An automatic adjustment device and method for three-phase load imbalance

Abstract

This invention discloses an automatic three-phase load imbalance adjustment device and method, relating to the field of power automation control technology. The method includes: collecting and preprocessing multi-dimensional data; extracting key feature parameters from the multi-dimensional data and uploading them to the main controller; the multi-dimensional data including load current, three-phase voltage, power factor, and temperature; executing a control strategy and optimizing it in conjunction with local real-time data to generate an optimal control strategy; performing phase switching operations according to the optimal control strategy, while simultaneously monitoring voltage and current waveforms and generating operating status data to be fed back to the main controller; analyzing the operating status data, recording and generating feedback data based on the analysis results, and transmitting the feedback data back to the main controller to optimize the control strategy. This invention improves the accuracy of model prediction, reduces computational complexity, and enhances the adaptability of the power grid to different scenarios through effective integration and modeling of multi-dimensional data.

Description

Technical Field

[0001] This invention relates to the field of power automation control technology, and in particular to an automatic adjustment device and method for three-phase load imbalance. Background Technology

[0002] With the continuous expansion of power grid scale and the rapid growth of distributed energy resources and loads, three-phase load imbalance has gradually become a significant factor affecting grid operating efficiency and power quality. Three-phase load imbalance not only leads to equipment overheating and increased line losses but also reduces the reliability and stability of the power supply network. With the development of smart grid technology, automated control devices are gradually being introduced. These devices typically rely on real-time collected data such as current and voltage for analysis and generate control strategies through algorithms.

[0003] Existing three-phase load imbalance regulation technologies have improved grid operating efficiency to some extent, but they still have the following shortcomings: First, in the data processing stage, traditional methods often focus only on single or a few characteristic parameters, ignoring the correlation between multi-dimensional data and their impact on the overall load status. This one-sided data processing approach may lead to insufficient model prediction accuracy. Second, in the generation and execution of regulation strategies, existing technologies lack a closed-loop feedback mechanism, making it impossible to dynamically adjust the regulation strategies according to the actual operating status. Once the load distribution changes significantly or an anomaly occurs, the grid may not be able to respond in a timely manner, resulting in poor regulation effects or even triggering new imbalance problems. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an automatic adjustment method for three-phase load imbalance to solve the problems of one-sided data processing and lack of dynamic control capability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an automatic adjustment method for three-phase load imbalance, comprising: collecting and preprocessing multi-dimensional data; extracting key feature parameters based on the multi-dimensional data and uploading them to a main controller; the multi-dimensional data including load current, three-phase voltage, power factor, and temperature; constructing a three-phase load distribution model of the distribution area based on the key feature parameters; generating a control strategy by analyzing the three-phase load state index output by the model; executing the control strategy and optimizing it in conjunction with local real-time data to generate an optimal control strategy; performing phase switching operations according to the optimal control strategy, while monitoring voltage and current waveforms, generating operating status data and feeding it back to the main controller; analyzing the operating status data, recording and generating feedback data based on the analysis results, and transmitting the feedback data back to the main controller to optimize the control strategy.

[0008] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the preprocessing includes noise reduction processing, missing value processing, and data normalization processing.

[0009] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the specific steps are as follows: extracting key feature parameters based on multi-dimensional data and uploading them to the main controller.

[0010] The local controller extracts the three-phase current imbalance by calculating the ratio of the difference between the maximum and minimum three-phase currents to the average value.

[0011] The power factor is extracted by measuring the phase difference between voltage and current;

[0012] Based on the line current and resistance, the line loss is extracted using Joule's law calculation method.

[0013] Key characteristic parameters are uploaded to the main controller via wireless communication.

[0014] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the specific steps for constructing the three-phase load distribution model of the transformer area based on key characteristic parameters are as follows:

[0015] The main controller organizes the extracted key feature parameters into a unified matrix data structure;

[0016] Based on the data structure, a three-phase load distribution model for the transformer area is constructed to predict the three-phase load state index F(X).

[0017] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the step of generating a control strategy by analyzing the three-phase load state index output by the model includes the following specific steps.

[0018] The main controller defines the load threshold F based on historical data and optimization goals;

[0019] When F(X) ≥ F, the three-phase load distribution is considered to be out of limit, and the time window T is shortened. base Time;

[0020] When F(X) < F, the three-phase load distribution is considered normal, and the current time window T is maintained. base Time;

[0021] Based on the analysis results, the target phase configuration X that minimizes the comprehensive evaluation value F(X) of the three-phase load state is found using dynamic programming. * and the switching time window T of each commutator switch base ;

[0022] Based on target phase configuration X * and time window T base Generate a control strategy S.

[0023] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the steps of executing the control strategy and optimizing it in conjunction with local real-time data to generate the optimal control strategy are as follows:

[0024] The phase switching device collects local three-phase voltage, load current and power factor in real time;

[0025] Based on local real-time data, the switching time window T is adjusted. base Optimize and generate a new time window T new ;

[0026] The optimal control strategy includes the target phase configuration X for each commutator switch. * With the adjusted time window T new .

[0027] As a preferred embodiment of the three-phase load imbalance automatic adjustment method of the present invention, the following steps are taken: switching operation of the commutator is performed according to the optimal control strategy, while simultaneously monitoring voltage and current waveforms, generating operating status data and feeding it back to the main controller; analyzing the operating status data, recording and generating feedback data based on the analysis results, and transmitting the feedback data back to the main controller to optimize the control strategy.

[0028] The local controller is based on the time window T bew and target phase configuration X * Send a switching command to the commutator;

[0029] Real-time acquisition of voltage and current waveform data, monitoring of changes in current in each phase, monitoring of amplitude and phase angle of voltage in each phase, monitoring of changes in power factor, and generation of operating status data;

[0030] The operating status data is uploaded to the main controller via wireless communication. The main controller can readjust the model parameters through the feedback mechanism and start a new round of optimization process.

[0031] Input three-phase current imbalance UIB and line loss P before and after phase switching l Compare the power factor Q data with the data and define a comprehensive evaluation index E;

[0032] The threshold T is defined based on historical data;

[0033] When E≥T, it indicates that the current strategy has achieved the expected results and meets the performance requirements;

[0034] When E < T, it indicates that the current strategy has not achieved the expected results and needs to be readjusted and optimized.

[0035] The analysis results are stored in a structured form, recording the effectiveness of the current strategy, the changes in key feature parameters, and abnormal situations. The analysis results and strategy status are organized into structured feedback data in JSON format and transmitted to the main controller via wireless communication.

[0036] After receiving the feedback data, the main controller stores it in the central database to form a complete operation record.

[0037] Secondly, this invention provides an automatic three-phase load imbalance adjustment device, including a data acquisition and preprocessing module, a load distribution modeling and strategy generation module, a real-time optimization and execution module, a switching operation and monitoring module, and a feedback analysis and optimization module. The data acquisition and preprocessing module collects and preprocesses multi-dimensional data, extracts key characteristic parameters based on the multi-dimensional data, and uploads them to the main controller. The multi-dimensional data includes load current, three-phase voltage, power factor, and temperature. The load distribution modeling and strategy generation module constructs a three-phase load distribution model of the distribution area based on the key characteristic parameters and generates a control strategy by analyzing the three-phase load state index output by the model. The real-time optimization and execution module executes the control strategy and optimizes it based on local real-time data to generate the optimal control strategy. The switching operation and monitoring module performs phase switching operations according to the optimal control strategy, while simultaneously monitoring voltage and current waveforms and generating operating status data to be fed back to the main controller. The feedback analysis and optimization module analyzes the operating status data, records and generates feedback data based on the analysis results, and sends the feedback data back to the main controller to optimize the control strategy.

[0038] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the three-phase load imbalance automatic adjustment method as described in the first aspect of the present invention.

[0039] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the three-phase load imbalance automatic adjustment method as described in the first aspect of the present invention.

[0040] The beneficial effects of this invention are as follows: By constructing a three-phase load distribution model for the distribution area and analyzing operational status data in real time, the accuracy and efficiency of three-phase load imbalance regulation are significantly improved. Effective integration and modeling of multi-dimensional data enhances model prediction accuracy, reduces computational complexity, and strengthens the power grid's adaptability to different scenarios. Simultaneously, through a closed-loop feedback mechanism and comprehensive evaluation indicators, dynamic optimization of the control strategy is achieved, effectively improving the reliability and adaptability of the power grid and avoiding equipment damage or power grid instability caused by improper operation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the automatic adjustment method for three-phase load imbalance in Example 1.

[0043] Figure 2 This is a schematic diagram of the three-phase load imbalance automatic adjustment device in Example 1.

[0044] Figure 3 This is a flowchart of the dynamic optimization and monitoring process in Example 1.

[0045] Figure 4 This is a flowchart of data acquisition and preprocessing in Example 1. Detailed Implementation

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1, referring to Figures 1 to 4This is the first embodiment of the present invention, which provides an automatic adjustment method for three-phase load imbalance, including the following steps:

[0050] S1. Collect multidimensional data and preprocess it. Extract key feature parameters based on the multidimensional data and upload them to the main controller. The multidimensional data includes load current, three-phase voltage, power factor and temperature.

[0051] It should be noted that load current and three-phase voltage are used to assess load balance and power quality, power factor is used to measure power utilization efficiency, and temperature is used to monitor equipment operation safety and thermal stability. These data comprehensively reflect the operating status of the power network from different perspectives, providing rich information support for subsequent key feature parameter extraction, model construction, and optimization strategy generation.

[0052] Preprocessing includes noise reduction, missing value handling, and data normalization.

[0053] It should be noted that denoising refers to removing noise interference from the original data through filtering techniques, while retaining the true and valid signal; missing value processing refers to the process of filling in and correcting invalid values ​​in the dataset to ensure data integrity; and data normalization refers to converting data of different dimensions or numerical ranges to the same standard range to facilitate subsequent calculations and analysis.

[0054] The local controller extracts the three-phase current imbalance by calculating the ratio of the difference between the maximum and minimum three-phase currents to the average value.

[0055] The power factor is extracted by measuring the phase difference between voltage and current;

[0056] Based on the line current and resistance, the line loss is extracted using Joule's law calculation method.

[0057] Key feature parameters are uploaded to the main controller via wireless communication;

[0058] It should be noted that the three-phase current imbalance is an indicator that measures the degree of difference in load current in a three-phase AC circuit. It is used to assess the current load distribution and guide the optimal configuration of the switching switches. The power factor is an indicator that measures the ratio of active power to apparent power in an AC circuit, indicating the effectiveness of the load in utilizing electrical energy. Line loss is the energy loss in the form of heat due to the resistance of the conductors during the transmission of electrical energy, reflecting the efficiency and economy of the power grid operation.

[0059] S2. Construct a three-phase load distribution model for the transformer area based on key characteristic parameters, and generate control strategies by analyzing the three-phase load state index output by the model.

[0060] The main controller organizes the extracted key feature parameters into a unified matrix data structure;

[0061] Based on the data structure, a three-phase load distribution model for the transformer area is constructed to predict the three-phase load state index F(X), which is expressed as:

[0062] F(X)=w1·UIB(X)+w2·L(X)+w3·Q(X);

[0063] Where F(X) is the three-phase load state index, X is the current phase configuration, UIB(X) is the three-phase current imbalance, L(X) is the line loss, Q(X) is the power factor, w1 is the three-phase current imbalance weighting coefficient, w2 is the line loss weighting coefficient, and w3 is the power quality index weighting coefficient.

[0064] It should be noted that the three-phase load distribution model of the distribution area is a core tool for describing the load status of the power network. It quantifies the impact of the current load distribution on the performance of the power network. By calculating the three-phase load status index F(X), it is possible to quickly assess whether the current phase switching configuration is reasonable and help to search for the optimal phase switching configuration scheme.

[0065] The main controller defines the load threshold F based on historical data and optimization goals;

[0066] When F(X) ≥ F, the three-phase load distribution is considered to be out of limit, and the time window T needs to be shortened. base The time will continue until the three-phase load distribution returns to normal.

[0067] If F(X) < F, the three-phase load distribution is considered normal, and the current time window T is maintained. base Time;

[0068] It should be noted that the time window T base This refers to the time interval during which the phase-changing switch performs its switching operation. This time window determines the frequency and timing of control actions. When the three-phase load condition index exceeds the limit, the main controller needs to shorten the time window T. base In order to accelerate the control frequency and improve the control response speed, the target phase configuration of the commutator switch can be adjusted in a timely manner to avoid the continuous deterioration of the imbalance; when the three-phase load condition index is normal, there is no need to shorten the time window T. base We can continue to adjust the time interval as it is currently maintained.

[0069] Based on the analysis results, the target phase configuration X that minimizes the comprehensive evaluation value F(X) of the three-phase load state is found using dynamic programming. * and the switching time window T of each commutator switch base ;

[0070] Based on target phase configuration X* and time window T base Generate control strategy S;

[0071] It should be noted that dynamic programming is an optimization technique used to solve multi-stage decision problems. It decomposes a complex problem into multiple subproblems and obtains the global optimum by recursively solving these subproblems; the objective phase configuration X * This refers to the optimal three-phase load distribution that the main controller aims to achieve. This configuration is calculated using an optimization algorithm to minimize key performance indicators such as three-phase imbalance, line loss, and power factor.

[0072] S3. Execute the control strategy and optimize it based on local real-time data to generate the optimal control strategy.

[0073] The main controller sends the control strategy S to each commutator switch and begins to execute the switching operation;

[0074] The phase switching device collects local three-phase voltage, load current and power factor in real time;

[0075] It should be noted that the main controller sends the control strategy to each commutator switch via wireless communication technology. After receiving the control strategy, the commutator switch executes the control strategy according to the predetermined time window T. base and target phase configuration X * The corresponding switching operation is performed. While performing the switching operation, the phase switching switch continuously monitors and records key parameters such as load current, three-phase voltage and power factor at its location. By comparing the data changes before and after the switching, the control effect can be evaluated and a basis for further optimization can be provided.

[0076] Based on local real-time data, the switching time window T is adjusted. base Optimize and generate a new time window T new ;

[0077] The optimal control strategy includes the target phase configuration X for each commutator switch. * With the adjusted time window T new ;

[0078] It should be noted that the commutation switch continuously monitors local real-time data. By analyzing this data, trends and patterns of load changes can be identified. The commutation switch uses an optimization algorithm to calculate a new time window T based on the real-time data. new .

[0079] S4. Perform the switching operation of the phase switching according to the optimal control strategy, while monitoring the voltage and current waveforms and generating operating status data to be fed back to the main controller.

[0080] The local controller is based on the time window Tnew and target phase configuration X * Send a switching command to the commutator;

[0081] It should be noted that the local controller refers to the control unit located at a local location on the power grid, which is responsible for receiving the control strategy from the main controller and directly commanding the commutation switch to operate.

[0082] Real-time acquisition of voltage and current waveform data, monitoring of changes in current in each phase, monitoring of amplitude and phase angle of voltage in each phase, monitoring of changes in power factor, and generation of operating status data;

[0083] It should be noted that real-time acquisition of voltage and current waveform data provides a comprehensive understanding of the dynamic characteristics of the power grid, such as the presence of harmonic distortion and transient fluctuations. Monitoring changes in phase current helps identify three-phase imbalance problems. Monitoring the amplitude and phase angle of phase voltage helps assess grid quality, such as whether there are issues like excessively high or low voltage or phase asymmetry. Changes in the power factor directly affect the efficiency and cost of the power grid. A lower power factor means more reactive power consumption and increased transmission losses. By monitoring changes in the power factor, measures can be taken to improve energy utilization and reduce operating costs. Operating status data provides a comprehensive view of the power grid's operation, helping to evaluate the effectiveness of control strategies and providing a basis for future optimization. It can be used to detect potential problems, verify grid performance, and support fault diagnosis. By comparing actual switching times and planned time windows, the timing accuracy of the master controller can be evaluated, and any factors that may cause timing deviations can be identified. Long-term recording of the distribution of three-phase loads helps analyze load change trends, providing a reference for future demand forecasting and power grid planning.

[0084] The operating status data is uploaded to the main controller via wireless communication. The main controller can readjust the model parameters through the feedback mechanism and start a new round of optimization process.

[0085] It should be noted that wireless communication technology enables operational status data to be uploaded to the main controller in real time or periodically, ensuring that the main controller can obtain the latest power grid operation information in a timely manner. This method avoids the complexity and cost of wiring and is particularly suitable for remote monitoring and management in distributed power networks. After receiving operational status data from each local controller, the main controller can evaluate the accuracy and effectiveness of the current model based on this data. If a deviation is found between the model prediction and the actual situation, its accuracy can be improved by adjusting the model parameters. Based on the updated model parameters, the main controller initiates a new round of optimization. This continuous optimization cycle helps the power grid adapt to changing load conditions and maintain optimal operating conditions, thereby improving the stability and efficiency of the power grid.

[0086] S5. Analyze the operating status data, record and generate feedback data based on the analysis results, and send the feedback data back to the main controller to optimize the control strategy.

[0087] Input three-phase current imbalance UIB and line loss P before and after phase switching l Compare the data with the power factor Q, and define a comprehensive evaluation index E, the expression of which is:

[0088]

[0089] Where f1(ΔUIB) is the exponentially decaying transformation of the change in three-phase current imbalance, and f2(ΔP) is the exponentially decaying transformation of the change in three-phase current imbalance. l ) is the logarithmic transformation of the change in line loss, f Q (ΔQ) is the Sigmoid transformation of the change in Q, where α represents the weighting coefficient of the three-phase current imbalance with a value of 0.5, β represents the weighting coefficient of the line loss with a value of 0.3, and γ represents the weighting coefficient of the power factor with a value of 0.2.

[0090]

[0091] Where λ1 is the sensitivity parameter of the exponential decay function controlling the change in three-phase current imbalance ΔUIB, λ2 is the sensitivity parameter of the sigmoid function controlling the power factor Q, and δ is the target value of the power factor Q.

[0092] It should be noted that the comprehensive evaluation index E is a single value used to quantify the adjustment effect, which can comprehensively reflect the changes in multiple indicators such as three-phase current imbalance, line loss and power factor.

[0093] The threshold T is defined based on historical data;

[0094] Analysis results are generated based on the comprehensive evaluation index E and the threshold T;

[0095] When E≥T, it indicates that the current strategy has achieved the expected results and meets the performance requirements;

[0096] When E < t, it indicates that the current strategy has not achieved the expected results and needs to be readjusted and optimized.

[0097] It should be noted that the threshold T is a standard value used to judge whether the optimization strategy has achieved the expected results, and it reflects the minimum performance target of the power grid.

[0098] The analysis results are stored in a structured form, recording the effectiveness of the current strategy, the changes in key feature parameters, and abnormal situations. The analysis results and strategy status are organized into structured feedback data in JSON format and transmitted to the main controller via wireless communication.

[0099] After receiving the feedback data, the main controller stores it in the central database to form a complete operation record.

[0100] It should be noted that structured format refers to organizing data according to a certain logic into a format that is easy to parse and use. This format facilitates subsequent data processing, analysis, and storage, ensures data integrity and consistency, and provides reliable feedback information to the controller.

[0101] This embodiment also provides a three-phase load imbalance automatic adjustment device, including: a data acquisition and preprocessing module, a load distribution modeling and strategy generation module, a real-time optimization and execution module, a switching operation and monitoring module, and a feedback analysis and optimization module;

[0102] The data acquisition and preprocessing module is used to collect multidimensional data and perform preprocessing, extract key feature parameters based on the multidimensional data and upload them to the main controller. The multidimensional data includes load current, three-phase voltage, power factor and temperature.

[0103] The load distribution modeling and strategy generation module is used to construct a three-phase load distribution model of the transformer area based on key characteristic parameters, and generate control strategies by analyzing the three-phase load state index output by the model.

[0104] The real-time optimization and execution module is used to execute the control strategy and optimize it by combining local real-time data to generate the optimal control strategy.

[0105] The switching operation and monitoring module is used to perform the switching operation of the commutator according to the optimal control strategy, while monitoring the voltage and current waveforms and generating operating status data to be fed back to the main controller.

[0106] The feedback analysis and optimization module is used to analyze operating status data, record and generate feedback data based on the analysis results, and send the feedback data back to the main controller to optimize the control strategy.

[0107] This embodiment also provides a computer device applicable to the three-phase load imbalance automatic adjustment method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the three-phase load imbalance automatic adjustment method proposed in the above embodiment.

[0108] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0109] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the automatic adjustment method for three-phase load imbalance as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0110] In summary, this invention significantly improves the accuracy and efficiency of three-phase load imbalance regulation by constructing a three-phase load distribution model for the distribution area and analyzing operational status data in real time. Through effective integration and modeling of multi-dimensional data, it enhances the model's predictive accuracy, reduces computational complexity, and strengthens the power grid's adaptability to different scenarios. Simultaneously, by leveraging a closed-loop feedback mechanism and comprehensive evaluation indicators, it achieves dynamic optimization of the control strategy, effectively improving the power grid's reliability and adaptability, and preventing equipment damage or power grid instability caused by improper operation.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for automatic regulation of three-phase load imbalance, characterized by: Comprise, Collect and preprocess multi-dimensional data, extract key feature parameters based on multi-dimensional data, and upload to the host controller, the multi-dimensional data including load current, three-phase voltage, power factor and temperature; Construct a three-phase load distribution model of the transformer area based on the key feature parameters, generate a control strategy by analyzing the three-phase load state index output by the model; the specific steps of constructing a three-phase load distribution model of the transformer area based on the key feature parameters are as follows, The host controller arranges the extracted key feature parameters into a unified matrix form data structure; According to the data structure, a three-phase load distribution model of a transformer area is constructed, and a three-phase load state index is predicted ; The expression is: ； wherein is a three-phase load condition index, is a current phase configuration, is a three-phase current unbalance degree, is a line loss, is a power factor, is a three-phase current unbalance degree weight coefficient, a weight coefficient, is a power quality index weight coefficient; The specific steps of generating a control strategy by analyzing the three-phase load state index output by the model are as follows, The host defines the load threshold according to historical data and optimization target ; When ≥ If the three-phase load distribution is considered to be out of limits, the time window is shortened of the time; When < the three-phase load distribution is considered normal, the current time window is maintained for a certain time; Based on the analysis result, the target phase configuration that minimizes the three-phase minimization is found by a dynamic programming method and the switching time window of each phase-change switch ; Target phase configuration And time window Generating a control policy ; Execute the control strategy and optimize it in combination with local real-time data to generate an optimal control strategy; which includes, The master will regulate the strategy The switching operation is started by sending a command to each phase changer. The phase-change switch collects local three-phase voltage, load current and power factor in real time; The main controller sends the regulation strategy to each commutator switch through wireless communication technology. After receiving the regulation strategy, the commutator switch executes the corresponding switching operation according to the predetermined time window and target phase configuration , and continuously monitors and records the load current, three-phase voltage and power factor at the location of the commutator switch while executing the switching operation. Switching time window is optimized according to local real-time data to generate a new time window ; The optimal control strategy includes a target phase configuration for each commutation switch with the adjusted time window ; The commutation switch continuously monitors local real-time data, and through analysis of these data, identifies trends and patterns in load changes. The commutation switch uses an optimization algorithm to calculate new time windows based on real-time data ; According to the optimal control strategy, the switching operation of the phase-change switch is performed, and the voltage and current waveforms are monitored to generate operation state data and feed back to the host controller; Analyze the operation state data, record and generate feedback data according to the analysis results, and return the feedback data to the host controller to optimize the control strategy.

2. The three-phase load imbalance automatic regulation method of claim 1, wherein: The preprocessing includes denoising, missing value processing and data normalization.

3. The three-phase load imbalance automatic regulation method of claim 1, wherein: The specific steps of extracting key feature parameters based on multi-dimensional data and uploading to the host controller are as follows, The local controller extracts the three-phase current imbalance by calculating the difference between the maximum and minimum values of the three-phase current relative to the average value; The power factor is extracted by measuring the phase difference between the voltage and the current; According to the line current and resistance, the line loss is calculated by the Joule law; The key feature parameters are uploaded to the host controller through wireless communication.

4. The method of claim 1, wherein: the three-phase load imbalance automatic adjustment is performed by a controller. The specific steps of performing switching operation of the phase-change switch according to the optimal control strategy, monitoring voltage and current waveforms, generating operation state data and feeding back to the host controller, analyzing operation state data, recording and generating feedback data according to the analysis results, and returning the feedback data to the host controller to optimize the control strategy are as follows, The local controller sends switching instructions to the commutation switch according to a time window and a target phase configuration , to the commutation switch; Real-time acquisition of voltage and current waveform data, monitoring of the change of each phase current, monitoring of the amplitude and phase angle of each phase voltage, monitoring of the change of power factor, and generation of operation state data; The operation state data is uploaded to the host controller through wireless communication, and the host controller can adjust the model parameters through the feedback mechanism and start a new round of optimization process; Three-phase current unbalance degree before and after input switching phase , line loss , and power factor Data are compared and a comprehensive evaluation index is defined ; The expression is: ； wherein, is an exponential decay transformation on the change amount of three-phase current unbalance degree, is a logarithmic transformation on the change amount of line loss, is a Sigmoid transformation on the change amount of represents the weight coefficient of three-phase current unbalance degree, and the value is 0.5, represents the weight coefficient of line loss, and the value is 0.3, represents the weight coefficient of power factor, and the value is 0.2;​ ， ， ； wherein is a sensitivity parameter of an exponential decay function controlling the amount of change of the three-phase current unbalance degree is a sensitivity parameter of a Sigmoid function controlling the power factor is a target value of the power factor ​​​ Defining a threshold value from historical data ; When ≥ , it indicates that the current strategy achieves the expected effect and meets the performance requirements; When < , indicating that the current strategy does not achieve the desired effect and needs to be adjusted and optimized again; The analysis results are stored in a structured form, the effectiveness of the current strategy is recorded, the change value of the key feature parameters is recorded, the abnormal situation is recorded, and the analysis results and strategy state are arranged into structured feedback data in JSON form and transmitted to the host controller through wireless communication; After receiving the feedback data, the host controller stores it in the central database to form a complete operation record.

5. A three-phase load unbalance automatic regulating device based on the three-phase load unbalance automatic regulating method according to any one of claims 1 to 4, characterized in that: Comprise, data acquisition and preprocessing module, load distribution modeling and strategy generation module, real-time optimization and execution module, switching operation and monitoring module, and feedback analysis and optimization module; A data acquisition and preprocessing module is configured to collect and preprocess multi-dimensional data, extract key characteristic parameters based on the multi-dimensional data, and upload the key characteristic parameters to a main controller. The multi-dimensional data includes load current, three-phase voltage, power factor, and temperature. A load distribution modeling and strategy generation module is configured to construct a three-phase load distribution model of a transformer area based on the key characteristic parameters, analyze three-phase load state indexes output by the model, and generate a regulation strategy. A real-time optimization and execution module is configured to execute the regulation strategy, optimize the regulation strategy in combination with local real-time data, and generate an optimal regulation strategy. A switching operation and monitoring module is configured to perform switching operation of a phase-change switch according to the optimal regulation strategy, monitor voltage and current waveforms, and generate operation state data and feed back the operation state data to the main controller. A feedback analysis and optimization module is configured to analyze the operation state data, record and generate feedback data according to an analysis result, and feed back the feedback data to the main controller to optimize the regulation strategy. The processor executes the computer program to implement the steps of the three-phase load imbalance automatic regulation method according to any one of claims 1-4. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The computer program is executed by the processor to implement the steps of the three-phase load imbalance automatic regulation method according to any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: ​

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