High-voltage electric appliance switch cabinet operation control method and system

Abstract

The invention provides an operation control method and system for a high-voltage electric appliance switch cabinet, and belongs to the technical field of power distribution automation, and the method comprises the steps: calculating the correlation between a plurality of first operation parameters of the high-voltage electric appliance switch cabinet and control output data, and selecting a second operation parameter based on a correlation calculation result; training an intelligent control model based on the second operation parameter and control output data of the high-voltage electric appliance switch cabinet; and performing operation control on the high-voltage electrical appliance switch cabinet based on the intelligent control model. The high-voltage electric appliance switch cabinet operation control method and system provided by the invention can improve the control precision of the high-voltage electric appliance switch cabinet.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution automation, and more specifically, relates to a method and system for controlling the operation of a high-voltage electrical switch cabinet. Background Art

[0002] As an important equipment in the power system, the high-voltage electrical switchgear can distribute the high-voltage electricity from the power plant or the upper substation to different power consumption areas or lower substations according to different load requirements. By operating the circuit breakers, disconnectors and other equipment in the switchgear, the circuit can be cut off or connected when needed to ensure the safe and stable operation of the power system. Therefore, the reliable operation of the high-voltage electrical switchgear is crucial to ensure the stability and safety of power supply.

[0003] With the development of artificial intelligence technology, the control of high-voltage electrical switchgear has become intelligent, which can reduce the reliance on manual intervention and reduce the risk of human error. However, the control accuracy of the existing high-voltage electrical switchgear operation control method needs to be improved. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method and system for controlling the operation of a high-voltage electrical switch cabinet, so as to improve the control accuracy of the high-voltage electrical switch cabinet.

[0005] A first aspect of the embodiments of the present disclosure provides a method for controlling the operation of a high-voltage electrical switch cabinet, comprising: Respectively calculating correlations between a plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data, and selecting a second operating parameter based on the correlation calculation results; Training an intelligent control model based on the second operating parameter and control output data of the high-voltage electrical switch cabinet; The operation control of the high-voltage electrical switch cabinet is performed based on the intelligent control model.

[0006] A second aspect of the embodiments of the present disclosure provides a high-voltage electrical switch cabinet operation control system, comprising: A correlation calculation module, used to respectively calculate the correlation between a plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data, and select a second operating parameter based on the correlation calculation result; A model training module, used for training an intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet; An operation control module is used to perform operation control of a high-voltage electrical switch cabinet based on the intelligent control model.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned high-voltage electrical switch cabinet operation control method when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned high-voltage electrical switch cabinet operation control method are implemented.

[0009] The beneficial effects of the high-voltage electrical switch cabinet operation control method and system provided by the embodiments of the present disclosure are: In the disclosed embodiment, by calculating the correlation between multiple first operating parameters and control output data, key parameters closely related to the control output (i.e., second operating parameters) are screened out, and an intelligent control model is trained based on the second operating parameters and the control output data of the high-voltage electrical switchgear. This can reduce the dimension and complexity of the data, reduce the interference of irrelevant parameters, and enable the model to focus more on factors that have an important impact on the control output, thereby improving the control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a high-voltage electrical switch cabinet operation control method provided in an embodiment of the present disclosure; Figure 2 A structural block diagram of a high-voltage electrical switch cabinet operation control system provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flow chart of a high-voltage electrical switch cabinet operation control method provided in an embodiment of the present disclosure, the method comprising: S101: respectively calculating the correlations between a plurality of first operating parameters of a high-voltage electrical switch cabinet and control output data, and selecting a second operating parameter based on the correlation calculation results.

[0015] In this embodiment, the plurality of first operating parameters of the high-voltage electrical switch cabinet may include electrical parameters (including voltage, current, power, frequency, etc.), environmental parameters (including temperature, humidity, etc.). The control output data may include switch status, adjustment instructions (including voltage adjustment instructions, switching instructions of reactive power compensation devices, etc.).

[0016] The correlation between the multiple first operating parameters and the control output data can be achieved through a variety of methods such as the Pearson correlation coefficient and the Spearman correlation coefficient. Taking the Pearson correlation coefficient as an example, the value range of the Pearson correlation coefficient is between -1 and 1. The larger the absolute value of the correlation coefficient, the stronger the linear correlation between the two variables. A positive correlation indicates that the change trends of the two variables are the same, and a negative correlation indicates that the change trends of the two variables are opposite.

[0017] A first threshold may be preset, the correlation coefficient may be compared with the first threshold, and a first operating parameter whose corresponding correlation coefficient is greater than the first threshold may be selected as the second operating parameter.

[0018] In different application scenarios, the selected second operating parameter may be different. For example, in heavy industrial production environments such as steel mills and chemical plants, due to the frequent start-up and stop of large equipment, the current fluctuates greatly. It is necessary to adjust the control strategy of the switch cabinet in time according to the change of current to prevent overcurrent faults from causing damage to the equipment. At this time, the current parameter is highly correlated with the control output data. For another example, in the power system, in order to improve the efficiency and stability of the power system, it is necessary to maintain the power factor at a high level. It is necessary to automatically switch the reactive compensation device according to the change of the power factor to realize the intelligent adjustment of the power factor. At this time, the power factor is highly correlated with the control output data.

[0019] S102: Training an intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet.

[0020] In this embodiment, the intelligent control model is trained using the selected second operating parameters and the control output data of the high-voltage electrical switch cabinet, which can screen out key parameters closely related to the control output, reduce the dimension and complexity of the data, and improve the efficiency of subsequent model training and operation control.

[0021] Specifically, a variety of machine learning algorithms can be used to train the intelligent control model, such as neural networks, support vector machines, decision trees, etc. By learning a large amount of data, the intelligent control model can establish a mapping relationship between the second operating parameter and the control output, so that the control output can be predicted and adjusted according to the input operating parameters, thereby realizing intelligent control of the switch cabinet.

[0022] S103: Performing operation control of the high-voltage electrical switch cabinet based on the intelligent control model.

[0023] In actual operation, the data of the second operating parameter can be collected in real time and input into the intelligent control model. The model outputs the corresponding control strategy based on the learned relationship, such as adjusting the voltage, current, switch status, etc., so as to realize the operation control of the high-voltage electrical switchgear.

[0024] From the above, it can be concluded that this embodiment calculates the correlation between multiple first operating parameters and control output data, screens out key parameters closely related to the control output (i.e., second operating parameters), and trains an intelligent control model based on the second operating parameters and the control output data of the high-voltage electrical switchgear. This can reduce the dimension and complexity of the data, reduce the interference of irrelevant parameters, and make the model more focused on factors that have an important impact on the control output, thereby improving the control accuracy.

[0025] In one embodiment of the present disclosure, training an intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet includes: Dividing the second operating parameter and the control output data of the high-voltage electrical switch cabinet into training data and test data; Train the neural network model based on the training data, and verify the neural network model based on the test data; If the verification fails, the third operating parameter is deleted from the second operating parameter, and the process returns to the step of training the neural network model based on the training data; the third operating parameter is the second operating parameter with the smallest corresponding correlation calculation result; If the verification is successful, the neural network model is determined as the intelligent control model.

[0026] In this embodiment, during the training process of the intelligent control model, the second operating parameter and the control output data of the high-voltage electrical switch cabinet can be first divided into training data and test data, and the neural network model is trained based on the training data. The neural network model minimizes the loss function by continuously adjusting the weights and biases, thereby learning the mapping relationship between the second operating parameter and the control output data.

[0027] Then, the trained neural network model is verified using the test data. Verification indicators may include accuracy, mean square error, etc., which can be selected by technicians in this field according to actual needs. If the verification result does not meet the requirements, it means that the model may have overfitting (performing well on training data but poorly on test data) or underfitting (performing poorly on both training data and test data).

[0028] If the verification result does not meet the requirements, the third operating parameter, that is, the second operating parameter with the smallest correlation calculation result, can be deleted from the second operating parameter, because the operating parameter with the smallest correlation may contribute less to the model. Removing it can simplify the model and improve model performance. Then return to execute retraining of the neural network model until the model verification passes.

[0029] If the model verification passes, it indicates that the neural network model training is completed, and the trained neural network model is used as the intelligent control model.

[0030] It can be concluded from the above that this embodiment can simplify the model structure, reduce the computational complexity, and improve the training and operation efficiency of the model by deleting the operating parameters with less correlation through multiple iterations. At the same time, it can also reduce the impact of noise and irrelevant factors on the model, so that the model can focus more on key operating parameters and improve the control accuracy of the model.

[0031] In one embodiment of the present disclosure, training a neural network model based on training data includes: If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the first application scenario, determining the loss function of the neural network model based on the power quality; If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the second application scenario, the loss function of the neural network model is determined based on the power loss.

[0032] In this embodiment, the first type of application scenario is a scenario where the power quality level is greater than the set level (i.e., a scenario with high power quality requirements), such as precision electronic equipment manufacturing, medical facilities, etc., where it is necessary to ensure voltage stability and low harmonic content. The loss function can be designed as an indicator related to power quality, such as a function of voltage deviation, harmonic distortion, etc., and the neural network model outputs the corresponding control strategy by minimizing the loss function, thereby optimizing power quality. The loss function related to power quality can be: ; where J represents the loss function, Indicates the real-time value of voltage. Indicates the expected value of the voltage.

[0033] The second type of scenario is the scenario where the amount of power loss is greater than the set threshold (i.e., the scenario with large power loss), such as large industrial enterprises, commercial buildings, etc. The above scenarios focus on energy saving and cost reduction, and reducing power loss is an important goal. Therefore, the loss function can be designed based on indicators of power loss, such as power loss, line loss, etc., which helps the neural network model learn control strategies that can reduce power loss. The loss function related to power loss can be:

[0034] in, represents the loss function, Indicates the calculation time. Indicates the transformer power loss, , Indicates the line loss power, , Represents the sum of the power losses of other electrical equipment. represents the current, Indicates, voltage, , , All are set constants.

[0035] From the above, it can be concluded that this embodiment determines the corresponding loss function according to different application scenarios, so that the neural network model can be trained and optimized more specifically, can better meet the needs of the corresponding scenarios, and improve the adaptability and performance of high-voltage electrical switchgear in various application scenarios.

[0036] In one embodiment of the present disclosure, the high-voltage electrical switch cabinet operation control method further includes: Determine the update cycle based on the failure rate, usage time and load rate of the high-voltage electrical switchgear; The intelligent control model is retrained based on the update cycle.

[0037] In this embodiment, the failure rate of the high-voltage electrical switch cabinet can be obtained by counting the number of failures within a set time (for example, 1000 hours), the usage time is the time difference between the time when the high-voltage electrical switch cabinet is put into use and the current time, and the load rate is the ratio of the actual load power of the high-voltage electrical switch cabinet to the rated power.

[0038] As the high-voltage electrical switchgear operates, its performance and status will change. By regularly retraining the intelligent control model, the model can always maintain accurate control of the high-voltage electrical switchgear, adapt to new operating conditions in a timely manner, reduce control deviations caused by equipment changes, and improve control accuracy and reliability.

[0039] At the same time, if the failure rate of the switchgear is high, it means that its operating state is unstable, and the intelligent control model needs to be updated more frequently to adapt to the changing situation and improve the accuracy and reliability of the control. As the use time increases, the equipment may experience problems such as aging and performance degradation, and the update cycle needs to be adjusted according to the length of use. For example, a newly installed high-voltage electrical switchgear can be updated for the first time after a long time, while a high-voltage electrical switchgear with a longer use time may require a shorter update cycle. The working intensity of the high-voltage electrical switchgear is high under high load rate, and the risk of wear and failure is relatively high. Therefore, a high-voltage electrical switchgear with a high load rate requires a shorter update cycle.

[0040] Therefore, this embodiment determines the update cycle of the intelligent control model based on the failure rate, service life and load rate of the high-voltage electrical switch cabinet. Specifically, the reference value P of the failure rate can be preset. a0 , the reference value of load factor P l0 And the reference value XT0 of the update cycle. Based on the actual failure rate P of the high-voltage electrical switch cabinet a And the reference value of failure rate P a0 Determine the first proportionality coefficient k1=P a0 / P a , based on the actual load rate P of the high-voltage electrical switchgear l And the reference value P of the load factor l0 Determine the second proportionality coefficient k2=P l0 / P l .

[0041] At the same time, the usage time is segmented. For example, for a high-voltage electrical switch cabinet whose usage time is less than the first time threshold, the corresponding third proportional coefficient k3 is a first value; for a high-voltage electrical switch cabinet whose usage time is between the first time threshold and the second time threshold, the corresponding second proportional coefficient k3 is a second value; for a high-voltage electrical switch cabinet whose usage time is greater than the second time threshold, the corresponding third proportional coefficient k3 is a third value, and the first value, the second value and the third value decrease successively.

[0042] By adjusting the reference value XT0 of the update period based on the first proportional coefficient k1, the second proportional coefficient k2, and the third proportional coefficient k3, the update period XT=XT0×k1×k2×k3 can be obtained.

[0043] It can be concluded from the above that this embodiment comprehensively considers the failure rate, usage time and load rate to determine the update cycle of the intelligent control model, and avoids frequent updates of the intelligent control model and increased computing burden while ensuring equipment reliability.

[0044] In one embodiment of the present disclosure, the high-voltage electrical switch cabinet operation control method further includes: In response to the fault information output by the fault detection model, the output instructions of the intelligent control model are adjusted, and the operation control of the high-voltage electrical switch cabinet is performed based on the adjusted output instructions.

[0045] In this embodiment, a fault detection model can also be configured in the high-voltage electrical switch cabinet, and the fault detection model can monitor the high-voltage electrical switch cabinet in real time, detect various potential fault conditions in time, and output fault information. The fault information may include the fault type (such as short circuit, overload, ground fault, etc.), fault location, fault severity, etc. For example, the fault detection model determines that there is a short circuit fault in the switch cabinet by monitoring abnormal changes in current and voltage, or by detecting partial discharge and other phenomena, and outputs corresponding fault information.

[0046] The fault information output by the fault detection model can be transmitted to the intelligent control model. After receiving the fault information from the fault detection model, the intelligent control model can adjust the output instructions in time. For example, if the fault detection model detects an overload fault and sends the overload fault information to the intelligent control model, the intelligent control model can adjust the output instructions to reduce the load or start the overload protection device to deal with the overload fault. Among them, the load can be reduced by adjusting the output voltage of the transformer, and overload protection can be achieved by controlling the opening and closing of the circuit breaker.

[0047] It can be concluded from the above that this embodiment can detect faults in time through the coordinated work of the fault detection model and the intelligent control model, and automatically adjust the operation control strategy according to the fault information to enhance the reliability and stability of the high-voltage electrical switch cabinet. By adjusting the operating state in time, the further development of the fault can be avoided and the fault tolerance of the high-voltage electrical switch cabinet can be improved.

[0048] In one embodiment of the present disclosure, the high-voltage electrical switch cabinet operation control method further includes: In response to a change in the operating mode output by the intelligent control model, a diagnostic parameter of the fault diagnosis model is adjusted.

[0049] In this embodiment, it is considered that the fault diagnosis parameters may change in different operation modes. For example, in the light load operation mode, due to the small current, the overcurrent protection threshold can be set relatively low. Under normal circumstances, the overcurrent protection threshold is 120% of the rated current, which can be adjusted to 110% in the light load mode to improve the detection accuracy of overcurrent faults.

[0050] It can be concluded from the above that this embodiment adjusts the diagnostic parameters of the fault diagnosis model based on the change of the operating mode, which can improve the accuracy and effectiveness of fault diagnosis.

[0051] Corresponding to the high-voltage electrical switch cabinet operation control method of the above embodiment, Figure 2 This is a structural block diagram of a high-voltage electrical switch cabinet operation control system provided by an embodiment of the present disclosure. For ease of description, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The high-voltage electrical switch cabinet operation control system 20 includes: a correlation calculation module 21, a model training module 22 and an operation control module 23. The correlation calculation module 21 is used to respectively calculate the correlation between a plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data, and select the second operating parameter based on the correlation calculation result; A model training module 22, used for training an intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet; The operation control module 23 is used to perform operation control of the high-voltage electrical switch cabinet based on the intelligent control model.

[0052] In one embodiment of the present disclosure, the model training module 22 is specifically used to: Dividing the second operating parameter and the control output data of the high-voltage electrical switch cabinet into training data and test data; Train the neural network model based on the training data, and verify the neural network model based on the test data; If the verification fails, the third operating parameter is deleted from the second operating parameter, and the process returns to the step of training the neural network model based on the training data; the third operating parameter is the second operating parameter with the smallest corresponding correlation calculation result; If the verification is successful, the neural network model is determined as the intelligent control model.

[0053] In one embodiment of the present disclosure, the model training module 22 is further used to: If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the first application scenario, determining the loss function of the neural network model based on the power quality; If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the second application scenario, the loss function of the neural network model is determined based on the power loss.

[0054] In one embodiment of the present disclosure, the model training module 22 is specifically used to: Determine the update cycle based on the failure rate, usage time and load rate of the high-voltage electrical switchgear; The intelligent control model is retrained based on the update cycle.

[0055] In one embodiment of the present disclosure, the operation control module 23 is specifically used to: In response to the fault information output by the fault detection model, the output instructions of the intelligent control model are adjusted, and the operation control of the high-voltage electrical switch cabinet is performed based on the adjusted output instructions.

[0056] In one embodiment of the present disclosure, the operation control module 23 is specifically used to: In response to a change in the operating mode output by the intelligent control model, a diagnostic parameter of the fault diagnosis model is adjusted.

[0057] In one embodiment of the present disclosure, the correlation calculation module 21 is specifically used for: The correlation between the plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data is determined based on the correlation coefficient.

[0058] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0059] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0060] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0061] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0062] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the high-voltage electrical switchgear operation control method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0063] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0064] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0065] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0067] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0068] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0069] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0070] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for controlling the operation of a high-voltage electrical switch cabinet, characterized in that: include: Respectively calculating correlations between a plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data, and selecting a second operating parameter based on the correlation calculation results; Training an intelligent control model based on the second operating parameter and control output data of the high-voltage electrical switch cabinet; The operation control of the high-voltage electrical switch cabinet is performed based on the intelligent control model.

2. The high-voltage electrical switch cabinet operation control method according to claim 1, characterized in that: The training of the intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet includes: Dividing the second operating parameter and the control output data of the high-voltage electrical switch cabinet into training data and test data; Training a neural network model based on the training data, and verifying the neural network model based on the test data; If the verification fails, the third operating parameter is deleted from the second operating parameter, and the step of training the neural network model based on the training data is returned to execute; the third operating parameter is the second operating parameter with the smallest corresponding correlation calculation result; If the verification is successful, the neural network model is determined as an intelligent control model.

3. The high-voltage electrical switch cabinet operation control method according to claim 2, characterized in that: The step of training a neural network model based on the training data comprises: If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the first application scenario, determining the loss function of the neural network model based on the power quality; If the second operating parameter and the control output data of the high-voltage electrical switch cabinet belong to the second type of application scenario, the loss function of the neural network model is determined based on the power loss.

4. The high-voltage electrical switch cabinet operation control method according to claim 1, characterized in that: Also includes: Determine the update cycle based on the failure rate, usage time and load rate of the high-voltage electrical switchgear; The intelligent control model is retrained based on the update cycle.

5. The high-voltage electrical switch cabinet operation control method according to claim 1, characterized in that: Also includes: In response to the fault information output by the fault detection model, the output instructions of the intelligent control model are adjusted, and the operation control of the high-voltage electrical switch cabinet is performed based on the adjusted output instructions.

6. The high-voltage electrical switch cabinet operation control method according to claim 5, characterized in that: Also includes: In response to a change in the operating mode output by the intelligent control model, a diagnostic parameter of the fault diagnosis model is adjusted.

7. The high-voltage electrical switch cabinet operation control method according to claim 1, characterized in that: Also includes: The correlation between the plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data is determined based on the correlation coefficient.

8. A high-voltage electrical switch cabinet operation control system, characterized in that: include: A correlation calculation module, used to respectively calculate the correlation between a plurality of first operating parameters of the high-voltage electrical switch cabinet and the control output data, and select a second operating parameter based on the correlation calculation result; A model training module, used for training an intelligent control model based on the second operating parameter and the control output data of the high-voltage electrical switch cabinet; An operation control module is used to perform operation control of a high-voltage electrical switch cabinet based on the intelligent control model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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