Temperature control method, system and equipment for water cooling system of static frequency converter and medium

Abstract

The invention discloses a static frequency converter water cooling system temperature control method, system, device and medium, and the method comprises the steps: firstly, building a BP neural network model for predicting the primary side flow of a plate heat exchanger; then, real-time data of the static frequency converter are obtained to serve as input data of the BP neural network model, and a predicted value of the primary side flow of the plate heat exchanger is obtained; and finally, according to the predicted value of the primary side flow of the plate heat exchanger, an electric three-way valve opening angle instruction value is obtained through calculation, and the electric three-way valve is controlled. According to the technical scheme, the problems of temperature overshoot, long adjustment time and temperature oscillation caused by external interference such as static frequency converter power, plate heat exchanger secondary side temperature and flow can be solved, and stable control over the cooling water temperature is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature control, and particularly relates to a temperature control method, system, device and medium for a water-cooled system of a static frequency converter. Background Art

[0002] Due to advantages such as simple structure, high power density and high reliability, static frequency converters (SFCs) are widely used in the soft start and speed regulation of large synchronous motors. The thyristor elements in the static frequency converter generate a large amount of heat during operation, and the heat needs to be carried out by circulating cooling water. After heat exchange through a plate heat exchanger, the flow rate of the primary side of the plate heat exchanger is adjusted by controlling the angle of an electric three-way valve to cool the cooling water to a reasonable range and then flow back to the static frequency converter to form a water-cooled circulation system.

[0003] The conventional water temperature control method is to collect the temperature at the inlet of the static frequency converter, and generate an electric three-way valve angle control command through the deviation between the target value and the actual value by PI control. In this control process, interference factors such as the power of the static frequency converter, the temperature and flow rate of the secondary side of the plate heat exchanger are ignored, which easily leads to temperature overshoot, long adjustment time, and even temperature oscillation, affecting the operation reliability of the static frequency converter. Summary of the Invention

[0004] The purpose of the present invention is to provide a temperature control method, system, device and medium for a water-cooled system of a static frequency converter, which can solve the problems of temperature overshoot, long adjustment time and temperature oscillation caused by external interferences such as the power of the static frequency converter, the temperature and flow rate of the secondary side of the plate heat exchanger, and realize stable control of the cooling water temperature.

[0005] In order to achieve the above object, the solution of the present invention is as follows:

[0006] A temperature control method for a water-cooled system of a static frequency converter includes the following steps:

[0007] Step 1, constructing a BP neural network model for predicting the flow rate of the primary side of the plate heat exchanger;

[0008] Step 2, obtaining the real-time data of the static frequency converter as the input data of the BP neural network model, and obtaining the predicted value of the flow rate of the primary side of the plate heat exchanger;

[0009] Step 3, calculating the opening angle command value of the electric three-way valve according to the predicted value of the flow rate of the primary side of the plate heat exchanger, and controlling the electric three-way valve.

[0010] In the above step 1, the construction method of the BP neural network model is as follows:

[0011] Step a1: Obtain the historical operation data of the static frequency converter system, determine the input data and output data of the BP neural network model, and perform normalization processing;

[0012] Step a2: Divide the normalized data into a training set, a validation set, and a test set;

[0013] Step a3: Construct a BP neural network model with a three-layer network, train it using the training set, and adjust the hyperparameters to make the training set error meet the requirements. At the same time, substitute the validation set into the model. When the validation set error does not meet the requirements, adjust the hyperparameters and retrain until both the training set error and the validation set error meet the requirements;

[0014] Step a4: Use the test set to verify the generalization ability of the model. If the test set error does not meet the requirements, repeat Step a3 until the errors of the training set, the validation set, and the test set all meet the requirements, and obtain the final BP neural network model.

[0015] In the above Step a1, the historical operation data of the static frequency converter system includes but is not limited to the power of the static frequency converter, the inlet temperature of the static frequency converter, the outlet temperature of the static frequency converter, the main circulation flow rate, the primary side flow rate of the plate heat exchanger, the inlet temperature of the secondary side of the plate heat exchanger, the outlet temperature of the secondary side of the plate heat exchanger, and the secondary side flow rate of the plate heat exchanger. Among them, the primary side flow rate of the plate heat exchanger is used as the output data of the BP neural network, and the rest are used as the input data of the BP neural network.

[0016] In the above Step 3, according to the predicted value of the primary side flow rate of the plate heat exchanger, calculate the opening angle command value of the electric three-way valve, including,

[0017] Calculate the flow split ratio that satisfies the current heat exchange capacity of the static frequency converter according to the predicted value of the primary side flow rate of the plate heat exchanger;

[0018] Query the angle of the electric three-way valve corresponding to the flow split ratio according to the electric three-way valve angle-flow split ratio curve;

[0019] Based on the queried angle of the electric three-way valve, obtain the opening angle command value of the electric three-way valve.

[0020] The construction steps of the above electric three-way valve angle-flow split ratio curve are,

[0021] Step b1: Obtain the opening angles α0, α1, α2, …, α n of the electric three-way valve corresponding to the flow split ratios σ0, σ1, σ2, …, σ n ; where, α0 = 0°, α i = i * Δα, i = 1, 2, 3, …, n, n represents dividing 90° into n equal parts, then Δα = 90° / n;

[0022] Step b3: Based on all measurement points (α j , σ j ), plot curves, where j = 0, 1, 2, …, n, to obtain the electric three-way valve angle - flow split ratio curve.

[0023] In the above step b1, the method for obtaining the flow split ratios σ0, σ1, σ2, …, σ n is as follows:

[0024] When the static frequency converter is in the shutdown state and the water cooling system operates at the rated working condition, collect the opening angles α0, α1, α2, …, α n of the electric three-way valve corresponding to the primary side water flow rates f0, f1, f2, …, f n of the plate heat exchanger and the main circulating water flow rates F0, F1, F2, …, F n respectively;

[0025] Calculate the flow split ratios σ0, σ1, σ2, …, σ n according to σ = f / F.

[0026] Among them, obtaining the opening angle command value of the electric three-way valve based on the queried electric three-way valve angle includes:

[0027] Obtain the current opening angle command value α out of the electric three-way valve, calculate the difference between the queried electric three-way valve angle α p and α out . If α p - α out ≥Δθ, update the opening angle command value of the electric three-way valve to α out + Δθ; if α out - α p ≥Δθ, update the opening angle command value of the electric three-way valve to α out - Δθ; if |α p - α out | < Δθ, maintain the opening angle command value α out unchanged; where Δθ is the command value step interval, and 0°≤α out ≤90°.

[0028] A temperature control system for a static frequency converter water cooling system includes:

[0029] A model construction module configured to construct a BP neural network model for predicting the primary side flow rate of a plate heat exchanger;

[0030] A predicted value acquisition module configured to obtain real-time data of the static frequency converter as the input data of the BP neural network model to obtain the predicted value of the primary side flow rate of the plate heat exchanger; and,

[0031] The command value acquisition module is configured to calculate the opening angle command value of the electric three-way valve according to the predicted value of the primary side flow rate of the plate heat exchanger, and control the electric three-way valve.

[0032] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of the temperature control method for the static frequency converter water cooling system as described above are implemented.

[0033] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the steps of the temperature control method for the static frequency converter water cooling system as described above are implemented.

[0034] After adopting the above solution, the present invention predicts the primary side flow rate of the plate heat exchanger for balancing the heat generation under the real-time working conditions of the static frequency converter through the BP neural network, and then adjusts the electric three-way valve to an appropriate angle to achieve temperature adjustment, which can make up for the deficiencies of the prior art in temperature overshoot, long adjustment time and temperature oscillation caused by interference such as the power of the static frequency converter, the secondary side temperature and flow rate of the plate heat exchanger, and improve the stability of the cooling water temperature control. Description of the Drawings

[0035] Figure 1 It is a schematic diagram of the principle of the static frequency converter water cooling system according to an embodiment of the present invention;

[0036] Among them, the components are as follows:

[0037] ① is the inlet temperature transmitter of the static frequency converter;

[0038] ② is the power unit of the static frequency converter;

[0039] ③ is the outlet temperature transmitter of the static frequency converter;

[0040] ④ is the main circulation flow transmitter of the static frequency converter;

[0041] ⑤ is the main circulation pump of the water cooling system;

[0042] ⑥ is the electric three-way valve;

[0043] ⑦ is the primary side flow transmitter of the plate heat exchanger;

[0044] ⑧ is the secondary side flow transmitter of the plate heat exchanger;

[0045] ⑨ is the plate heat exchanger;

[0046] ⑩ is the secondary side inlet temperature transmitter of the plate heat exchanger;

[0047] It is the temperature transmitter of the secondary water outlet of the plate heat exchanger;

[0048] Figure 2 is a BP neural network training flow chart of an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of the structural principle of the BP neural network of an embodiment of the present invention;

[0050] Figure 4 1 is an electric three-way valve angle-diversion ratio curve of an embodiment of the present invention. DETAILED DESCRIPTION

[0051] With the mature development and application of machine learning, the present invention considers using BP neural network combined with the principle of thermal balance to predict the flow rate required on the primary side of the plate heat exchanger when the static inverter is heated, and obtains the electric three-way valve command angle through the electric three-way valve angle-diversion ratio curve. In this process, the influence of interference factors such as inverter control power and ambient temperature is taken into account to achieve smooth control of water temperature.

[0052] The technical solutions and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] The present invention provides a temperature control method for a water cooling system of a static frequency converter, and the water cooling system of the static frequency converter to which the method is applicable is as follows: Figure 1 As shown in the figure, its working principle is: the cooling water medium with constant pressure and flow continuously flows through the static inverter power unit, and the medium temperature rises to take out the heat generated by the static inverter power unit, and then flows into the electric three-way valve for flow distribution after passing through the main circulation pump of the water cooling system. One part enters the plate heat exchanger to exchange heat with the external circulation cooling water, and the other part directly returns to the static inverter through the short pipe, forming a closed circulation of cooling water. The transmitters installed at each measuring point collect relevant flow and temperature information for the control and protection of the water cooling system.

[0054] The temperature control method of the water cooling system of a static frequency converter provided in an embodiment of the present invention comprises the following steps:

[0055] S1, constructing a BP neural network to predict the primary side flow of a plate heat exchanger;

[0056] Reference Figure 2 According to the embodiment, in S101, historical operating parameters of the static inverter system are collected. Figure 1The collected historical operating parameters of the static frequency converter system include the inlet temperature of the static frequency converter, the power of the static frequency converter, the outlet temperature of the static frequency converter, the main circulation flow rate of the static frequency converter, the primary side flow rate of the plate heat exchanger, the secondary side flow rate of the plate heat exchanger, the secondary side inlet temperature of the plate heat exchanger, and the secondary side outlet temperature of the plate heat exchanger. Among them, the primary side flow rate of the plate heat exchanger is used as the output data of the BP neural network, and the others are used as the input data of the BP neural network. Linear normalization processing is performed on the above collected data, and the normalization formula is as follows:

[0057]

[0058] In the formula, x max and x min are the maximum and minimum values of their respective data sets respectively, x is each piece of original data, and x norm is the data after normalization.

[0059] According to the embodiment, in S102, the data is divided into a training set, a validation set, and a test set according to the approximate distribution principle, and the division ratio is 0.7∶0.15∶0.15.

[0060] According to the embodiment, in S103, a suitable three-layer BP neural network is constructed, where the number of hidden layers is 1, and a suitable number of hidden layer neurons is selected. The empirical formula is as follows:

[0061]

[0062] In the formula, m is the number of hidden layer neurons; p is the number of input layer neurons, which is 7 in this embodiment; q is the number of output layer neurons, which is 1 in this embodiment; a ranges from 1 to 10.

[0063] According to the embodiment, in S104, the gradient descent method is used to learn the training set, participate Figure 3 , during the training process, the generalization ability of the model is continuously tested with the validation set data, and the parameters are adjusted during overfitting, and finally the weight coefficients and thresholds are obtained.

[0064] According to the embodiment, after the model training is completed in S105, the test set data is input into the trained model for classification diagnosis to obtain the error accuracy rate of the test set. If the error accuracy rate of the test set meets the set requirements, there is no need to optimize the network model. If the accuracy rate does not meet the expected value, the network hyperparameters need to be optimized and retrained, and a BP neural network model available for prediction is obtained in S106.

[0065] S2. Establish an electric three-way valve angle - flow split ratio curve offline. The construction steps of the electric three-way valve angle - flow split ratio curve are as follows:

[0066] Step b1: The static frequency converter is in the shutdown state, and the water cooling system operates at the rated condition. Manually adjust the opening angle α0 of the electric three-way valve to 0°, record the primary side water flow f0 of the plate heat exchanger and the main circulating water flow F0 respectively, and calculate the split ratio σ0 = f0 / F0.

[0067] Step b2: For i = 1, 2, 3, …, n, where n represents dividing 90° into n equal parts, then Δα = 90° / n; perform the following operations respectively: Adjust the angle of the electric three-way valve to α i = i*Δα, record the primary side water flow f i of the plate heat exchanger and the main circulating water flow F i , and calculate the split ratio σ i = f i / F i , until α i reaches the maximum limit value of 90°.

[0068] Step b3: Based on all measurement points (α i , σ j ), draw a curve, j = 0, 1, 2, …, n, to obtain the electric three-way valve angle - split ratio curve, as Figure 4 shown.

[0069] S3. Substitute the real-time operation data of the static frequency converter into the final model determined in S106, and perform anti-normalization to obtain the predicted value f p of the primary side flow of the plate heat exchanger. According to the current main flow F p , calculate the split ratio σ p = f p / F p required to meet the heat exchange capacity of the current static frequency converter. Then, according to the above electric 三 three-way valve angle - split ratio curve, calculate the opening angle α p that the electric three-way valve needs to open.

[0070] S4. In order to avoid frequent actions of the electric three-way valve when adjusting the water temperature, the opening angle command value of the electric three-way valve is output in a stepped manner. In this embodiment, the step interval Δθ = 5° is set. When the change in the opening angle that the electric three-way valve needs to open is less than Δθ, the previous command value is maintained unchanged; otherwise, according to the increase or decrease in the angle, the command value is updated accordingly for increase or decrease. Specifically, obtain the opening angle command value α out of the electric three-way valve in the current control cycle, and calculate the opening angle α p of the electric three-way valve. After that, calculate the value of α p -α out . If α p -α out ≥5°, update the opening angle of the electric three-way valve to α out +5°; if αout -α p If it is ≥ 5°, update the opening angle of the electric three-way valve to α out -5°; otherwise, it is |α p -α out | < 5°. At this time, the change amplitude is less than 5°, and the current opening angle of the electric three-way valve is maintained at α out , α out The lower limit of the value of α is 0°, the upper limit is 90°, and the initial value is 0°. It can be seen that its value is an integer multiple of 5°. As the operating conditions of the static frequency converter change, the predicted value of the primary side flow rate of the plate heat exchanger is continuously updated, and the actual opening angle command value α of the electric three-way valve out also follows and is continuously updated to achieve temperature control of the water cooling system.

[0071] The embodiment of the present invention also provides a temperature control system for the water cooling system of a static frequency converter, including

[0072] A model construction module configured to construct a BP neural network model for predicting the primary side flow rate of the plate heat exchanger;

[0073] A predicted value acquisition module configured to acquire real-time data of the static frequency converter as input data of the BP neural network model to obtain a predicted value of the primary side flow rate of the plate heat exchanger; and

[0074] An instruction value acquisition module configured to calculate an opening angle instruction value of the electric three-way valve according to the predicted value of the primary side flow rate of the plate heat exchanger to control the electric three-way valve.

[0075] The embodiment of the present invention also provides another computer device, including a processor and a memory configured to store a computer program that can run on the processor; wherein, when the processor is configured to run the computer program, it executes the method steps in the foregoing embodiment.

[0076] In practical applications, the above-mentioned processor includes a Field-Programmable Gate Array (FPGA). The processor can be a Central Processing Unit (CPU) or a Digital Signal Processor (DSP, Digital Signal Processing). It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor can also be others, and the embodiments of the present invention do not make specific limitations.

[0077] The above-mentioned memory can be a volatile memory, such as a random-access memory (RAM); or a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or a combination of the above types of memories, and provides instructions and data to the processor.

[0078] In an exemplary embodiment, the embodiment of the present invention further provides a computer-readable storage medium for storing a computer program.

[0079] Optionally, the computer-readable storage medium can be applied to any one of the methods in the embodiments of the present invention, and the computer program causes the computer to execute the corresponding processes implemented by the processor in each of the methods of the embodiments of the present invention. For the sake of brevity, it will not be described in detail here.

[0080] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0081] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0082] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks to achieve the specified functions.

[0083] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks to achieve the specified functions.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks to achieve the specified functions.

[0085] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0086] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A temperature control method for the water-cooling system of a static frequency converter, characterized in that It includes the following steps: Step 1, construct a BP neural network model for predicting the primary side flow rate of the plate heat exchanger; Step 2, obtain the real-time data of the static frequency converter as the input data of the BP neural network model, and obtain the predicted value of the primary side flow rate of the plate heat exchanger; Step 3, calculate the opening angle command value of the electric three-way valve according to the predicted value of the primary side flow rate of the plate heat exchanger, and control the electric three-way valve.

2. The method according to claim 1, wherein: In the said Step 1, the construction method of the BP neural network model is as follows: Step a1, obtain the historical operation data of the static frequency converter system, determine the input data and output data of the BP neural network model, and perform normalization processing; Step a2, divide the normalized data into a training set, a validation set and a test set; Step a3, construct a BP neural network model with a three-layer network, use the training set for training, adjust the hyperparameters to make the training set error meet the requirements; at the same time, substitute the validation set into the model, and when the validation set error does not meet the requirements, adjust the hyperparameters and retrain until both the training set error and the validation set error meet the requirements; Step a4, use the test set to verify the generalization ability of the model. If the test set error does not meet the requirements, repeat Step a3 until the errors of the training set, the validation set and the test set all meet the requirements, and obtain the final BP neural network model.

3. The method according to claim 2, wherein: In the said Step a1, the historical operation data of the static frequency converter system includes but is not limited to the power of the static frequency converter, the inlet temperature of the static frequency converter, the outlet temperature of the static frequency converter, the main circulation flow rate, the primary side flow rate of the plate heat exchanger, the secondary side inlet temperature of the plate heat exchanger, the secondary side outlet temperature of the plate heat exchanger, and the secondary side flow rate of the plate heat exchanger. Among them, the primary side flow rate of the plate heat exchanger is used as the output data of the BP neural network, and the rest are used as the input data of the BP neural network.

4. The method according to claim 1, wherein: In the said Step 3, calculating the opening angle command value of the electric three-way valve according to the predicted value of the primary side flow rate of the plate heat exchanger includes: Calculate the flow split ratio that meets the current heat exchange capacity of the static frequency converter according to the predicted value of the primary side flow rate of the plate heat exchanger; Query the angle of the electric three-way valve corresponding to the flow split ratio according to the electric three-way valve angle-flow split ratio curve; Based on the queried angle of the electric three-way valve, obtain the opening angle command value of the electric three-way valve.

5. The method according to claim 4, wherein: The construction steps of the said electric three-way valve angle-flow split ratio curve are as follows: Step b1, obtain the opening angles α0, α1, α2, …, α of the electric three-way valve n and the corresponding flow split ratios σ0, σ1, σ2, …, σ n ; where α0 = 0°, α i = i * Δα, i = 1, 2, 3, …, n, n represents dividing 90° into n equal parts, then Δα = 90° / n; Step b3: Based on all measurement points (α j , σ j ), draw a curve, where j = 0, 1, 2, …, n, to obtain the electric three-way valve angle - flow split ratio curve.

6. The method according to claim 5, wherein: In the step b1, the acquisition methods of the split ratios σ0, σ1, σ2, …, σ n are as follows: When the static frequency converter is in the shutdown state and the water cooling system operates under the rated conditions, the opening angles α0, α1, α2, …, α of the electric three-way valve are collected respectively n The corresponding primary side water flow rates f0, f1, f2, …, f of the plate heat exchanger n , the main circulating water flow rates F0, F1, F2, …, F n ; The flow split ratios σ0, σ1, σ2, …, σ are calculated according to σ = f / F n .

7. The method according to claim 4, characterized in that: Based on the queried angle of the electric three-way valve, obtaining the opening angle command value of the electric three-way valve includes: Obtain the current opening angle command value α of the electric three-way valve out , calculate the angle α of the electric three-way valve obtained by query p and the difference with α out . If α p - α out ≥ Δθ, update the opening angle command value of the electric three-way valve to α out + Δθ; if α out - α p ≥ Δθ, update the opening angle command value of the electric three-way valve to α out - Δθ; if |α p - α out | < Δθ, maintain the opening angle command value α of the electric three-way valve out unchanged; where Δθ is the command value step interval, and 0° ≤ α out ≤ 90°.

8. A temperature control system for the water-cooled system of a static frequency converter, characterized in that: Include: A model construction module, configured to construct a BP neural network model for predicting the primary side flow rate of the plate heat exchanger; A predicted value acquisition module, configured to obtain the real-time data of the static frequency converter as the input data of the BP neural network model, and obtain the predicted value of the primary side flow rate of the plate heat exchanger; and A command value acquisition module, configured to calculate the opening angle command value of the electric three-way valve according to the predicted value of the primary side flow rate of the plate heat exchanger, and control the electric three-way valve.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: When the processor executes the computer program, it implements the steps of the temperature control method for the static frequency converter water cooling system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by a processor, it implements the steps of the temperature control method for the static frequency converter water cooling system according to any one of claims 1 to 7.

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