each device may be determined according to the maximum value and the minimum value of the given operating parameter. For example, the range of the cooling main unit is: 5< = host machine outlet water temperature < =20, 20< = cooling backwater temperature < =40, 30< = chilled water pump frequency < =50, 30< = cooling water pump frequency < =50, and 30< = cooling tower fan frequency < =50.
(6) And according to the global power model, the system load, the outdoor wet bulb temperature and the interval range of the operation parameters in the power model of each device, optimizing and calculating by adopting a genetic algorithm, and outputting a sample point with the minimum global power of the system to obtain the optimized control parameters of each device.
The genetic algorithm can be used for solving the extreme value of a function, is a random search algorithm based on a biological genetic mechanism, namely a biological evolution (natural elimination, crossing, mutation and the like) phenomenon, carries out search and evolution by simulating a biological evolution process through a computer, finally seeks an optimal solution, searches all running sample points based on a global power mathematical model, finds a sample meeting the minimum global power, namely global optimization, and has the following main mathematical principle:
1) Initialization: setting an evolution algebra counter T =0, setting a maximum evolution algebra T, and randomly generating M individuals as an initial population P (0); 2) Individual evaluation: calculating the fitness of each individual in the population P (t); 3) Selecting and operating: acting a selection operator on the population; 4) And (3) cross operation: applying a crossover operator to the population; 5) And (3) mutation operation: acting mutation operators on the population; the group P (t) is subjected to selection, intersection and mutation operation to obtain a next generation group P (t + 1); 6) And (4) judging termination conditions: and if T = T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and terminating the calculation.
The following describes a global optimization energy-saving control method for a central air-conditioning refrigeration station, provided by the invention, by using a specific example.
For example, 4 refrigeration main machines 2813.6kW and 4 chilled water pumps 350m are taken as operation monitoring platforms of a central air-conditioning system refrigeration station of a certain company building 3 Per, 4 cooling water pumps 350m 3 And h, 180m & lt 3 & gt/h of 16 cooling towers. The method is verified based on the collected historical data. Collecting operation data of each device of the refrigeration station for supplying cold for 10 minutes in XX year, performing necessary data preprocessing, namely vacancy value processing, processing mutation data to serve as reference data of the training power model and global optimization, and improving data quality to perform important basic work of subsequent data analysis, for example: taking a certain refrigeration host as an example, the data before and after preprocessing are shown in fig. 4-5, fig. 4 is a time sequence scatter diagram of data which is operated by 10 minutes in the cold season in XX year of the actual electric power of the host before preprocessing, and fig. 5 is a time sequence scatter diagram of data which is operated by 10 minutes in the cold season in XX year of the actual electric power of the host after preprocessing.
Training data sample building equipment power model
Selecting a data sample preprocessed by each refrigeration host, a refrigeration water pump, a cooling water pump and cooling tower equipment of a refrigeration station, bringing the data sample into a power calculation formula of each corresponding refrigeration unit, refrigeration pump, cooling pump and cooling tower equipment, applying the operation data sample of each equipment to the power calculation formula, identifying parameters in the model by a least square method, and obtaining a power model of each equipment, namely building the power model for 4 refrigeration hosts, 4 refrigeration water pumps, 4 cooling water pumps and 16 cooling tower training data.
Taking the identification model parameters of the refrigeration hosts as an example, selecting the operation data of the XX year cooling season refrigerating capacity, the host outlet water temperature, the cooling return water temperature and the actual electric power, carrying out preprocessing, and then carrying out training by taking the operation data into a refrigeration host calculation power formula to obtain a power model trained by each host as follows, wherein training error scatter diagrams are shown in fig. 6 to 9.
The refrigeration main machine 1: fitting error R 2 ＝0.85；
The refrigeration main machine 2: fitting error R 2 ＝0.83；
The refrigeration main machine 3: fitting error R 2 ＝0.86；
The refrigeration main machine 4: fitting error R 2 ＝0.84；
The chilled water pump, the cooling water pump and the cooling water tower are modeled according to the training data, and are not explained one by one.
Event-driven genetic algorithm SGA global optimization:
and (3) according to the total system cold quantity and the outdoor wet bulb temperature at the triggering moment and the interval range of the operation parameters (for example, 5< = host outlet water temperature < =20, 20< = cooling return water temperature < =40, 30< = chilled water pump frequency < =50, 30< = cooling water pump frequency < =50 and 30< = cooling tower fan frequency < = 50), outputting a sample point with the minimum global power, namely an extreme value, and obtaining the operation parameters of each starting device.
Setting the triggering rules of the refrigerating station events (the number change > =1 of the refrigerating machines, the triggering time is the 5 th point after the number change, the temperature of the outdoor wet bulb continuously rises or falls by 0.5 ℃, the triggering time is the 3 rd point, the temperature of the frozen water continuously rises or falls by 0.3 ℃, and the triggering time is the 3 rd point). Counting data of all trigger time interval intervals (for example, 6-point trigger, 8-point trigger, and collecting working condition data in 6-8-point period), and dividing external working condition environment parameters of the system into multiple sections according to different data gradients, wherein the environmental parameters of the collection points are shown in FIG. 10; it can be seen that there are mainly 8 operating conditions. Global optimization is performed by adopting a genetic algorithm based on the 8 different working conditions, and comparison between the obtained optimized parameters and actual parameters is shown in FIG. 11; in addition, compared with the estimated effect and the actual manual adjustment effect of the operation of the optimized parameters, as shown in fig. 12, the total energy saving rate of the method adopted in the XX year cooling season reaches 14.7 percent through calculation.
The method comprises the steps of regularly collecting operation data of equipment in a short period of time at preset intervals, identifying parameters in a formula based on an equipment power calculation formula least square method to obtain a power model, and finally searching a sample point with minimum overall power in real time by using an event-driven optimization genetic algorithm SGA to obtain operation parameters of each equipment. The intelligent realization is to the overall optimization energy-saving control of central air conditioning refrigeration station, has solved the drawback that each equipment independent control of traditional refrigeration station can't accomplish overall energy-saving, has avoided the shortcoming that current overall optimization calculation time is long, optimization parameter is many, high frequency suboptimum simultaneously, has improved overall optimization's promptness and accuracy and practical level, reduces the input of the manpower, material resources that the frequent operation of system brought.
Furthermore, compared with a global optimization energy-saving control method of a refrigeration station of a central air-conditioning system, the intelligent method based on event-driven genetic algorithm global optimization is adopted, so that the parameters needing to be optimized are reduced, the defects of long calculation time, more optimized parameters and high-frequency suboptimal performance of the conventional global optimization are avoided, and the system is more stable; meanwhile, power models of various devices are established based on actual operation data of the device physical characteristics in the minute level, real-time data of the devices are sampled at intervals of a preset time period, data training can be conducted on line or on line for 30 days, the power models of the devices are output, and the real and real-time characteristics of the operation of the devices are guaranteed. The method provides reliable experience reference for energy-saving control, equipment operation characteristics and equipment fault diagnosis of the refrigeration station of the central air-conditioning system.
Furthermore, the invention has strong universality and wide engineering applicability. The invention is suitable for global optimization energy-saving control of a refrigeration station of a central air-conditioning system, is not only suitable for conventional refrigeration stations, namely, refrigeration stations of a refrigeration host, a freezing water pump, a cooling water pump and a cooling tower primary pump system, but also suitable for central air-conditioning system refrigeration stations of a cold accumulation system, a secondary pump system and the like. In addition, the invention not only carries out global optimization on the data which is not limited to the on-line real-time data, but also can be applied to historical real-time data to give optimized operating parameters, and the optimized operating parameters are compared with actual operating parameters to calculate the energy saving amount. In addition, the method is also suitable for the global optimization energy-saving control of a plurality of refrigeration stations at a project level, and is not limited to the global optimization energy-saving control of a single refrigeration station.
The invention also provides a global optimization energy-saving control system of the central air-conditioning refrigeration station, which comprises the following components:
the data acquisition module is used for periodically acquiring historical data of each device of the central air-conditioning refrigeration station, preprocessing the historical data and acquiring an operation data sample of each device; the equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower.
And the power model module is used for identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data samples of each device and the power calculation formula of each device to obtain the power model of each device.
And the global optimizing module is used for acquiring the sample point with the minimum global power of the system in real time based on the event-driven optimization genetic algorithm to acquire the optimization control parameters of each device.
And the adjusting module is used for adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device.
The present invention also provides an electronic device comprising: a memory and a processor;
the memory is used for storing a computer program;
the processor is used for executing a computer program to realize the global optimization energy-saving control method of the central air-conditioning refrigeration station disclosed by the embodiment of the invention.
The invention also provides a readable storage medium, wherein a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the processor is enabled to process the steps of the global optimization energy-saving control method for the central air-conditioning refrigeration station disclosed by the embodiment of the invention.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.
Claims (8)
1. A global optimization energy-saving control method for a central air-conditioning refrigeration station is characterized by comprising the following steps:
the method comprises the steps of periodically obtaining historical data of each device of a central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device; each equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower;
identifying parameters in the power calculation formula of each device by adopting a preset method according to the operation data samples of each device and the power calculation formula of each device to obtain a power model of each device;
acquiring a sample point with minimum system global power in real time based on an event-driven optimization genetic algorithm to obtain optimization control parameters of each device;
adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device;
the method for acquiring the sample point with the minimum system global power in real time based on the event-driven optimization genetic algorithm comprises the following steps of:
determining an event trigger point;
acquiring on-off state signals of all equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point based on the event trigger point;
acquiring the system load and the outdoor wet bulb temperature of the central air-conditioning refrigeration station at the current moment of the event trigger point;
accumulating power models of started equipment in the equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point to obtain a global power model of the central air-conditioning refrigeration station;
determining the interval range of the operation parameters in the power model of each device;
and according to the global power model, the system load, the outdoor wet bulb temperature and the interval range of the operation parameters in the power model of each device, optimizing and calculating by adopting a genetic algorithm and outputting a sample point with the minimum global power of the system to obtain the optimized control parameters of each device.
2. The global optimization energy-saving control method for the central air-conditioning refrigeration station as claimed in claim 1, wherein the periodically obtaining historical data of each device of the central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device comprises:
sampling data of the real-time data of each device at intervals of a preset time period, pushing the data forward for 30 days from the previous day of the sampling time, and sampling the data by taking a preset time interval as a sampling frequency to obtain historical data of each device;
and removing the vacancy value, the mutation value, the data with the equipment switch state of 0 and the continuous multiple-point unchanged data of the number of data cards from the historical data of each equipment to obtain an operation data sample of each equipment.
3. The method for globally optimizing energy-saving control of a central air-conditioning refrigeration station according to claim 1, wherein the identifying parameters in the power calculation formula of each device by using a preset method according to the operation data samples of each device and the power calculation formula of each device, and the obtaining of the power model of each device comprises:
and identifying parameters in the power calculation formula of each device by adopting a least square method according to the operation data sample of each device and the power calculation formula of each device to obtain a power model of each device.
4. The global optimization energy-saving control method for the central air-conditioning refrigeration station as claimed in claim 3, wherein the power calculation formula of each device comprises: a power calculation formula of the refrigerating unit, a power calculation formula of a refrigerating pump/cooling pump and a power calculation formula of a cooling tower;
the power calculation formula of the refrigerating unit is as follows:
in the formula, Q ch : actual refrigeration capacity, kW; p ch : actual power, kW; t is a unit of chws : freezing the effluent temperature at DEG C; t is a unit of cws1 : cooling return water temperature, DEG C; d is a radical of 1 、d 2 、d 3 、d 4 、d 5 Is a parameter to be identified;
the power calculation formula of the refrigerating pump/cooling pump is as follows:
in the formula, Q x : the flow rate of the water pump is m3/h when the frequency of the water pump is x Hz; px is the calculated power of the water pump, kW; h X : the calculated lift of the water pump, m; eta x : efficiency,%, calculated by the water pump; ρ g: defaults to 9.8;
the power calculation formula of the cooling tower is as follows:
in the formula, T cws2 : the temperature of cooling water out of the tower is lower than the temperature of the cooling water; t is a unit of wb : outdoor wet bulb temperature, deg.C; epsilon: calculating the heat exchange efficiency of the cooling tower,%; k: a correction factor; m is w : cooling water flow rate, m3/h; w: actual electrical power, kW; c. C 1 、c 2 、c 3 、c 4 、c 5 Is the parameter to be identified.
5. The global optimization energy-saving control method for central air-conditioning refrigeration station according to claim 4, wherein Q is x 、H X And η x Satisfies the following conditions:
H x ＝H X1 -H X2 ；
η x ＝(9.8(H X1 -H X2 )·Q X )/(3600·P X )；
in the formula, H X1 : the outlet pressure of the water pump is converted into water column m; h X2 : converting the inlet pressure of the water pump into water column m; h 0 : rated lift of the water pump, m; f. of 50 : the frequency of the water pump is defaulted to 50,Hz when the water pump is fully loaded; fx: actual frequency of the water pump, hz; q 0 -rated flow of the water pump, m3/h; a1, a2, a3, a4, b1, b2, b3, b4 are parameters to be identified.
6. A global optimization energy-saving control system for a central air-conditioning refrigeration station is characterized by comprising the following components:
the system comprises an acquisition data module, a data processing module and a data processing module, wherein the acquisition data module is used for periodically acquiring historical data of each device of a central air-conditioning refrigeration station, and preprocessing the historical data to obtain an operation data sample of each device; the equipment of the central air-conditioning refrigeration station comprises: any one or more of a refrigerating unit, a freezing pump, a cooling pump and a cooling tower;
the power model module is used for identifying parameters in the power calculation formulas of the equipment by adopting a preset method according to the operation data samples of the equipment and the power calculation formulas of the equipment to obtain power models of the equipment;
the global optimizing module is used for acquiring a sample point with the minimum global power of the system in real time based on an event-driven optimized genetic algorithm to acquire optimized control parameters of each device;
the adjusting module is used for adjusting the current operation parameters of each device of the central air-conditioning refrigeration station according to the optimized control parameters of each device;
the method for acquiring the sample point with the minimum system global power in real time based on the event-driven optimization genetic algorithm comprises the following steps of:
determining an event trigger point;
acquiring on-off state signals of all equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point based on the event trigger point;
acquiring the system load and the outdoor wet bulb temperature of the central air-conditioning refrigeration station at the current moment of the event trigger point;
accumulating power models of started equipment in the equipment of the central air-conditioning refrigeration station at the current moment of the event trigger point to obtain a global power model of the central air-conditioning refrigeration station;
determining the interval range of the operation parameters in the power model of each device;
and according to the global power model, the system load, the outdoor wet bulb temperature and the interval range of the operation parameters in the power model of each device, optimizing and calculating by adopting a genetic algorithm and outputting a sample point with the minimum global power of the system to obtain the optimized control parameters of each device.
7. An electronic device, comprising: a memory and a processor;
the memory is used for storing a computer program;
the processor is used for executing the computer program to realize the global optimization energy-saving control method of the central air-conditioning refrigeration station as set forth in any one of claims 1-5.
8. A readable storage medium having stored thereon a computer program that, when executed by a processor, causes the processor to process the steps of the method for globally optimizing energy savings control for a central air conditioning refrigeration station as set forth in any one of claims 1-5.
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