Data center heating and ventilation group control system operation optimization method and system

Abstract

The invention discloses a data center heating and ventilation group control system operation optimization method and system, and relates to the technical field of heating and ventilation group control, and the method comprises the following steps: S1, analyzing water chilling unit data, S2, determining a switch selection mode, S3, processing time domain data, S4, dividing risk levels, S5, screening main time domain data, and S6, optimizing the selection mode. According to the method, the switching times of each water chilling unit, the energy consumption of all the units and the proportion of insufficient refrigeration are determined by utilizing the actual time domain data under different risk levels and the collected time domain data, and main time domain data are screened out through the indexes, so that the subsequent switching optimization mode can be smoother, and the efficiency is improved. The system analyzes the main time domain data after determining the main time domain data, and selects the water chilling unit of the next switch according to the accumulated parameter value, so that the opportunity of switching on and off the water chilling unit each time is more reasonable and scientific.

Description

Technical Field

[0001] The present invention relates to the technical field of HVAC group control, and in particular to an operation optimization method and system for a HVAC group control system in a data center. Background Art

[0002] The operation optimization method of the HVAC group control system of the data center monitors the parameters and equipment status of the chiller in real time, flexibly adjusts the cooling strategy, and optimizes the cold source operation mode, ultimately achieving efficient and stable operation. The invention patent with application number 202310839379.4 discloses "a HVAC group control method and system based on the ISODATA algorithm. The steps of the HVAC group control method are: collecting sensor data, analyzing sensor data and selecting corresponding control algorithms and control strategies, adjusting parameters, generating and issuing control instructions. The HVAC group control system includes a cloud-based energy-saving optimization platform device, an edge-end main controller device, a collection device, a control device, and a field sensor; the present invention adopts the ISODATA algorithm to perform cluster analysis on various factors, statistically analyzes the corresponding scene characteristics, and realizes automatic switching of modes through optimization scheduling algorithms; the present invention has the advantages of relatively better reliability, relatively more obvious system energy-saving effect, and can achieve optimal control."

[0003] The above-mentioned existing technology solves the problem of being unable to automatically switch between cooling and heating modes according to scene characteristics. However, when the system is running, since there are no multiple ways to control the opening or closing of the chiller, when the required cooling capacity is too large, the load of the unit is too high, which affects the service life. When the required cooling capacity is too small, the load is too low, resulting in a waste of resources. In addition, the system has not further optimized and adjusted the method, so that the resources cannot be fully utilized. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for optimizing the operation of a data center HVAC group control system, so as to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the operation of a data center HVAC group control system, comprising the following steps:

[0006] S1. Analyze chiller data: determine the ideal chilled water mass flow, ideal set point temperature, ideal return water temperature, actual chilled water mass flow, actual return water temperature, actual set point temperature, and the return water temperature, chilled water mass flow, set point temperature, and overflow flow in the main loop directly connected to the chiller;

[0007] S2. Determine the on / off selection method: Build a chiller model using ANSYS simulation software. Select the chiller to be on or off based on the actual cooling capacity, actual return water temperature, and actual overflow flow. Count the number of chiller on / off cycles, energy consumption, and operating time, and record the proportion of cooling shortage for each chiller.

[0008] S3. Processing time domain data: Determine the probability distribution of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset, and use the time domain data to construct the relationship function between the return water temperature difference, cold water flow error, and overflow flow error;

[0009] S4. Risk level classification: Construct error risk levels and classify them into six categories: low risk level, medium risk level, medium-high risk level, high risk level, very high risk level, and acceptable risk level;

[0010] S5. Filtering key time-domain data: Count the number of chiller on / off cycles, energy consumption of all units, and the proportion of insufficient cooling corresponding to actual time-domain data at different risk levels, and filter out key time-domain data;

[0011] S6. Optimization selection method: Use the constructed conditional function to calculate the cumulative parameter value corresponding to the current actual cooling capacity, and select the next chiller to be switched on or off based on the cumulative parameter value.

[0012] Preferably, the step S1 specifically includes the following steps:

[0013] S101. Determine the ideal chilled water mass flow m and ideal set point temperature T in the main circuit directly connected to the chiller. d And the ideal return water temperature T h After that, the actual cold water mass flow m' and the actual set point temperature T' in the main circuit during operation are obtained. d And the return water temperature T during collection h col , using m, T d 、T h , m′, T′ d and T h col Calculate the cold water flow error Δm and the set point temperature difference ΔT d And the return water temperature offset value ΔT h col , where Δm=mm′, ΔT d =T d -T′ d , ΔT h col =T h -T h col ;

[0014] S102, using the ideal return water temperature T h , ideal cold water mass flow rate m, ideal set point temperature T d , cold water flow error Δm and set point temperature difference ΔT d Calculate the actual return water temperature T' h ,in According to the actual return water temperature T' h , ideal return water temperature T h , Return water temperature T during collection h col And the return water temperature offset value ΔT h col Analyze the return water temperature difference ΔT h ,in

[0015] S103, read the cold water mass flow m during collection col , set point temperature T d col And the return water temperature T h col , determine the ideal cold water mass flow rate m, the ideal set point temperature T d and ideal return water temperature T h , according to m col 、T d col , m and T d Analyze the cold water flow offset value Δm col and the set point temperature offset ΔT d col , where Δm col =mm col , ΔT d col =T d -T d col , using m col 、T d col 、T h col , Δm col , ΔT d col and return water temperature offset ΔT h col Calculate the cooling capacity offset value ΔS col , where ΔS col =-γ·m col (ΔT h col -ΔT d col )-γ.Δmcol (T h col -T d col ), γ represents the specific heat capacity;

[0016] S104, obtain the overflow flow l during collection col , set the ideal constant side cold water flow l0 and the ideal variable side cold water flow l b Then, according to l0 and l b Calculate the ideal overflow flow rate l, where l = l0-l b =l col +Δl col , Δl col Indicates the overflow flow offset value, determines the constant side flow error value Δl0 and the variable side flow error value Δl b , according to l, Δl0 and Δl b Calculate the actual overflow flow l', where l'=l-(Δl0+Δl b )=l col -(Δl b -Δl col -Δl0), using l col The overflow flow error value Δl=Δl is analyzed by l′ b -Δl col -Δl0.

[0017] Preferably, the step S2 specifically includes the following steps:

[0018] S201, construct a chiller model using ANSYS simulation software, obtain the rated power P0 of the chiller, and set the upper limit of the optimal operating range to P according to the rated power P0. max and the lower limit P min , where P max =0.92*P0,P min =0.5*P0, count the number of chillers started k and the actual return water temperature T' h , actual cooling capacity S', actual chilled water mass flow m', actual set point temperature T' d and the actual overflow flow l′, where S′=γ·m′(T′ h -T′ d ), according to the current number of chillers k and the upper limit of the optimal operating range P max and the lower limit P min Calculate the initial maximum cooling capacity and initial minimum cooling capacity in like Then select the next chiller to start from the chillers that are not turned on. If Then select the next closed chiller from the turned-on chillers;

[0019] S202. Calculate the initial maximum return water temperature and the initial minimum return water temperature based on the initial maximum cooling capacity, the initial minimum cooling capacity, the ideal chilled water mass flow rate, the ideal set point temperature, the number of chillers, and the specific heat capacity. If the actual return water temperature is greater than or equal to the initial maximum return water temperature, select the next chiller to be started from the chillers that are not started. If the actual return water temperature is less than or equal to the initial minimum return water temperature, select the next chiller to be shut down from the chillers that are already started. Obtain a preset flow value for the chiller. If the actual overflow flow rate is less than or equal to zero, select the next chiller to be started from the chillers that are not started. If the actual overflow flow rate is greater than or equal to the preset flow rate value, select the next chiller to be shut down from the chillers that are already started.

[0020] S203. Count the number of times each chiller is turned on and off, determine the energy consumption and operating time of all chillers, record the duration that the difference between the actual set point temperature and the ideal set point temperature of each chiller is greater than a preset value, and calculate the proportion of insufficient cooling based on the duration and operating time.

[0021] Preferably, the step S3 specifically includes the following steps:

[0022] S301, reading the accuracy of the temperature sensor that collects the set point temperature and return water temperature, and the electromagnetic flowmeter that collects the cold water mass flow rate and overflow flow rate, and using the accuracy to analyze the probability distribution of the set point temperature offset value, return water temperature offset value, cold water flow rate offset value, and overflow flow rate offset value;

[0023] S302: Set the sampling interval and the number of data, obtain the return water temperature time domain data, the cold water mass flow time domain data, and the overflow flow time domain data according to the sampling interval, use Fourier transform to convert all the time domain data into the corresponding frequency domain data, and calculate the intensity and spectral components based on the frequency domain data;

[0024] S303 , constructing a relationship function between the return water temperature difference and time, a relationship function between the cold water flow error and time, and a relationship function between the overflow flow error and time through intensity and spectrum components.

[0025] Preferably, the step S5 specifically includes the following steps:

[0026] S501. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset according to different risk levels, and set the values ​​of the amplitudes in the return water temperature difference relationship function, cold water flow error relationship function, and overflow flow error relationship function according to the risk level;

[0027] S502. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, overflow flow offset, return water temperature difference, cold water flow error, and overflow flow error at different risk levels based on the times corresponding to the collected set point temperature time domain data, return water temperature time domain data, cold water mass flow time domain data, and overflow flow time domain data. Calculate the actual return water temperature time domain data, actual cold water mass flow time domain data, and actual overflow flow time domain data at different risk levels using the offset, temperature difference, and error values.

[0028] S503. Determine the number of times each chiller is turned on and off, the energy consumption of all units, and the proportion of insufficient cooling through the collected time domain data and the actual time domain data. Filter out the main time domain data based on the number of times each chiller is turned on and off, the energy consumption of all units, and the proportion of insufficient cooling corresponding to the actual time domain data under different risk levels. The main time domain data include return water temperature time domain data, cold water mass flow time domain data, and set point temperature time domain data.

[0029] Preferably, step S6 specifically includes the following steps:

[0030] S601. After acquiring the collected time-domain data of return water temperature, time-domain data of cold water mass flow, and time-domain data of set point temperature, calculate the mean corresponding to each set of time-domain data, analyze the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature based on the time-domain data and the mean, and combine the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature using a standard deviation analysis algorithm to obtain the standard deviation of cooling capacity. The standard deviation analysis algorithm is specifically as follows:

[0031]

[0032] Among them, σ S represents the standard deviation of cooling capacity, γ represents the specific heat capacity, N represents the amount of data, represents the standard coefficient, ΔT represents the temperature difference, represents the mean value of the return water temperature time domain data, represents the mean of the set point temperature time domain data, represents the time domain data of the i-th cold water mass flow rate, represents the mean of the time domain data of the i-th cold water mass flow rate, represents the time domain data of the i-th return water temperature, represents the time domain data of the i-th set point temperature, m col represents the mass flow rate of cold water during collection, and i represents the parameter;

[0033] S602. Calculate the current cooling capacity time domain data based on the collected set point temperature time domain data, return water temperature time domain data, and cold water mass flow time domain data, and analyze the probability distribution of the actual cooling capacity using the cooling capacity standard deviation and the cooling capacity time domain data.

[0034] Preferably, the step S6 further includes the following steps:

[0035] S603: Obtain the initial maximum cooling capacity and the initial minimum cooling capacity, and construct a corresponding conditional function using the probability distribution of the initial maximum cooling capacity, the initial minimum cooling capacity, and the actual cooling capacity;

[0036] S604. Set the corresponding startup parameters and shutdown parameters according to the initial maximum cooling capacity and the initial minimum cooling capacity. If the current actual cooling capacity is greater than the initial maximum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the cumulative parameter value is greater than or equal to the startup parameter, select the next chiller to be started from the chillers that have not been turned on. If the current actual cooling capacity is less than the initial minimum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the parameter value is greater than or equal to the shutdown parameter, select the next chiller to be turned off from the chillers that have been turned on.

[0037] The data center HVAC group control system operation optimization system includes a data analysis unit, a mode design unit, a data processing unit, a level classification unit, a data screening unit and a selection optimization unit;

[0038] The data analysis unit determines the ideal chilled water mass flow, the ideal set point temperature, the ideal return water temperature, the actual chilled water mass flow, the actual return water temperature, the actual set point temperature, and the return water temperature, chilled water mass flow, set point temperature, and overflow flow in the primary loop directly connected to the chiller;

[0039] The design unit uses ANSYS simulation software to build a chiller model, selects and switches the chiller based on actual cooling capacity, actual return water temperature, and actual overflow flow, calculates the number of chiller on-offs, energy consumption, and operating time, and records the proportion of insufficient cooling for each chiller.

[0040] The data processing unit determines the probability distribution of the set point temperature offset value, the return water temperature offset value, the cold water flow offset value, and the overflow flow offset value, and constructs the relationship function of the return water temperature difference value, the cold water flow error value, and the overflow flow error value with time using the time domain data;

[0041] The grading unit constructs error risk levels and divides the error risk levels into six categories, namely low risk level, medium risk level, medium-high risk level, high risk level, very high risk level and acceptable risk level;

[0042] The data screening unit counts the number of chiller on / off times, energy consumption of all units, and the proportion of insufficient cooling corresponding to the actual time domain data at different risk levels, and screens out the main time domain data;

[0043] The selection optimization unit calculates the cumulative parameter value corresponding to the current actual cooling capacity using the constructed conditional function, and selects the next chiller to be switched on or off according to the cumulative parameter value.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention extracts the ideal chilled water mass flow rate, the ideal set point temperature, the ideal return water temperature, and the ideal overflow flow rate in the main circuit connected to the chilled water group, reads the collected data of corresponding types, combines them with the actual data, and introduces a temperature difference value, an offset value, and an error value to indicate the correlation between the three types of data, so that the actual data can be accurately analyzed according to the temperature difference value, the offset value, the error value and the collected data. At the same time, the initial maximum cooling capacity and the initial minimum cooling capacity are set. When the required cooling capacity is greater than the initial maximum cooling capacity, a new chiller is opened in time, which can reduce the load of the existing chiller to a certain extent and increase the service life of the equipment. When the required cooling capacity is less than the initial minimum cooling capacity, some chillers are shut down, which can reduce overall energy consumption and avoid waste of resources. The return water temperature is associated with the cooling capacity, so the corresponding initial maximum return water temperature and the minimum return water temperature can also help control the switch of the chiller. In addition, the additional addition of the preset flow value further ensures flexibility in actual operation.

[0046] 2. The present invention analyzes the collected equipment accuracy and time domain data to obtain the probability distribution of the offset value and the relationship function between the temperature difference value, the error value and the time, so as to facilitate the subsequent accurate determination of the corresponding values ​​of the offset value, the temperature difference value and the error value according to different risk levels. The actual time domain data under different risk levels and the collected time domain data are used to determine the number of times each chiller is switched on and off, the energy consumption of all units and the proportion of insufficient cooling. The main time domain data are screened out through these indicators to ensure that the subsequent optimization of the switching method can be smoother. After determining the main time domain data, the system analyzes it and selects the next chiller to be switched on and off according to the accumulated parameter values, so that the timing of each switching of the chiller is more reasonable and scientific, ensuring the effective use of resources and preventing related equipment in the data center from being affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Provides an overall method flow chart for an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] See also Figure 1 The present invention provides a technical solution: a method for optimizing the operation of a data center HVAC group control system, comprising the following steps:

[0050] S1. Analyze chiller data: determine the ideal chilled water mass flow, ideal set point temperature, ideal return water temperature, actual chilled water mass flow, actual return water temperature, actual set point temperature, and the return water temperature, chilled water mass flow, set point temperature, and overflow flow in the main loop directly connected to the chiller;

[0051] S2. Determine the on / off selection method: Build a chiller model using ANSYS simulation software. Select the chiller to be on or off based on the actual cooling capacity, actual return water temperature, and actual overflow flow. Count the number of chiller on / off cycles, energy consumption, and operating time, and record the proportion of cooling shortage for each chiller.

[0052] S3. Processing time domain data: Determine the probability distribution of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset, and use the time domain data to construct the relationship function between the return water temperature difference, cold water flow error, and overflow flow error;

[0053] S4. Risk level classification: Construct error risk levels and classify them into six categories: low risk level, medium risk level, medium-high risk level, high risk level, very high risk level, and acceptable risk level;

[0054] S5. Filtering key time-domain data: Count the number of chiller on / off cycles, energy consumption of all units, and the proportion of insufficient cooling corresponding to actual time-domain data at different risk levels, and filter out key time-domain data;

[0055] S6. Optimization selection method: Use the constructed conditional function to calculate the cumulative parameter value corresponding to the current actual cooling capacity, and select the next chiller to be switched on or off based on the cumulative parameter value.

[0056] Step S1 specifically includes the following steps:

[0057] S101. Determine the ideal chilled water mass flow m and ideal set point temperature T in the main circuit directly connected to the chiller. d And the ideal return water temperature T h After that, the actual cold water mass flow m' and the actual set point temperature T' in the main circuit during operation are obtained. d And the return water temperature T during collection h col , using m, T d 、T h , m′, T′ d and T h col Calculate the cold water flow error Δm and the set point temperature difference ΔT d And the return water temperature offset value ΔT h col , where Δm=mm′, ΔT d =T d -T′ d , ΔT h col =T h -T h col ;

[0058] S102, using the ideal return water temperature T h , ideal cold water mass flow rate m, ideal set point temperature T d , cold water flow error Δm and set point temperature difference ΔT d Calculate the actual return water temperature T' h ,in According to the actual return water temperature T' h , ideal return water temperature T h , Return water temperature T during collection h col And the return water temperature offset value ΔT h col Analyze the return water temperature difference ΔT h ,in

[0059] S103, read the cold water mass flow m during collection col , set point temperature T d col And the return water temperature T h col , determine the ideal cold water mass flow rate m, the ideal set point temperature T d and ideal return water temperature T h , according to m col 、T d col , m and T dAnalyze the cold water flow offset value Δm col and the set point temperature offset ΔT d col , where Δm col =mm col , ΔT d col =T d -T d col , using m col 、T d col 、T h col , Δm col , ΔT d col and return water temperature offset ΔT h col Calculate the cooling capacity offset value ΔS col , where ΔS col =-γ·m col (ΔT h col -ΔT d col )-γ.Δm col (T h col -T d col ), γ represents the specific heat capacity;

[0060] S104, obtain the overflow flow l during collection col , set the ideal constant side cold water flow l0 and the ideal variable side cold water flow l b Then, according to l0 and l b Calculate the ideal overflow flow rate l, where l = l0-l b =l col +Δl col , Δl col Indicates the overflow flow offset value, determines the constant side flow error value Δl0 and the variable side flow error value Δl b , according to l, Δl0 and Δl b Calculate the actual overflow flow l', where l'=l-(Δl0+Δl b )=l col -(Δl b -Δl col -Δl0), using l col The overflow flow error value Δl=Δl is analyzed by l′ b -Δl col -Δl0;

[0061] Step S2 specifically includes the following steps:

[0062] S201, construct a chiller model using ANSYS simulation software, obtain the rated power P0 of the chiller, and set the upper limit of the optimal operating range to P according to the rated power P0. max and the lower limit P min , where P max =0.92*P0,P min =0.5*P0, count the number of chillers started k and the actual return water temperature T' h , actual cooling capacity S', actual chilled water mass flow m', actual set point temperature T' d and the actual overflow flow l′, where S′=γ·m′(T′ h -T′ d ), according to the current number of chillers k and the upper limit of the optimal operating range P max and the lower limit P min Calculate the initial maximum cooling capacity and initial minimum cooling capacity in like Then select the next chiller to start from the chillers that are not turned on. If Then select the next closed chiller from the turned-on chillers;

[0063] S202. Calculate the initial maximum return water temperature and the initial minimum return water temperature based on the initial maximum cooling capacity, the initial minimum cooling capacity, the ideal chilled water mass flow rate, the ideal set point temperature, the number of chillers, and the specific heat capacity. If the actual return water temperature is greater than or equal to the initial maximum return water temperature, select the next chiller to be started from the chillers that are not started. If the actual return water temperature is less than or equal to the initial minimum return water temperature, select the next chiller to be shut down from the chillers that are already started. Obtain a preset flow value for the chiller. If the actual overflow flow rate is less than or equal to zero, select the next chiller to be started from the chillers that are not started. If the actual overflow flow rate is greater than or equal to the preset flow rate value, select the next chiller to be shut down from the chillers that are already started.

[0064] S203. Count the number of times each chiller is turned on and off, determine the energy consumption and operating time of all chillers, record the duration of time the difference between the actual set point temperature and the ideal set point temperature of each chiller is greater than a preset value, and calculate the proportion of insufficient cooling based on the duration and operating time;

[0065] Step S3 specifically includes the following steps:

[0066] S301, reading the accuracy of the temperature sensor that collects the set point temperature and return water temperature, and the electromagnetic flowmeter that collects the cold water mass flow rate and overflow flow rate, and using the accuracy to analyze the probability distribution of the set point temperature offset value, return water temperature offset value, cold water flow rate offset value, and overflow flow rate offset value;

[0067] S302: Set the sampling interval and the number of data, obtain the return water temperature time domain data, the cold water mass flow time domain data, and the overflow flow time domain data according to the sampling interval, use Fourier transform to convert all the time domain data into the corresponding frequency domain data, and calculate the intensity and spectral components based on the frequency domain data;

[0068] S303, constructing a relationship function between the return water temperature difference and time, a relationship function between the cold water flow error and time, and a relationship function between the overflow flow error and time through intensity and spectrum components;

[0069] Step S5 specifically includes the following steps:

[0070] S501. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset according to different risk levels, and set the values ​​of the amplitudes in the return water temperature difference relationship function, cold water flow error relationship function, and overflow flow error relationship function according to the risk level;

[0071] S502. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, overflow flow offset, return water temperature difference, cold water flow error, and overflow flow error at different risk levels based on the times corresponding to the collected set point temperature time domain data, return water temperature time domain data, cold water mass flow time domain data, and overflow flow time domain data. Calculate the actual return water temperature time domain data, actual cold water mass flow time domain data, and actual overflow flow time domain data at different risk levels using the offset, temperature difference, and error values.

[0072] S503. Determine the number of times each chiller is turned on and off, the energy consumption of all chillers, and the proportion of insufficient cooling using the collected time domain data and the actual time domain data. Filter out key time domain data based on the number of times each chiller is turned on and off, the energy consumption of all chillers, and the proportion of insufficient cooling using the actual time domain data at different risk levels. The key time domain data includes return water temperature time domain data, chilled water mass flow rate time domain data, and set point temperature time domain data.

[0073] Step S6 specifically includes the following steps:

[0074] S601. After acquiring the collected time-domain data of return water temperature, time-domain data of cold water mass flow, and time-domain data of set point temperature, calculate the mean corresponding to each set of time-domain data, analyze the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature based on the time-domain data and the mean, and combine the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature using a standard deviation analysis algorithm to obtain the standard deviation of cooling capacity. The standard deviation analysis algorithm is specifically as follows:

[0075]

[0076] Among them, σ S represents the standard deviation of cooling capacity, γ represents the specific heat capacity, N represents the amount of data, represents the standard coefficient, ΔT represents the temperature difference, represents the mean value of the return water temperature time domain data, represents the mean of the set point temperature time domain data, represents the time domain data of the i-th cold water mass flow rate, represents the mean of the time domain data of the i-th cold water mass flow rate, represents the time domain data of the i-th return water temperature, represents the time domain data of the i-th set point temperature, m col represents the mass flow rate of cold water during collection, and i represents the parameter;

[0077] S602, calculating the current cooling capacity time domain data based on the collected set point temperature time domain data, return water temperature time domain data, and cold water mass flow time domain data, and analyzing the probability distribution of the actual cooling capacity using the cooling capacity standard deviation and the cooling capacity time domain data;

[0078] Step S6 specifically further includes the following steps:

[0079] S603: Obtain the initial maximum cooling capacity and the initial minimum cooling capacity, and construct a corresponding conditional function using the probability distribution of the initial maximum cooling capacity, the initial minimum cooling capacity, and the actual cooling capacity. The conditional function is specifically:

[0080]

[0081] Among them, φ x Indicates the cumulative parameter value when the current actual cooling capacity is greater than the initial maximum cooling capacity, φ y Indicates the cumulative parameter value when the current actual cooling capacity is less than the initial minimum cooling capacity, v represents the adjustment coefficient, x, y represent the parameters, ΔS x Indicates the difference between the current actual cooling capacity and the initial maximum cooling capacity, ΔS y Indicates the difference between the current actual cooling capacity and the initial minimum cooling capacity, σ S represents the standard deviation of cooling capacity, and e represents the natural constant;

[0082] S604. Set corresponding startup parameters and shutdown parameters according to the initial maximum cooling capacity and the initial minimum cooling capacity. If the current actual cooling capacity is greater than the initial maximum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the cumulative parameter value is greater than or equal to the startup parameter, select the next chiller to be started from the unstarted chillers. If the current actual cooling capacity is less than the initial minimum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the parameter value is greater than or equal to the shutdown parameter, select the next chiller to be shut down from the started chillers.

[0083] A data center HVAC group control system operation optimization system includes a data analysis unit, a mode design unit, a data processing unit, a level classification unit, a data screening unit and a selection optimization unit;

[0084] The data analysis unit determines the ideal chilled water mass flow rate, the ideal set point temperature, the ideal return water temperature, the actual chilled water mass flow rate, the actual return water temperature, the actual set point temperature, and the return water temperature, chilled water mass flow rate, the set point temperature, and the overflow flow rate in the primary loop directly connected to the chiller;

[0085] The design unit uses ANSYS simulation software to build a chiller model. It selects and switches the chiller based on the actual cooling capacity, actual return water temperature, and actual overflow flow. It also counts the number of chiller on / off times, energy consumption, and operating time, and records the proportion of cooling shortage for each chiller.

[0086] The data processing unit determines the probability distribution of the set point temperature offset value, the return water temperature offset value, the cold water flow offset value, and the overflow flow offset value, and constructs the relationship function of the return water temperature difference value, the cold water flow error value, and the overflow flow error value with time using the time domain data;

[0087] The grading unit constructs error risk levels and divides the error risk levels into six categories: low risk level, medium risk level, medium-high risk level, high risk level, extremely high risk level, and acceptable risk level;

[0088] The data screening unit counts the number of chiller on / off times, energy consumption of all units, and the proportion of insufficient cooling corresponding to the actual time domain data at different risk levels, and screens out the main time domain data;

[0089] The optimization unit selects the cumulative parameter value corresponding to the current actual cooling capacity using the constructed conditional function, and selects the next chiller to be switched on or off based on the cumulative parameter value.

[0090] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0091] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data center HVAC group control system operation optimization method, characterized in that: The method comprises the following steps: S1. Analyze chiller data: determine the ideal chilled water mass flow, ideal set point temperature, ideal return water temperature, actual chilled water mass flow, actual return water temperature, actual set point temperature, and the return water temperature, chilled water mass flow, set point temperature, and overflow flow in the main loop directly connected to the chiller; S2. Determine the on / off selection method: Build a chiller model using ANSYS simulation software. Select the chiller to be on or off based on the actual cooling capacity, actual return water temperature, and actual overflow flow. Count the number of chiller on / off cycles, energy consumption, and operating time, and record the proportion of cooling shortage for each chiller. S3. Processing time domain data: Determine the probability distribution of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset, and use the time domain data to construct the relationship function between the return water temperature difference, cold water flow error, and overflow flow error; S4. Risk level classification: Construct error risk levels and classify them into six categories: low risk level, medium risk level, medium-high risk level, high risk level, very high risk level, and acceptable risk level; S5. Filtering key time-domain data: Count the number of chiller on / off cycles, energy consumption of all units, and the proportion of insufficient cooling corresponding to actual time-domain data at different risk levels, and filter out key time-domain data; S6. Optimization selection method: Use the constructed conditional function to calculate the cumulative parameter value corresponding to the current actual cooling capacity, and select the next chiller to be switched on or off based on the cumulative parameter value.

2. The method for optimizing the operation of a data center HVAC group control system according to claim 1, characterized in that: The step S1 specifically includes the following steps: S101. Determine the ideal chilled water mass flow m and ideal set point temperature T in the main circuit directly connected to the chiller. d And the ideal return water temperature T h After that, the actual cold water mass flow m′ and the actual set point temperature T in the main loop during operation are obtained. d ' and the return water temperature during collection Using m, T d 、T h ,m′,T d 'and Calculate the cold water flow error Δm and the set point temperature difference ΔT d and return water temperature offset Where Δm=mm′, ΔT d =T d -T d ′, S102, using the ideal return water temperature T h , ideal cold water mass flow rate m, ideal set point temperature T d , cold water flow error Δm and set point temperature difference ΔT d Calculate the actual return water temperature T h ',in According to the actual return water temperature T h ', ideal return water temperature T h , Return water temperature during collection and return water temperature offset Analyze the return water temperature difference ΔT h ,in S103, read the cold water mass flow m during collection col , set point temperature and return water temperature Determine the ideal cold water mass flow rate m and the ideal set point temperature T d and ideal return water temperature T h , according to m col 、 m and T d Analyze the cold water flow offset value Δm col and set point temperature offset where Δm col =mm col , Using m col 、 Δm col 、 and return water temperature offset Calculate the cooling capacity offset value ΔS col ,in γ represents specific heat capacity; S104, obtain the overflow flow l during collection col , set the ideal constant side cold water flow l0 and the ideal variable side cold water flow l b Then, according to l0 and l b Calculate the ideal overflow flow rate l, where l = l0-l b =l col +Δl col , Δl col Indicates the overflow flow offset value, determines the constant side flow error value Δl0 and the variable side flow error value Δl b , according to l, Δl0 and Δl b Calculate the actual overflow flow l', where l'=l-(Δl0+Δl b )=l col -(Δl b -Δl col -Δl0), using l col The overflow flow error value Δl=Δl is analyzed by l′ b -Δl col -Δl0.

3. The method for optimizing the operation of a data center HVAC group control system according to claim 1, characterized in that: The step S2 specifically includes the following steps: S201, construct a chiller model using ANSYS simulation software, obtain the rated power P0 of the chiller, and set the upper limit of the optimal operating range to P according to the rated power P0. max and the lower limit P min , where P max =0.92*P0,P min =0.5*P0, count the number of started chillers k and the actual return water temperature T h ′, actual cooling capacity S′, actual chilled water mass flow m′, actual set point temperature T d ′ and the actual overflow flow l′, where S′=γ·m′(T h ′-T d ′), according to the current number of chillers k and the upper limit value P of the optimal operating range max and the lower limit P min Calculate the initial maximum cooling capacity and initial minimum cooling capacity in like Then select the next chiller to start from the chillers that are not turned on. If Then select the next closed chiller from the turned-on chillers; S202. Calculate the initial maximum return water temperature and the initial minimum return water temperature based on the initial maximum cooling capacity, the initial minimum cooling capacity, the ideal chilled water mass flow rate, the ideal set point temperature, the number of chillers, and the specific heat capacity. If the actual return water temperature is greater than or equal to the initial maximum return water temperature, select the next chiller to be started from the chillers that are not started. If the actual return water temperature is less than or equal to the initial minimum return water temperature, select the next chiller to be shut down from the chillers that are already started. Obtain a preset flow value for the chiller. If the actual overflow flow rate is less than or equal to zero, select the next chiller to be started from the chillers that are not started. If the actual overflow flow rate is greater than or equal to the preset flow rate value, select the next chiller to be shut down from the chillers that are already started. S203. Count the number of times each chiller is turned on and off, determine the energy consumption and operating time of all chillers, record the duration that the difference between the actual set point temperature and the ideal set point temperature of each chiller is greater than a preset value, and calculate the proportion of insufficient cooling based on the duration and operating time.

4. The method for optimizing the operation of a data center HVAC group control system according to claim 1, characterized in that: The step S3 specifically includes the following steps: S301, reading the accuracy of the temperature sensor that collects the set point temperature and return water temperature, and the electromagnetic flowmeter that collects the cold water mass flow rate and overflow flow rate, and using the accuracy to analyze the probability distribution of the set point temperature offset value, return water temperature offset value, cold water flow rate offset value, and overflow flow rate offset value; S302: Set the sampling interval and the number of data, obtain the return water temperature time domain data, the cold water mass flow time domain data, and the overflow flow time domain data according to the sampling interval, use Fourier transform to convert all the time domain data into the corresponding frequency domain data, and calculate the intensity and spectral components based on the frequency domain data; S303 , constructing a relationship function between the return water temperature difference and time, a relationship function between the cold water flow error and time, and a relationship function between the overflow flow error and time through intensity and spectrum components.

5. The method for optimizing the operation of a data center HVAC group control system according to claim 1, characterized in that: The step S5 specifically includes the following steps: S501. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, and overflow flow offset according to different risk levels, and set the values ​​of the amplitudes in the return water temperature difference relationship function, cold water flow error relationship function, and overflow flow error relationship function according to the risk level; S502. Determine the values ​​of the set point temperature offset, return water temperature offset, cold water flow offset, overflow flow offset, return water temperature difference, cold water flow error, and overflow flow error at different risk levels based on the times corresponding to the collected set point temperature time domain data, return water temperature time domain data, cold water mass flow time domain data, and overflow flow time domain data. Calculate the actual return water temperature time domain data, actual cold water mass flow time domain data, and actual overflow flow time domain data at different risk levels using the offset, temperature difference, and error values. S503. Determine the number of times each chiller is turned on and off, the energy consumption of all units, and the proportion of insufficient cooling through the collected time domain data and the actual time domain data. Filter out the main time domain data based on the number of times each chiller is turned on and off, the energy consumption of all units, and the proportion of insufficient cooling corresponding to the actual time domain data under different risk levels. The main time domain data include return water temperature time domain data, cold water mass flow time domain data, and set point temperature time domain data.

6. The method for optimizing the operation of a data center HVAC group control system according to claim 1, characterized in that: The step S6 specifically includes the following steps: S601. After acquiring the collected time-domain data of return water temperature, time-domain data of cold water mass flow, and time-domain data of set point temperature, calculate the mean corresponding to each set of time-domain data, analyze the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature based on the time-domain data and the mean, and combine the standard deviation of cold water flow, the standard deviation of set point temperature, and the standard deviation of return water temperature using a standard deviation analysis algorithm to obtain the standard deviation of cooling capacity; S602. Calculate the current cooling capacity time domain data based on the collected set point temperature time domain data, return water temperature time domain data, and cold water mass flow time domain data, and analyze the probability distribution of the actual cooling capacity using the cooling capacity standard deviation and the cooling capacity time domain data.

7. The method for optimizing the operation of a data center HVAC group control system according to claim 6, characterized in that: The step S6 specifically further includes the following steps: S603: Obtain the initial maximum cooling capacity and the initial minimum cooling capacity, and construct a corresponding conditional function using the probability distribution of the initial maximum cooling capacity, the initial minimum cooling capacity, and the actual cooling capacity; S604. Set the corresponding startup parameters and shutdown parameters according to the initial maximum cooling capacity and the initial minimum cooling capacity. If the current actual cooling capacity is greater than the initial maximum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the cumulative parameter value is greater than or equal to the startup parameter, select the next chiller to be started from the chillers that have not been turned on. If the current actual cooling capacity is less than the initial minimum cooling capacity, calculate the cumulative parameter value corresponding to the current actual cooling capacity according to the conditional function. When the parameter value is greater than or equal to the shutdown parameter, select the next chiller to be turned off from the chillers that have been turned on.

8. A data center HVAC group control system operation optimization system, characterized in that: The operation optimization system is applicable to a data center HVAC group control system operation optimization method according to any one of claims 1 to 7, comprising a data analysis unit, a mode design unit, a data processing unit, a level division unit, a data screening unit, and a selection optimization unit; The data analysis unit determines the ideal chilled water mass flow, the ideal set point temperature, the ideal return water temperature, the actual chilled water mass flow, the actual return water temperature, the actual set point temperature, and the return water temperature, chilled water mass flow, set point temperature, and overflow flow in the primary loop directly connected to the chiller; The design unit uses ANSYS simulation software to build a chiller model, selects and switches the chiller based on actual cooling capacity, actual return water temperature, and actual overflow flow, calculates the number of chiller on-offs, energy consumption, and operating time, and records the proportion of insufficient cooling for each chiller. The data processing unit determines the probability distribution of the set point temperature offset value, the return water temperature offset value, the cold water flow offset value, and the overflow flow offset value, and constructs the relationship function of the return water temperature difference value, the cold water flow error value, and the overflow flow error value with time using the time domain data; The grading unit constructs error risk levels and divides the error risk levels into six categories, namely low risk level, medium risk level, medium-high risk level, high risk level, very high risk level and acceptable risk level; The data screening unit counts the number of chiller on / off times, energy consumption of all units, and the proportion of insufficient cooling corresponding to the actual time domain data at different risk levels, and screens out the main time domain data; The selection optimization unit calculates the cumulative parameter value corresponding to the current actual cooling capacity using the constructed conditional function, and selects the next chiller to be switched on or off according to the cumulative parameter value.

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