Energy-saving control system for refrigeration unit of data center

Abstract

The invention discloses an energy-saving control system for a refrigeration unit of a data center, belongs to the technical field of refrigeration energy conservation of the data center, and aims to solve the technical problems of how to effectively reduce energy consumption and improve the refrigeration efficiency of the data center. According to the technical scheme, the system comprises a hot standby power supply, an industrial gateway, an industrial switch, a group control CPU controller, a cold and heat meter, a flow meter, a plurality of refrigeration unit controllers and sensor acquisition modules corresponding to the refrigeration unit controllers, and the hot standby power supply is electrically connected with the industrial gateway; the group control CPU controller is electrically connected with the hot standby power supply, the industrial switch and the refrigeration unit controller respectively, the refrigeration unit controller is electrically connected with the industrial switch and the sensor acquisition module respectively, the cold and heat meter and the flow meter are electrically connected with the group control CPU controller respectively, and the group control CPU controller monitors the refrigeration unit controller. Start-stop control, operation detection, fault alarm and sensor monitoring analysis of the water chilling unit and the valve water pump are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data center refrigeration energy saving, in particular to an energy saving control system for a data center refrigeration unit. Background Art

[0002] The data center refrigeration unit is a key component to ensure the stable operation of the data center. Its main function is to provide a suitable cooling environment for the data center to prevent performance degradation or failure caused by overheating of the equipment. The main components of the data center refrigeration unit include compressors, condensers, evaporators, expansion valves, water pumps, cooling towers, etc. Its working principle is mainly to use refrigerants or water and other refrigerants to circulate in the system and achieve cooling effects through heat exchange. For example, in an air-cooled direct expansion system, the refrigerant absorbs the heat of the computer room in the evaporator and boils and vaporizes into steam, which is then compressed into high-pressure and high-temperature gas by the compressor and sent to the outdoor condenser to condense into liquid, thereby releasing heat.

[0003] However, the existing data center cooling units lack intelligent control and energy-saving control, which will cause energy waste. Therefore, how to effectively reduce energy consumption and improve the cooling efficiency of data centers is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The technical task of the present invention is to provide an energy-saving control system for a data center refrigeration unit to solve the problem of how to effectively reduce energy consumption and improve the refrigeration efficiency of the data center.

[0005] The technical task of the present invention is achieved in the following way: a data center refrigeration unit energy-saving control system, the system includes a hot standby power supply, an industrial gateway, an industrial switch, a group control CPU controller, a heat and cold meter, a flow meter, multiple refrigeration unit controllers and a sensor acquisition module arranged corresponding to the refrigeration unit controller. The hot standby power supply is electrically connected to the industrial gateway, the group control CPU controller is electrically connected to the hot standby power supply, the industrial switch and the refrigeration unit controller respectively, the refrigeration unit controller is electrically connected to the industrial switch and the sensor acquisition module respectively, the heat and cold meter and the flow meter are electrically connected to the group control CPU controller respectively, the group control CPU controller monitors the refrigeration unit controller to realize start and stop control, operation detection, fault alarm and sensor monitoring and analysis of the chiller and valve water pump; the group control CPU controller monitors the refrigeration unit controller in real time through a real-time heartbeat signal.

[0006] Preferably, the heartbeat sending driver of the group control CPU controller and the heartbeat receiving driver of the refrigeration unit controller send heartbeat signals to each other; the refrigeration unit controller is respectively connected to the water pipe temperature sensor, the water pipe temperature sensor and the water valve arranged on the main chilled water line.

[0007] Preferably, the system further comprises a control cabinet unit adopting a modular design, wherein the control cabinet unit comprises a main control module, a communication module, a data acquisition module and a display module;

[0008] Among them, the main control module is used for data processing and logic control, and establishes a connection with the remote server through the communication module to realize remote monitoring and control functions;

[0009] The data acquisition module is used to collect the operating data of each refrigeration unit in the data center. The operating data of each refrigeration unit includes temperature, humidity and energy consumption;

[0010] The display modules are used for local display and operation, respectively, to facilitate real-time monitoring and debugging by on-site personnel.

[0011] Preferably, the control cabinet unit dynamically adjusts the status and parameters of each refrigeration unit according to the actual load of the refrigeration room and the external environmental conditions, and integrates the energy-saving algorithm to achieve the optimal control strategy, as follows:

[0012] Support redundant switching: The group control CPU controller and the refrigeration unit controller observe each other's status through hard-wired heartbeat signals. If one refrigeration unit fails, another refrigeration unit will be started immediately to ensure the supply of cold capacity.

[0013] Support mode switching: The control cabinet unit divides the cooling mode into cooling mode, pre-cooling mode and self-cooling mode; the mode switching is comprehensively determined by the refrigeration unit controller based on the outdoor air wet bulb temperature and stability, the load of the cooling tower fan unit, and the operating conditions of the chiller and plate heat exchanger;

[0014] Supports refrigeration unit addition and subtraction: the control cabinet unit automatically increases or decreases the number of refrigeration units in operation according to the optimal load factor of the chiller; the group control CUP controller monitors the temperature on the supply and return water pipes and the flow sensor on the water supply pipe, and calculates the terminal required cooling load, while monitoring the rated cooling capacity signal sent by the refrigeration unit controller. When the calculated load reaches the preset value of the rated capacity ratio of the operation, the group control CUP controller will increase and open the next group of refrigeration units; when the cooling load drops below the preset value of the rated capacity ratio of the operation, the group control CUP controller will gradually shut down the refrigeration units.

[0015] Preferably, the energy-saving algorithm is to determine the optimal control strategy by setting the outlet water temperature of the refrigeration pump, the frequency of the refrigeration pump, the frequency of the cooling pump and the frequency of the cooling tower fan, input the refrigeration capacity requirement and the outdoor environment value, simulate the refrigeration effect and power consumption internally, and output the optimal control algorithm, including the chiller frequency, the water supply temperature, the refrigeration pump cooling pump frequency, and the cooling tower setting value;

[0016] Among them, the energy-saving algorithm formula is:

[0017] Preferably, the energy-saving algorithm uses a genetic algorithm to optimize the group control parameters of the data center refrigeration unit, and sets the parameters of the population size, number of iterations, crossover probability, and mutation probability according to the cooling capacity or temperature, and then gives the set values ​​of the chiller pump water outlet, frequency, cooling pump, and cooling tower; the details are as follows:

[0018] Create an initial population;

[0019] Obtain the fitness function based on the energy consumption demand of the data center cooling unit: obtain the energy consumption of the data center cooling unit, obtain the energy consumption value based on the SVM, and calculate the cooling effect;

[0020] Calculation function for simulating refrigeration effect: obtain the refrigeration capacity calculation formula based on SVM, and determine whether the chiller pump outlet water temperature setting value, chiller pump frequency, cooling pump frequency, and cooling tower fan are related to the refrigeration capacity based on the temperature difference;

[0021] The inverse of the fitness numpy array is taken through selection, crossover and mutation operations to reduce energy consumption;

[0022] Get the final population optimized by genetic algorithm as well as the best individual and fitness;

[0023] Run the genetic algorithm and plot the best fitness as a function of the number of iterations;

[0024] Get the optimal parameter combination and draw the best fitness change.

[0025] More preferably, the hot standby power supply selects the main municipal power supply and the standby UPS power supply, or the hot standby power supply selects the main UPS power supply and the standby UPS power supply.

[0026] Preferably, the display module adopts a touch screen.

[0027] The data center refrigeration unit energy-saving control system of the present invention has the following advantages:

[0028] (1) The group control CPU controller and each refrigeration unit controller of the present invention are completely independent at the network level, and the refrigeration unit controllers back up each other. If any one controller fails, it will not affect the normal operation of other controllers, so that the data center group control has redundant backup, avoiding the whole control cabinet from being unable to be used normally due to controller failure, single point failure, sensor failure, etc.;

[0029] (ii) The present invention relates to data center refrigeration technology, intelligent control technology, data communication technology, energy-saving technology, software development technology, system integration technology and other technologies. It is a data center refrigeration unit control cabinet that integrates remote control and local control functions and can achieve energy-saving effects. It can realize intelligent control and energy-saving optimization of the data center refrigeration system;

[0030] (III) The present invention uses energy-saving technologies and measures to achieve precise control and efficient management of data center refrigeration units, reduce energy consumption, and improve operating efficiency. It can effectively reduce energy consumption, reduce carbon emissions, and extend the sustainable utilization cycle of energy resources. At the same time, it can also save costs for enterprises, improve competitiveness, and promote sustainable economic development;

[0031] (IV) Upgrading and renovating the control cabinet of the data center refrigeration unit has achieved green, low-carbon and energy-saving operation. At the same time, the control cabinet unit has the characteristics of multi-channel and multi-mode control data, which can achieve the purpose of rapid circulation and exchange of the data center cooling source; when the remote control fails, the localized control data can be used to prevent the data center refrigeration unit control cabinet from stopping operation due to the failure, thereby improving the intelligence level of the data center refrigeration unit and providing strong guarantee for the safe and stable operation of the data center;

[0032] (V) The present invention adopts advanced energy-saving control strategies to effectively reduce the energy consumption cost of the data center, while achieving flexible switching between remote and local control, thereby improving the operation and maintenance efficiency of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention is further described below in conjunction with the accompanying drawings.

[0034] Attached Figure 1 This is a schematic diagram of the structure of the energy-saving control system of the data center refrigeration unit;

[0035] Attached Figure 2 It is a structural diagram of the control cabinet unit;

[0036] Attached Figure 3 It is a schematic diagram of redundant switching;

[0037] Attached Figure 4 This is a schematic diagram of mode switching;

[0038] Attached Figure 5 Schematic diagram of the refrigeration unit adder and subtractor. DETAILED DESCRIPTION

[0039] A data center refrigeration unit energy-saving control system of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments of the specification.

[0040] Example:

[0041] As attached Figure 1As shown, this embodiment provides an energy-saving control system for a refrigeration unit in a data center, which includes a hot standby power supply, an industrial gateway, an industrial switch, a group control CPU controller, a heat meter, a flow meter, multiple refrigeration unit controllers, and a sensor acquisition module corresponding to the refrigeration unit controller. The hot standby power supply is electrically connected to the industrial gateway, the group control CPU controller is electrically connected to the hot standby power supply, the industrial switch, and the refrigeration unit controller, respectively, the refrigeration unit controller is electrically connected to the industrial switch and the sensor acquisition module, the heat meter and the flow meter are electrically connected to the group control CPU controller, respectively, the group control CPU controller monitors the refrigeration unit controller, and realizes the start and stop control, operation detection, fault alarm, and sensor monitoring and analysis of the chiller and valve water pump; the group control CPU controller monitors the refrigeration unit controller in real time through a real-time heartbeat signal. Among them, the hot standby power supply selects the main municipal power supply and the backup UPS power supply, or the hot standby power supply selects the main UPS power supply and the backup UPS power supply.

[0042] The heartbeat sending driver of the group control CPU controller in this embodiment sends heartbeat signals to each other with the heartbeat receiving driver of the refrigeration unit controller; the refrigeration unit controller is respectively connected to the water pipe temperature sensor, water pipe temperature sensor and water valve arranged on the main chilled water line.

[0043] As attached Figure 2 As shown, the system also includes a control cabinet unit with a modular design, and the control cabinet unit includes a main control module, a communication module, a data acquisition module and a display module;

[0044] Among them, the main control module is used for data processing and logic control, and establishes a connection with the remote server through the communication module to realize remote monitoring and control functions;

[0045] The data acquisition module is used to collect the operating data of each refrigeration unit in the data center. The operating data of each refrigeration unit includes temperature, humidity and energy consumption;

[0046] The display module is used for local display and operation, which is convenient for on-site personnel to conduct real-time monitoring and debugging; among them, the display module adopts a touch screen.

[0047] The control cabinet unit in this embodiment dynamically adjusts the status and parameters of each refrigeration unit according to the actual load of the refrigeration room and the external environmental conditions, and integrates the energy-saving algorithm to achieve the optimal control strategy, as follows:

[0048] As attached Figure 3 As shown, redundant switching is supported: the group control CPU controller and the cooling unit controller observe each other's status through hard-wired heartbeat signals. If one cooling unit fails, another cooling unit will be started immediately to ensure cooling supply.

[0049] As attached Figure 4As shown, it supports mode switching: the control cabinet unit divides the cooling mode into cooling mode, pre-cooling mode and self-cooling mode; the mode switching is comprehensively determined by the refrigeration unit controller based on the outdoor air wet bulb temperature and stability, the load of the cooling tower fan unit, and the operating conditions of the chiller and plate heat exchanger;

[0050] As attached Figure 5 As shown, it supports the addition and subtraction of refrigeration units: the control cabinet unit automatically increases or decreases the number of refrigeration units in operation according to the optimal load factor of the chiller; the group control CUP controller monitors the temperature on the supply and return water pipes and the flow sensor of the water supply pipe, and calculates the terminal required cooling load, while monitoring the rated cooling capacity signal sent by the refrigeration unit controller. When the calculated load reaches the preset value of the rated capacity ratio of the operation, the group control CUP controller will increase and open the next group of refrigeration units; when the cooling load drops below the preset value of the rated capacity ratio of the operation, the group control CUP controller will gradually close the refrigeration units.

[0051] The energy-saving algorithm in this embodiment is to determine the optimal control strategy and output the optimal control algorithm by setting the outlet water temperature of the refrigeration pump, the frequency of the refrigeration pump, the frequency of the cooling pump and the frequency of the cooling tower fan, while inputting the refrigeration capacity requirements and the outdoor environmental value, internally simulating the refrigeration effect and power consumption, and outputting the optimal control algorithm, including the chiller frequency, the water supply temperature, the frequency of the refrigeration pump cooling pump, and the cooling tower setting value;

[0052] Among them, the energy-saving algorithm formula is:

[0053] The energy-saving algorithm in this embodiment adopts a genetic algorithm to optimize the group control parameters of the data center refrigeration unit. The parameters of the population size, number of iterations, crossover probability and mutation probability are set according to the cooling capacity or temperature, and then the water outlet, frequency, cooling pump and cooling tower setting values ​​of the refrigeration pump are given; the details are as follows:

[0054] S1, create the initial population;

[0055] S2. Obtaining a fitness function according to the energy consumption demand of the data center refrigeration unit: obtaining the energy consumption of the data center refrigeration unit, obtaining the energy consumption value according to the SVM, and calculating the refrigeration effect;

[0056] S3, calculation function for simulating refrigeration effect: obtain the refrigeration capacity calculation formula according to SVM, and judge whether the set value of the outlet water temperature of the refrigeration pump, the refrigeration pump frequency, the cooling pump frequency, and the cooling tower fan are related to the refrigeration capacity according to the temperature difference;

[0057] S4, take the inverse of the fitness numpy array through selection operation, crossover operation and mutation operation to reduce energy consumption;

[0058] S5, obtaining the final population optimized by the genetic algorithm as well as the best individual and fitness;

[0059] S6. Run the genetic algorithm and plot the change of the best fitness with the number of iterations;

[0060] S7. Obtain the optimal parameter combination and draw the best fitness change.

[0061] The key codes are as follows:

[0062] import numpy as np

[0063] import matplotlib.pyplot as plt

[0064] #Genetic algorithm function: Optimize the parameters of the refrigeration system group control strategy, based on the cooling capacity or temperature

[0065] #Input: How much cooling capacity is currently required. Is the quantification standard cooling capacity or set temperature?

[0066] #Output: Each parameter setting value, giving the chilled pump water output, frequency, cooling pump, cooling tower setting value

[0067] #Define the parameters of the genetic algorithm

[0068] POP_SIZE = 100 # population size

[0069] GEN_COUNT = 50 # Iteration times

[0070] CROSS_RATE=0.8#Crossover probability

[0071] MUTATION_RATE=0.01#Mutation probability

[0072] N_PARAMS=16#Number of parameters: chilled water outlet temperature setting value, chilled pump frequency setting value, cooling pump frequency setting value, cooling tower fan frequency setting value

[0073] #One needs to be passed in the cooling capacity requirement and the other is the outdoor measured temperature

[0074] OUT_TEMPERATURE=26#Outdoor temperature

[0075] MIN_COOLING_EFFECT = 200# Cooling effect requirement

[0076] #Create the initial population

[0077] def create_pop(pop_size,n_params):

[0078] return np.random.randint(1,100,(pop_size,n_params))

[0079] #Fitness function: energy consumption of cooling system

[0080] def fitness(individual):

[0081] #Here we need to get the energy consumption of the cooling system

[0082] #When initializing, you can use the mechanism formula to pre-infer

[0083] energy_consumption = np.sum(individual) #Energy consumption calculation

[0084] #According to SVM, get the energy consumption value.

[0085] effect = cooling_effect (individual) # Calculate cooling effect

[0086] if effect <MIN_COOLING_EFFECT:

[0087] #If cooling power can also be used, power consumption is not used

[0088] return energy_consumption+(MIN_COOLING_EFFECT-effect)*1000#penalty factor

[0089] return energy_consumption

[0090] #Calculation function to simulate cooling effect

[0091] def cooling_effect(individual):

[0092] #The cooling effect is obtained by SVM to calculate the cooling capacity, and then judged based on it; it is also related to the temperature difference (independent variables: chilled pump outlet water temperature setting value, chilled pump frequency, cooling pump frequency, cooling tower fan calculation dependent variable: cooling capacity)

[0093] effect = np.sum(individual) #Assuming that the cooling source is 1 degree higher, the effect is reduced by 2 units

[0094] #Call cooling effect calculation in RF

[0095] return effect

[0096] #Selection operation: Roulette selection

[0097] def selection(pop,fitnesses):

[0098] #Energy consumption part, we need to make the energy consumption lower, so here we should take the inverse of the fitness numpy array

[0099] reversed_fitness=1 / (fitnesses+1e-10)

[0100] idx=np.random.choice(np.arange(len(pop)),size=len(pop),replace=True,p=reversed_fitness / reversed_fitness.sum())

[0101] return pop[idx]

[0102]

[0103]

[0104] plt.title('Genetic Algorithm Optimization')

[0105] plt.legend()

[0106] plt.show().

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data center refrigeration unit energy-saving control system, characterized in that: The system includes a hot standby power supply, an industrial gateway, an industrial switch, a group control CPU controller, a heat and cold meter, a flow meter, multiple refrigeration unit controllers and a sensor acquisition module corresponding to the refrigeration unit controllers. The hot standby power supply is electrically connected to the industrial gateway, the group control CPU controller is electrically connected to the hot standby power supply, the industrial switch and the refrigeration unit controller respectively, the refrigeration unit controller is electrically connected to the industrial switch and the sensor acquisition module respectively, the heat and cold meter and the flow meter are electrically connected to the group control CPU controller respectively, the group control CPU controller monitors the refrigeration unit controller to realize start and stop control, operation detection, fault alarm and sensor monitoring and analysis of the chiller and valve water pump; the group control CPU controller monitors the refrigeration unit controller in real time through a real-time heartbeat signal.

2. The data center refrigeration unit energy-saving control system according to claim 1, characterized in that: The heartbeat sending driver of the group control CPU controller sends heartbeat signals to each other with the heartbeat receiving driver of the refrigeration unit controller; the refrigeration unit controller is respectively connected with a water pipe temperature sensor, a water pipe temperature sensor and a water valve arranged on the main chilled water line.

3. The data center refrigeration unit energy-saving control system according to claim 1 or 2, characterized in that: The system also includes a control cabinet unit adopting a modular design, and the control cabinet unit includes a main control module, a communication module, a data acquisition module and a display module; Among them, the main control module is used for data processing and logic control, and establishes a connection with the remote server through the communication module to realize remote monitoring and control functions; The data acquisition module is used to collect the operating data of each refrigeration unit in the data center. The operating data of each refrigeration unit includes temperature, humidity and energy consumption; The display modules are used for local display and operation, respectively, to facilitate real-time monitoring and debugging by on-site personnel.

4. The data center refrigeration unit energy-saving control system according to claim 3, characterized in that: The control cabinet unit dynamically adjusts the status and parameters of each refrigeration unit according to the actual load of the refrigeration room and the external environmental conditions, and integrates energy-saving algorithms to achieve the optimal control strategy, as follows: Support redundant switching: The group control CPU controller and the refrigeration unit controller observe each other's status through hard-wired heartbeat signals. If one refrigeration unit fails, another refrigeration unit will be started immediately to ensure the supply of cold capacity. Support mode switching: The control cabinet unit divides the cooling mode into cooling mode, pre-cooling mode and self-cooling mode; the mode switching is comprehensively determined by the refrigeration unit controller based on the outdoor air wet bulb temperature and stability, the load of the cooling tower fan unit, and the operating conditions of the chiller and plate heat exchanger; Supports refrigeration unit addition and subtraction: the control cabinet unit automatically increases or decreases the number of refrigeration units in operation according to the optimal load factor of the chiller; the group control CUP controller monitors the temperature on the supply and return water pipes and the flow sensor on the water supply pipe, and calculates the terminal required cooling load, while monitoring the rated cooling capacity signal sent by the refrigeration unit controller. When the calculated load reaches the preset value of the rated capacity ratio of the operation, the group control CUP controller will increase and open the next group of refrigeration units; when the cooling load drops below the preset value of the rated capacity ratio of the operation, the group control CUP controller will gradually shut down the refrigeration units.

5. The data center refrigeration unit energy-saving control system according to claim 4, characterized in that: The energy-saving algorithm is to set the outlet water temperature of the refrigeration pump, the frequency of the refrigeration pump, the frequency of the cooling pump and the frequency of the cooling tower fan, input the refrigeration capacity requirements and the outdoor environment value, simulate the refrigeration effect and power consumption internally, determine the optimal control strategy, and output the optimal control algorithm, including the chiller frequency, water supply temperature, refrigeration pump cooling pump frequency, and cooling tower setting value; Among them, the energy-saving algorithm formula is:

6. The data center refrigeration unit energy-saving control system according to claim 5, characterized in that: The energy-saving algorithm uses a genetic algorithm to optimize the group control parameters of the data center refrigeration unit. The population size, number of iterations, crossover probability, and mutation probability are set according to the cooling capacity or temperature, and then the chiller pump water outlet, frequency, cooling pump, and cooling tower setting values ​​are given; the details are as follows: Create an initial population; Obtain the fitness function based on the energy consumption demand of the data center cooling unit: obtain the energy consumption of the data center cooling unit, obtain the energy consumption value based on the SVM, and calculate the cooling effect; Calculation function for simulating refrigeration effect: obtain the refrigeration capacity calculation formula based on SVM, and determine whether the chiller pump outlet water temperature setting value, chiller pump frequency, cooling pump frequency, and cooling tower fan are related to the refrigeration capacity based on the temperature difference; The inverse of the fitness numpy array is taken through selection, crossover and mutation operations to reduce energy consumption; Get the final population optimized by genetic algorithm as well as the best individual and fitness; Run the genetic algorithm and plot the best fitness as a function of the number of iterations; Get the optimal parameter combination and draw the best fitness change.

7. The data center refrigeration unit energy-saving control system according to claim 6, characterized in that: The hot standby power supply selects the main municipal power supply and the backup UPS power supply, or the hot standby power supply selects the main UPS power supply and the backup UPS power supply.

8. The data center refrigeration unit energy-saving control system according to claim 7, characterized in that: The display module adopts a touch screen.

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