Fault location communication method and system for primary and secondary integrated ring network box

Abstract

The present invention discloses a primary-secondary integrated ring network box fault location communication method and system, which relate to the technical field of power system fault location. The specific steps of the communication method are: S100, multi-source parameter collaborative acquisition: through the electronic multi-function sensor built into the ring network box, the current, voltage and power factor parameters of each circuit are collected, and at the same time, the smart meter with communication function and the wireless data acquisition terminal based on the Internet of Things technology are used to obtain the operating parameters of the flexible load of the charging pile and the air-conditioning cluster, and the data is transmitted to the server for format conversion and outlier elimination; the present invention realizes real-time and accurate acquisition of key parameters of current, voltage and power factor of each circuit of the ring network box through multi-source parameter collaborative acquisition technology, and at the same time, combines the smart meter with communication function and the wireless data acquisition terminal based on the Internet of Things technology to comprehensively monitor the operating parameters of the flexible load, thereby improving the comprehensiveness and accuracy of data acquisition.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault location, and in particular to a primary-secondary integrated ring network box fault location communication method and system. Background Art

[0002] With the rapid development of smart grid technology, the automation and intelligence levels of distribution networks are constantly improving, which puts higher requirements on the safety and reliability of grid operation. As a key equipment in the distribution network, the operating status of the ring main box directly affects the power supply quality and stability of the entire grid. However, the ring main box may be affected by many factors during operation, such as equipment aging, external interference, and overload operation, resulting in frequent faults. Traditional fault location methods often rely on manual inspections and experience judgments, which are inefficient and inaccurate, and cannot meet the needs of modern smart grids for rapid fault location and processing. Therefore, developing an efficient and accurate communication method for ring main box fault location is of great significance to improving the operational safety and reliability of the distribution network.

[0003] Traditional ring main network box fault location technology has the following main shortcomings: First, the data collection method is single and often only limited operating parameters can be obtained, making it difficult to fully reflect the operating status of the ring main network box; second, the fault diagnosis method relies on manual experience and fixed rules, lacking intelligence and adaptive capabilities, resulting in long fault location time and low accuracy; third, the ability to regulate flexible loads is insufficient, and these resources cannot be fully utilized to improve the stability and operating efficiency of the power grid; finally, traditional technology often ignores the importance of reactive power compensation, resulting in low power factor and large transmission losses in the power grid, which increases the operating cost of the power grid. Therefore, traditional technology can no longer meet the high requirements of modern smart grids for fault location and processing.

[0004] Therefore, the development of a primary-secondary integrated ring network box fault location communication method and system will provide strong support for the stable operation and intelligent management of the distribution network. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a primary and secondary integrated ring network box fault location communication method and system. Through multi-source parameter collaborative acquisition technology, the operating data of the ring network box and flexible load are comprehensively acquired. The linkage model intelligent diagnosis module intelligently determines the fault type based on historical data and real-time parameters. The flexible load precise control module generates and executes control instructions according to the fault type. The fault verification and positioning optimization module accurately locks the fault location by monitoring the power factor change. The reactive collaborative intelligent compensation module optimizes the reactive compensation strategy and improves the power factor of the power grid.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a primary and secondary integrated ring network box fault location communication method, the specific steps of the communication method are:

[0007] S100, multi-source parameter collaborative acquisition: The ring network box uses built-in electronic multi-function sensors to collect current, voltage, and power factor parameters of each circuit. It also uses smart meters with communication capabilities and wireless data acquisition terminals based on IoT technology to obtain operating parameters of flexible loads such as charging piles and air conditioning clusters. The data is then transmitted to the server for format conversion and outlier removal.

[0008] S200, Linkage Model Intelligent Diagnosis: Collects historical fault and flexible load control data, calculates the weight of each monitoring parameter under different fault types, and calculates the probability of each fault type based on the real-time collected parameters to determine the current fault type;

[0009] S300, precise control of flexible loads: Based on the fault determination results, the responsiveness of flexible loads of different types is calculated by combining their type adjustment coefficient, operating status coefficient, and adjustable reactive power change, and control instructions are generated based on the responsiveness of the flexible loads.

[0010] S400, fault verification and location optimization: performs flexible load control and monitors the power factor changes of each circuit in the ring network box. When the power factor of a circuit returns to normal, it is locked as the fault-related circuit. The current phase and voltage harmonic parameters are compared with historical fault data to locate the specific fault location.

[0011] S500, Reactive Power Collaborative Intelligent Compensation: This system treats flexible loads as dynamic reactive sources, combines fault probability and flexible load responsiveness to construct a power factor optimization objective function, and uses a particle swarm optimization algorithm to calculate and adjust the flexible load regulation and reactive power compensation device control parameters.

[0012] Furthermore, in the step S100, the sensors selected for the multi-source parameter collaborative acquisition are:

[0013] Ring network box loop parameter acquisition sensor: This sensor uses an electronic multifunctional sensor that integrates a Rogowski coil current sensor, a capacitor voltage divider voltage sensor, and a power factor measurement module. It collects real-time current, voltage, and power factor values ​​for each loop, and supports the calculation and output of apparent power, active power, and reactive power.

[0014] Charging pile parameter collection equipment: Use a smart meter with communication function to collect parameters such as charging power, cumulative charging power, current charging current, charging voltage, equipment start and stop status, and charging mode;

[0015] Air conditioning cluster parameter collection IoT terminal: A wireless data collection terminal based on IoT technology is used to collect the set temperature, actual operating temperature, compressor start and stop status, fan speed gear, real-time operating power and working mode of each air conditioner.

[0016] Furthermore, in the S200, the weights of different fault types in the linkage model intelligent diagnosis are calculated using the following formula: ,in, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the Under the fault type The coefficient of variation of each parameter reflects the degree of fluctuation of the parameter under the fault type. For the The difference index of a parameter between normal operation and fault state, is the total number of fault types, is the index variable for the sum operation, An index representing the fault type, Indicates the index of the monitoring parameter.

[0017] Furthermore, in the S200, the fault type is determined in the linkage model intelligent diagnosis, and the formula is: , where, indicates the occurrence of The probability of a failure, is the activation function, which is used to map the weighted parameter value to the fault probability interval. The actual monitored Under the fault type The value of the parameter, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the total number of monitored parameters, calculate the probability of all fault types and select the probability The biggest failure currently occurring is An index representing the fault type, Indicates the index of the monitoring parameter.

[0018] Furthermore, in the S300, the calculation formula for the flexible load responsiveness in the flexible load precise control is: ,in, Indicates the Responsiveness of quasi-flexible loads, is the load type adjustment coefficient, which reflects the difficulty of adjusting different loads. For the The reactive power variation of the flexible load can be adjusted. The maximum reactive power regulation allowed by the system for this type of load, is the load operating state coefficient, which is determined according to the adjustment responsiveness of the current load operating state. It is a mark used to distinguish different types of flexible loads.

[0019] Furthermore, in the above S400, the current fault location is determined by a fault location fusion determination formula in the fault verification and location optimization, and the calculation formula is: ,in is the probability score of the kth monitoring point being a fault point, is the number of time windows for monitoring after regulation, 、 、 are the weight coefficients of power factor, current phase, and voltage harmonics, respectively, and , is the power factor change of the kth monitoring point at time t, is the reference power factor change of the same type of historical fault at time t, is the current phase change, is the reference current phase change, is the change in voltage harmonic distortion rate, is the change in the reference voltage harmonic distortion rate, It is The electrical distance between each monitoring point and the circuit associated with the initial locked fault, is the spatial attenuation coefficient, which controls the influence of distance on the score. Indicates the time point in the monitoring process after regulation.

[0020] Furthermore, in S500, the objective function for optimizing the power factor in the reactive power coordinated intelligent compensation is constructed as follows: ,in, is a set of control strategy variables, including flexible load adjustment and reactive compensation equipment control parameters. Indicates the The weight of quasi-flexible load in reactive power compensation, for The actual power factor at the moment, is the target power factor, R is the duration of the regulation cycle, is the time index within the control cycle, is the weight coefficient, balancing the power factor optimization and reactive power regulation costs, No. The weight of quasi-flexible load in reactive power compensation, It is Reactive power regulation of flexible loads, is the total number of flexible load types, is the index of the flexible load type.

[0021] Furthermore, the specific steps of calculating the flexible load adjustment amount and the reactive compensation device control parameters by using the particle swarm optimization algorithm in the reactive collaborative intelligent compensation in S500 are as follows:

[0022] (1) Initialize the particle swarm, set the number of particles, randomly assign positions and velocities to each particle, and determine the individual and global optimal positions;

[0023] (2) Substitute the parameter combination represented by the particle into the objective function and calculate the fitness value to measure the quality of the solution;

[0024] (3) Compare the current fitness value of the particle with the individual's optimal fitness value, and update the individual's optimal position if it is better;

[0025] (4) Compare the fitness values ​​of all particles with the global optimal value, and update the global optimal position if it is better;

[0026] (5) Update particle speed and position based on the rules and combining individual and global optimal information;

[0027] Determine whether the maximum number of iterations has been reached or the solution has stabilized. If so, output the optimal parameters; otherwise, continue iterating.

[0028] On the other hand, the primary and secondary integrated ring network box fault location communication system includes: multi-source parameter collaborative acquisition module, linkage model intelligent diagnosis module, flexible load precise control module, fault verification and location optimization module and reactive power collaborative intelligent compensation module;

[0029] The multi-source parameter collaborative acquisition module collects the current, voltage, and power factor parameters of each circuit through the electronic multi-function sensor built into the ring network box. It also uses smart meters with communication functions and wireless data acquisition terminals based on Internet of Things technology to obtain the flexible load operating parameters of the charging piles and air conditioning clusters. This data is then transmitted to the server for format conversion and outlier removal.

[0030] The linkage model intelligent diagnosis module collects historical fault and flexible load control data, calculates the weight of each monitoring parameter under different fault types, and then calculates the probability of each fault type based on the parameters collected in real time to determine the current fault type;

[0031] The flexible load precise control module calculates the responsiveness of the flexible load based on the fault determination result and generates a control instruction based on the responsiveness, taking into account the type adjustment coefficient, operating state coefficient, and adjustable reactive power variation of different types of flexible loads.

[0032] The fault verification and location optimization module monitors the changes in the power factor of each circuit of the ring network box. When the power factor of a circuit returns to normal, it is locked as a fault-related circuit. The current phase and voltage harmonic parameters are compared with historical fault data to locate the specific location of the fault.

[0033] The reactive collaborative intelligent compensation module treats flexible loads as dynamic reactive sources, combines fault probability and flexible load responsiveness, calculates the weights of various flexible loads in reactive compensation to construct a power factor optimization objective function, and calculates and adjusts the flexible load regulation amount and reactive compensation equipment control parameters using a particle swarm optimization algorithm.

[0034] Compared with the existing technology, the primary and secondary integrated ring network box fault location communication method and system has the following beneficial effects:

[0035] 1. The present invention realizes the real-time and accurate collection of key parameters of current, voltage and power factor of each loop of the ring network box through multi-source parameter collaborative collection technology. At the same time, combined with smart meters with communication functions and wireless data collection terminals of Internet of Things technology, the operating parameters of flexible loads are comprehensively monitored, which not only improves the comprehensiveness and accuracy of data collection, but also provides a rich data basis for subsequent fault diagnosis. The linkage model intelligent diagnosis module collects historical fault data and combines it with real-time collected parameters to calculate the weights of each monitoring parameter under different fault types, and then determines the current fault type, shortening the fault location time. In addition, the flexible load precise control module generates control instructions according to the fault judgment results, and accurately controls different types of flexible loads, effectively reducing the impact of faults on power grid operation and improving the stability and reliability of the power grid.

[0036] 2. The present invention treats flexible loads as dynamic reactive sources, combines fault probability and flexible load responsiveness, calculates the weights of various flexible loads in reactive compensation, constructs a power factor optimization objective function, and calculates the flexible load adjustment amount and reactive compensation equipment control parameters through the particle swarm optimization algorithm, thereby realizing intelligent and refined reactive compensation. It not only improves the efficiency and accuracy of reactive compensation, but also reduces the operating cost of the power grid. By optimizing the reactive compensation strategy, it reduces the transmission loss of reactive power in the power grid, improves the power factor of the power grid, and further improves the economy and operating efficiency of the power grid. At the same time, the module also takes into account the adjustment capability and responsiveness of the flexible load, making reactive compensation more flexible and efficient, and providing strong support for the intelligent and sustainable development of the power grid.

[0037] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0039] Figure 1 This is a framework diagram of a fault location communication method for a primary / secondary integrated ring network box;

[0040] Figure 2 This is a flow chart of the fault location communication system for the primary and secondary integrated ring network box. DETAILED DESCRIPTION

[0041] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. Example

[0042] Fault location and troubleshooting of primary and secondary integrated ring network boxes in commercial complexes.

[0043] The power supply system of a certain urban commercial complex is equipped with a primary and secondary integrated ring network box to supply power to the charging piles, air conditioning system flexible loads, and conventional electrical equipment in the mall. Because the mall's power load is complex and fluctuates greatly, it is necessary to quickly locate and handle possible faults in the ring network box to ensure power supply reliability.

[0044] S100, multi-source parameter collaborative acquisition: The ring network box has a built-in electronic multi-function sensor that continuously collects the current, voltage and power factor values ​​of each power supply circuit, and calculates and outputs the parameters of apparent power, active power and reactive power, such as Figure 1 As shown in the figure, the smart meter with communication function equipped for the shopping mall charging pile collects the charging power, cumulative charging power, charging current, charging voltage, equipment start and stop status and charging mode operating parameters in real time; the wireless data acquisition terminal based on Internet of Things technology collects the set temperature, actual operating temperature, compressor start and stop status, fan speed gear, real-time operating power and working mode parameters of each air conditioner in the shopping mall air-conditioning cluster. All collected data is transmitted to the server, which converts the data format and eliminates abnormal data.

[0045] S200, linkage model intelligent diagnosis, collects historical fault data of the ring network box and flexible load control data in the mall, and uses the weight calculation formula to calculate the weight of each monitoring parameter under different fault types. The calculation formula is: ,in, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the Under the fault type The coefficient of variation of each parameter reflects the degree of fluctuation of the parameter under the fault type. For the The difference index of a parameter between normal operation and fault state, is the total number of fault types, is the index variable for the sum operation, An index representing the fault type, Represents the index of the monitoring parameter. This formula comprehensively considers the fluctuation degree of the parameter under the fault type and the difference index m between normal operation and fault state. Combined with the parameters collected in real time, the fault probability calculation formula is used to calculate the probability of each type of fault. The formula is: ,in, Indicates the occurrence of The probability of a failure, is the activation function, which is used to map the weighted parameter value to the fault probability interval. The actual monitored Under the fault type The value of the parameter, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the total number of monitored parameters, calculate the probability of all fault types and select the probability The biggest failure currently occurring is An index representing the fault type, Indicates the index of the monitoring parameter and selects the fault type with the highest probability as the current fault.

[0046] S300, precise control of flexible loads. Based on the fault determination results, for different types of flexible loads such as charging piles and air conditioning clusters in the mall, the responsiveness of the flexible load is calculated using the responsiveness calculation formula, taking into account the load type adjustment coefficient, operating status coefficient, and adjustable reactive power change. The calculation formula is: ,in, Indicates the Responsiveness of quasi-flexible loads, is the load type adjustment coefficient, which reflects the difficulty of adjusting different loads. For the The reactive power variation of the flexible load can be adjusted. The maximum reactive power regulation allowed by the system for this type of load, is the load operating state coefficient, which is determined according to the adjustment responsiveness of the current load operating state. It is used to identify different types of flexible loads and generate control instructions based on the calculated responsiveness to achieve precise control of the flexible load. S400, fault verification and location optimization, after controlling the flexible load, continuously monitors the changes in the power factor of each circuit of the ring network box. When the power factor of a circuit returns to normal, it is locked as a fault-related circuit. Combined with the current phase and voltage harmonic parameters, it is compared with historical fault data and the fault location fusion judgment formula is used to determine the specific location of the current fault. The calculation formula is: ,in is the probability score of the kth monitoring point being a fault point, is the number of time windows for monitoring after regulation, 、 、 are the weight coefficients of power factor, current phase, and voltage harmonics, respectively, and , is the power factor change of the kth monitoring point at time t, is the reference power factor change of the same type of historical fault at time t, is the current phase change, is the reference current phase change, is the change in voltage harmonic distortion rate, is the change in the reference voltage harmonic distortion rate, It is The electrical distance between each monitoring point and the circuit associated with the initial locked fault, is the spatial attenuation coefficient, which controls the influence of distance on the score. Indicates the time point in the monitoring process after regulation.

[0047] S500, reactive power coordinated intelligent compensation, treats the flexible loads in the mall as dynamic reactive sources. It combines the fault probability and flexible load responsiveness to construct a power factor optimization objective function. The formula is: ,in, is a set of control strategy variables, including flexible load adjustment and reactive compensation equipment control parameters. Indicates the The weight of quasi-flexible load in reactive power compensation, for The actual power factor at the moment, is the target power factor, R is the duration of the regulation cycle, is the time index within the control cycle, is the weight coefficient, balancing the power factor optimization and reactive power regulation costs, No. The weight of quasi-flexible load in reactive power compensation, It is Reactive power regulation of flexible loads, is the total number of flexible load types, is the index of the flexible load type. This function aims to balance the power factor optimization and reactive power regulation cost. It calculates the flexible load regulation amount and reactive power compensation equipment control parameters through the particle swarm optimization algorithm and adjusts them to achieve reactive power coordinated intelligent compensation.

[0048] In summary, in the commercial complex scenario, this method constructs a three-dimensional monitoring network through the collaborative collection of multi-source parameters, identifies faults using the weight calculation and fault probability calculation in the linkage model intelligent diagnosis, realizes differentiated regulation with the help of the responsiveness calculation of the flexible load precise control, and locks the fault location by relying on the fusion judgment of fault verification and positioning optimization. Finally, a closed loop is formed through the objective function of reactive collaborative intelligent compensation and particle swarm optimization. The entire solution efficiently handles ring network box faults and ensures power supply continuity under complex loads. Example

[0049] Application of fault location communication system for primary and secondary integrated ring network boxes in industrial parks.

[0050] In an industrial park, a primary / secondary integrated ring main network (RMN) box provides power to production equipment, charging stations, and air conditioning systems within the park. Due to the large industrial load and diverse electrical equipment within the park, high fault location and handling efficiency are required of the RMN box to reduce production losses caused by faults.

[0051] Multi-source parameter collaborative acquisition module: The electronic multi-function sensor in the module is installed in the ring network box to collect the current, voltage and power factor parameters of each circuit, and has the function of calculating and outputting apparent power, active power and reactive power; the charging piles in the park are equipped with smart meters with communication functions to collect parameters such as charging power, cumulative charging power, charging current, charging voltage, equipment start and stop status and charging mode; the wireless data acquisition terminal based on Internet of Things technology collects the set temperature, actual operating temperature, compressor start and stop status, fan speed gear, real-time operating power and working mode parameters of each air conditioner in the park air conditioning cluster, and transmits the collected data to the server, which completes the data format conversion and eliminates abnormal values, such as Figure 2 shown.

[0052] Linkage model intelligent diagnosis module: The module collects historical fault data of the ring network box during operation in the industrial park, as well as the control data of the flexible load in the park. It uses the weight calculation formula to determine the weight of each monitoring parameter under different fault types. The calculation formula is: This formula takes into account the parameter fluctuation degree and state difference, and combines the parameters collected in real time to calculate the probability of each type of fault through the fault probability calculation formula. The calculation formula is: , thereby determining the current fault type.

[0053] Flexible load precise control module: Based on the fault results determined by the linkage model intelligent diagnosis module, the responsiveness of the flexible loads, such as charging piles and air conditioners, is calculated using the responsiveness calculation formula based on the load type adjustment coefficient, operating status coefficient, and adjustable reactive power change within the park. The calculation formula is: , and generates control instructions based on the responsiveness to achieve precise control of flexible loads.

[0054] Fault Verification and Location Optimization Module: After regulating the flexible load, the module continuously monitors the power factor changes of each circuit in the ring network box. When the power factor of a circuit is found to have returned to normal, it is identified as a fault-related circuit. The fault location is determined by combining the current phase and voltage harmonic parameters with historical fault data and applying the fault location fusion judgment formula. The calculation formula is: .

[0055] Reactive power collaborative intelligent compensation module: The flexible loads in the park are equivalent to dynamic reactive power sources. The power factor optimization objective function is constructed by combining the fault probability and flexible load responsiveness. The calculation formula is: , in order to balance the power factor optimization and reactive power regulation costs, and through the particle swarm optimization algorithm, calculate the flexible load regulation amount and reactive power compensation equipment control parameters, and adjust them to achieve reactive power collaborative intelligent compensation and ensure the stable operation of the park power supply system.

[0056] In summary, the industrial park's system uses an industrial-grade multi-source parameter collaborative acquisition module for anti-interference monitoring, uses the linkage model's intelligent diagnosis weights and fault probability calculations to identify industrial-specific reactive power, generates friendly strategies with the help of responsiveness calculations for precise control of flexible loads, and rapidly locates faults using a fusion of fault verification and location optimization. Efficient compensation is achieved through the objective function of reactive collaborative intelligent compensation and particle swarm optimization, forming an integrated solution to improve the park's power supply reliability.

[0057] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for locating faults in a primary / secondary integrated ring network box, characterized in that: The specific steps of the communication method are: S100, multi-source parameter collaborative acquisition: The ring network box uses built-in electronic multi-function sensors to collect current, voltage, and power factor parameters of each circuit. It also uses smart meters with communication capabilities and wireless data acquisition terminals based on IoT technology to obtain operating parameters of flexible loads such as charging piles and air conditioning clusters. The data is then transmitted to the server for format conversion and outlier removal. S200, Linkage Model Intelligent Diagnosis: Collects historical fault and flexible load control data, calculates the weight of each monitoring parameter under different fault types, and calculates the probability of each fault type based on the real-time collected parameters to determine the current fault type; S300, precise control of flexible loads: Based on the fault determination results, the responsiveness of flexible loads of different types is calculated by combining their type adjustment coefficient, operating status coefficient, and adjustable reactive power change, and control instructions are generated based on the responsiveness of the flexible loads. S400, Fault Verification and Location Optimization: Performs flexible load control and monitors the power factor changes of each loop in the ring network box. When the power factor of a loop returns to normal, it is locked as a fault-related loop. Combined with the current phase and voltage harmonic parameters and compared with historical fault data, the current fault location is determined using the fault location fusion judgment formula. The calculation formula is: ,in is the probability score of the kth monitoring point being a fault point, is the number of time windows for monitoring after regulation, 、 、 are the weight coefficients of power factor, current phase, and voltage harmonics, respectively, and , is the power factor change of the kth monitoring point at time t, is the reference power factor change of the same type of historical fault at time t, is the current phase change, is the reference current phase change, is the change in voltage harmonic distortion rate, is the change in the reference voltage harmonic distortion rate, It is The electrical distance between each monitoring point and the circuit associated with the initial locked fault, is the spatial attenuation coefficient, which controls the influence of distance on the score. Indicates the time point in the monitoring process after regulation; S500, reactive collaborative intelligent compensation: the flexible load is equivalent to a dynamic reactive source, and the power factor optimization objective function is constructed by combining the fault probability and the flexible load responsiveness. The calculation formula of the power factor optimization objective function is: ,in, is a set of control strategy variables, including flexible load adjustment and reactive compensation equipment control parameters. Indicates the The weight of quasi-flexible load in reactive power compensation, for The actual power factor at the moment, is the target power factor, R is the duration of the regulation cycle, is the time index within the control cycle, is the weight coefficient, balancing the power factor optimization and reactive power regulation costs, No. The weight of quasi-flexible load in reactive power compensation, It is Reactive power regulation of flexible loads, is the total number of flexible load types, It is the index of the flexible load type, and the flexible load adjustment amount and reactive compensation equipment control parameters are calculated and adjusted through the particle swarm optimization algorithm.

2. The method for locating and communicating faults in a primary / secondary integrated ring network box according to claim 1, characterized in that: In step S100, the sensors selected for the multi-source parameter collaborative acquisition are: Ring network box loop parameter acquisition sensor: This sensor uses an electronic multifunctional sensor that integrates a Rogowski coil current sensor, a capacitor voltage divider voltage sensor, and a power factor measurement module. It collects real-time current, voltage, and power factor values ​​for each loop, and supports the calculation and output of apparent power, active power, and reactive power. Charging pile parameter collection equipment: Use a smart meter with communication function to collect parameters such as charging power, cumulative charging power, current charging current, charging voltage, equipment start and stop status, and charging mode; Air conditioning cluster parameter collection IoT terminal: A wireless data collection terminal based on IoT technology is used to collect the set temperature, actual operating temperature, compressor start and stop status, fan speed gear, real-time operating power and working mode of each air conditioner.

3. The method for locating and communicating faults in a primary / secondary integrated ring network box according to claim 1, characterized in that: In step S200, the weights of different fault types in the linkage model intelligent diagnosis are calculated using the following formula: ,in, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the Under the fault type The coefficient of variation of each parameter reflects the degree of fluctuation of the parameter under the fault type. For the The difference index of a parameter between normal operation and fault state, is the total number of fault types, is the index variable for the sum operation, An index representing the fault type, Indicates the index of the monitoring parameter.

4. The method for locating and communicating faults in a primary / secondary integrated ring network box according to claim 3, characterized in that: In step S200, the fault type is determined in the linkage model intelligent diagnosis. The formula is: ,in, Indicates the occurrence of The probability of a failure, is the activation function, which is used to map the weighted parameter value to the fault probability interval. The actual monitored Under the fault type The value of the parameter, Indicates the Under the fault type The weight coefficient of each monitoring parameter, For the total number of monitored parameters, calculate the probability of all fault types and select the probability The biggest failure currently occurring is An index representing the fault type, Indicates the index of the monitoring parameter.

5. The method for locating and communicating faults in a primary-secondary integrated ring network box according to claim 1, characterized in that: The calculation formula for the flexible load responsiveness in the flexible load precise control in step S300 is: ,in, Indicates the Responsiveness of quasi-flexible loads, is the load type adjustment coefficient, which reflects the difficulty of adjusting different loads. For the The reactive power variation of the flexible load can be adjusted. The maximum reactive power regulation allowed by the system for this type of load, is the load operating state coefficient, which is determined according to the adjustment responsiveness of the current load operating state. It is a mark used to distinguish different types of flexible loads.

6. The method for locating and communicating faults in a primary / secondary integrated ring network box according to claim 1, characterized in that: The specific steps of calculating the flexible load adjustment amount and the reactive compensation device control parameters by using the particle swarm optimization algorithm in S500 in the reactive collaborative intelligent compensation are as follows: Initialize the particle swarm, set the number of particles, randomly assign positions and velocities to each particle, and determine the individual and global optimal positions; Substitute the parameter combination represented by the particle into the objective function and calculate the fitness value to measure the quality of the solution; Compare the particle's current fitness value with the individual's optimal fitness value, and if it is better, update the individual's optimal position; Compare the fitness values ​​of all particles with the global optimal value, and update the global optimal position if it is better; According to the rules, the particle speed and position are updated by combining individual and global optimal information; Determine whether the maximum number of iterations has been reached or the solution has stabilized. If so, output the optimal parameters; otherwise, continue iterating.

7. A primary-secondary integrated ring network box fault location communication system, the system being applicable to the primary-secondary integrated ring network box fault location communication method according to any one of claims 1 to 6, characterized in that: The system includes: multi-source parameter collaborative acquisition module, linkage model intelligent diagnosis module, flexible load precise control module, fault verification and positioning optimization module and reactive collaborative intelligent compensation module; The multi-source parameter collaborative acquisition module collects the current, voltage, and power factor parameters of each circuit through the electronic multi-function sensor built into the ring network box. It also uses smart meters with communication functions and wireless data acquisition terminals based on Internet of Things technology to obtain the flexible load operating parameters of the charging piles and air conditioning clusters. This data is then transmitted to the server for format conversion and outlier removal. The linkage model intelligent diagnosis module collects historical fault and flexible load control data, calculates the weight of each monitoring parameter under different fault types, and then calculates the probability of each fault type based on the parameters collected in real time to determine the current fault type; The flexible load precise control module calculates the responsiveness of the flexible load based on the fault determination result and generates a control instruction based on the responsiveness, taking into account the type adjustment coefficient, operating state coefficient, and adjustable reactive power variation of different types of flexible loads. The fault verification and location optimization module monitors the changes in the power factor of each circuit of the ring network box. When the power factor of a circuit returns to normal, it is locked as a fault-related circuit. The current phase and voltage harmonic parameters are compared with historical fault data to locate the specific location of the fault. The reactive collaborative intelligent compensation module treats flexible loads as dynamic reactive sources, combines fault probability and flexible load responsiveness, calculates the weights of various flexible loads in reactive compensation to construct a power factor optimization objective function, and calculates and adjusts the flexible load regulation amount and reactive compensation equipment control parameters using a particle swarm optimization algorithm.

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