Power distribution network fault processing method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a power distribution network fault processing method and device, electronic equipment and a storage medium. The method comprises the following steps: determining fault information corresponding to a faulted target power distribution network; determining switch configuration information of the target power distribution network based on a preset random forest algorithm model and the fault information; and performing fault processing on the target power distribution network according to the switch configuration information. According to the scheme, the fault repairing efficiency and reliability can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution data processing, and in particular to a method, device, electronic device and storage medium for handling power distribution network faults. Background Art

[0002] As a core component of the power system, the operation status of the smart distribution network plays a vital role in the stability and reliability of the power grid. With the continuous advancement of science and technology, the application of multi-source information fusion technology in smart distribution networks is becoming more and more extensive. However, the traditional distribution network line fault handling method relies on manual operation, and it often takes hours or even longer from fault location, isolation to power restoration in non-fault areas. In order to improve the efficiency of fault handling, the fault repair function is introduced to shorten the distribution network line fault handling time to "minutes" or even "seconds". Although the optimization of distribution network planning methods, the improvement of operation control level and integrated decision analysis achieved through big data technology have improved the fault repair capability to a certain extent, there are still certain limitations. In particular, when the first fault repair is unsuccessful, manual on-site power restoration is still required, which greatly reduces the efficiency and reliability of fault repair. Summary of the invention

[0003] The embodiments of the present application provide a distribution network fault processing system, method, electronic device and storage medium, which can improve the efficiency and reliability of fault repair.

[0004] In a first aspect, an embodiment of the present application provides a method for handling a distribution network fault, comprising:

[0005] Determine the fault information corresponding to the target distribution network of the fault;

[0006] Determine switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information;

[0007] According to the switch configuration information, fault processing is performed on the target distribution network.

[0008] Optionally, in some embodiments of the present application, the fault information corresponding to the target distribution network having the fault is determined to include:

[0009] Collect real-time data from the distribution network system;

[0010] Detecting the real-time data based on a pre-trained fault detection model, and determining in the distribution network system that a faulty distribution network is a target distribution network;

[0011] Fault information corresponding to the target distribution network is determined based on the fault detection model.

[0012] Optionally, in some embodiments of the present application, determining the fault information corresponding to the target distribution network based on the fault detection model includes:

[0013] Acquire real-time fault data of the target distribution network through a preset blockchain;

[0014] The real-time fault data is analyzed and processed using the fault detection model to determine fault information corresponding to the target distribution network.

[0015] Optionally, in some embodiments of the present application, determining the switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information includes:

[0016] Determine the fault scenario corresponding to the fault information based on a preset random forest algorithm model;

[0017] The switch configuration information corresponding to the fault scenario is determined as the switch configuration information of the target distribution network.

[0018] Optionally, in some embodiments of the present application, before determining the fault scenario corresponding to the fault information based on a preset random forest algorithm model, the method further includes:

[0019] Obtain historical fault data of multiple distribution networks;

[0020] The historical fault data is used to train the initial model to obtain a random forest algorithm model.

[0021] Optionally, in some embodiments of the present application, performing fault processing on the target distribution network according to the switch configuration information includes:

[0022] Determine the power distribution equipment to be controlled corresponding to the switch configuration information;

[0023] The power distribution equipment to be controlled is controlled according to the configuration strategy corresponding to the switch configuration information.

[0024] Optionally, in some embodiments of the present application, after controlling the power distribution device to be controlled according to the configuration strategy corresponding to the switch configuration information, the method further includes:

[0025] Monitor the power supply situation in non-fault areas after executing power distribution control;

[0026] A secondary processing strategy is generated according to the power supply situation.

[0027] In a second aspect, an embodiment of the present application provides a distribution network fault processing device, including:

[0028] A first determination module is used to determine fault information corresponding to a target distribution network with a fault;

[0029] A second determination module is used to determine the switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information;

[0030] A processing module is used to perform fault processing on the target distribution network according to the switch configuration information.

[0031] Correspondingly, the present application also provides an electronic device, comprising a memory, a processor, and a processing program stored in the memory and executable on the processor, wherein the processor executes the program according to any of the steps of the method described above.

[0032] The present application also provides a storage medium, wherein the storage medium stores a processor program, and the processor program implements any of the above methods when the processor program is executed by a processor.

[0033] The present application provides a distribution network fault processing method, device, electronic device and storage medium. After determining the fault information corresponding to the target distribution network of the fault, the switch configuration information of the target distribution network is determined based on a preset random forest algorithm model and the fault information. Finally, the target distribution network is fault processed according to the switch configuration information. In the distribution network fault processing scheme provided by the present application, the switch configuration information of the target distribution network is determined based on a preset random forest algorithm model and the fault information, and then the fault target distribution network is fault processed according to the switch configuration information. There is no need for manual on-site fault processing, which can improve the efficiency and reliability of fault processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 It is a flow chart of a method for handling distribution network faults provided in an embodiment of the present application;

[0036] Figure 2 It is a flowchart of using a machine learning algorithm to perform fault diagnosis and location according to an embodiment of the present application;

[0037] Figure 3 It is a schematic diagram of the construction process of the information sharing platform provided in the embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of a process for constructing a switch self-healing type adaptive configuration provided by an embodiment of the present application;

[0039] Figure 5 It is a flowchart of the fault-tolerant processing mechanism provided in the embodiment of the present application;

[0040] Figure 6 It is a flowchart of the execution step analysis provided in the embodiment of the present application;

[0041] Figure 7 It is a structural schematic diagram of a distribution network fault processing device provided in an embodiment of the present application;

[0042] Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of systems and methods consistent with the examples detailed in the attached claims, or with some aspects of the present application.

[0044] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0045] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0047] Embodiments of the present application provide a distribution network fault processing method, device, electronic device, and storage medium.

[0048] Among them, the distribution network fault processing device can be deployed in a terminal, and the terminal can be a smart phone, a tablet, etc. As mentioned above, the terminal 102 is often configured with a display device, and the display device can also be a display, a display screen, a touch screen, etc. The touch screen can also be a touch screen, a touch panel, etc., but is not limited to this. The terminal can communicate with the server in a wired or wireless manner. The server can include an independently running server or a distributed server, or a server cluster composed of multiple servers. The terminal can include a mobile phone, a tablet computer or a personal processor (PC, Personal Computer).

[0049] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0050] The present application provides a distribution network fault processing method, including: determining fault information corresponding to a target distribution network of the fault; determining switch configuration information of the target distribution network based on a preset random forest algorithm model and the fault information; and performing fault processing on the target distribution network according to the switch configuration information.

[0051] See also Figure 1 , Figure 1 : is a flow chart of a distribution network fault handling method provided by the present application, and the distribution network fault handling method specifically includes the following process:

[0052] 101. Determine fault information corresponding to the faulty target distribution network.

[0053] The distribution network is an important part of the power system. It is responsible for distributing the electric energy in the high-voltage transmission network to various power consumption areas after the voltage is reduced by the substation, and finally transmits the electric energy to the user end, such as homes, commercial buildings and industrial facilities, through low-voltage lines. The management and optimization of the distribution network is crucial to ensure the reliability, safety and economy of power supply.

[0054] For example, real-time data is collected from intelligent terminal devices of the distribution network (such as smart meters, sensors, protection relays, etc.), and the real-time data is checked to see if it exceeds the threshold of normal operating parameters, such as voltage, current, power quality, etc. Identify any events automatically recorded by the equipment or system, such as switch tripping, protection device operation, etc. Perform signal processing and analysis on real-time data, such as Fourier transform, to identify fault characteristics. Apply algorithms (such as travel time method, current phasor comparison method, etc.) to determine the approximate location of the fault. At the same time, use machine learning or deep learning models to analyze data patterns to identify fault types (such as short circuit, ground fault, overload, etc.).

[0055] Optionally, in some embodiments of the present application, the step of “determining fault information corresponding to the faulty target distribution network” may specifically include:

[0056] Collect real-time data from the distribution network system;

[0057] The real-time data is detected based on the pre-trained fault detection model, and the faulty distribution network is determined as the target distribution network in the distribution network system;

[0058] The fault information corresponding to the target distribution network is determined based on the fault detection model.

[0059] For example, the distribution network OMS system collects the operating status of each device in the distribution network, historical fault data, and self-healing strategies obtained through analysis. These data include real-time data from different sensors and devices. Then, the collected historical data and real-time monitoring data are analyzed using machine learning algorithms to determine the type and location of the fault. Finally, the type of fault (such as short circuit, open circuit, etc.), whether it can be self-healed, and the specific location of the fault are determined based on the data pattern and characteristics.

[0060] Optionally, in some embodiments of the present application, the step of “determining fault information corresponding to the target distribution network based on the fault detection model” may specifically include:

[0061] Obtain real-time fault data of the target distribution network through the preset blockchain;

[0062] The fault detection model is used to analyze and process the real-time fault data to determine the fault information corresponding to the target distribution network.

[0063] For example, smart devices and sensors in the distribution network are configured to upload real-time data to the blockchain platform. The blockchain platform is designed to store and share data securely and transparently while ensuring that the data cannot be tampered with. Collect real-time operating data in the distribution network, including electrical parameters such as current, voltage, power, frequency, as well as the operating status of the equipment and environmental factors. These data can come from smart terminal devices such as smart meters, sensors, and protection relays. The real-time fault data obtained from the blockchain is input into the fault detection model. The real-time data is analyzed using a preset fault detection model (which can be based on machine learning, deep learning, or other algorithm models). Thus, key information such as the type, location, and severity of the fault is determined.

[0064] 102. Based on a preset random forest algorithm model and fault information, determine switch configuration information of the target distribution network.

[0065] For example, load a pre-trained random forest model. This model should have been trained using historical fault data and corresponding switch configurations. Determine which fault features of the fault information are used as input to the model, including fault location, type, severity, etc. Input the pre-processed fault information (i.e., fault features) into the random forest model. Based on the input fault information, predict the optimal switch configuration strategy to isolate the fault area and minimize the impact on the non-fault area. The model obtains the predicted switch configuration information.

[0066] Optionally, in some embodiments of the present application, the step of “determining switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information” may specifically include:

[0067] Determine the fault scenario corresponding to the fault information based on the preset random forest algorithm model;

[0068] The switch configuration information corresponding to the fault scenario is determined as the switch configuration information of the target distribution network.

[0069] For example, the preprocessed fault data is input into the random forest algorithm model. The random forest algorithm model analyzes the input data and predicts the most likely fault scenarios, such as single-phase grounding, two-phase short circuit, three-phase short circuit, etc. According to the predicted fault scenario, the corresponding switch configuration strategy is determined. In this way, the optimal configuration state (such as open or closed) of each switch in the target distribution network is determined, thereby achieving fault isolation and power supply restoration in non-fault areas.

[0070] Optionally, in some embodiments of the present application, before the step of “determining the fault scenario corresponding to the fault information based on a preset random forest algorithm model”, the following may also be specifically included:

[0071] Obtain historical fault data of multiple distribution networks;

[0072] The initial model is trained using historical fault data to obtain a random forest algorithm model.

[0073] The Random Forest algorithm is an ensemble learning method that is mainly used for classification and regression tasks. Random Forest makes predictions by building multiple decision trees and aggregating the prediction results of these decision trees to improve the accuracy and robustness of the overall model.

[0074] For example, specifically, determine which distribution networks can be used to obtain historical fault data, which may include smart meters, sensors, fault recording systems, etc. Collect historical fault data from multiple distribution networks to ensure data diversity and comprehensiveness. Remove invalid or erroneous data records and fill missing values ​​to improve data quality. Extract features useful for fault prediction from the original data, such as timestamp, fault type, fault location, fault duration, etc. Assign labels or categories to each fault event in the dataset, which will be used to train the model. Merge the historical fault data collected from different distribution networks into a unified dataset. Ensure that the merged dataset is consistent in format and metrics. Divide the dataset into a training set and a test set, usually in a ratio of 70:30 or 80:20. Select the random forest algorithm as the model for fault prediction. Set the initial parameters of the random forest model, such as the number of trees, maximum depth, minimum sample split, etc. Use the data on the training set to train the random forest model. Adjust the model parameters based on the performance on the validation set, and perform multiple iterations to optimize the model. Use the test set to evaluate the performance of the model. Common indicators include accuracy, recall, F1 score, etc. Confirm that the model's performance meets the predetermined goal and prepare to deploy the model to the production environment. Deploy the trained random forest model to the distribution network's monitoring system and perform fault prediction.

[0075] 103. Perform fault processing on the target distribution network according to the switch configuration information.

[0076] For example, obtain the switch configuration information of the target distribution network. The switch configuration information includes the identifier of the switch to be operated (such as a circuit breaker, disconnector, etc.), the desired state (open or closed), and the order of operation. Verify that the switch configuration information is technically feasible and will not cause instability or safety issues in the distribution network. Convert the switch configuration information into specific operation instructions, which will be sent to the automation control system of the distribution network. The automation control system of the distribution network remotely performs the switch operation.

[0077] Optionally, in some embodiments of the present application, the step of “performing fault processing on the target distribution network according to the switch configuration information” may specifically include:

[0078] Determine the power distribution equipment to be controlled corresponding to the switch configuration information;

[0079] According to the configuration strategy corresponding to the switch configuration information, the controlled power distribution equipment is controlled.

[0080] For example, specifically, the identifier of the power distribution equipment to be operated is parsed from the acquired switch configuration information. According to the equipment database of the distribution network, the identifier in the switch configuration information is matched with the actual power distribution equipment to determine the power distribution equipment to be controlled. The current status of the power distribution equipment to be controlled is queried, including whether it is already in the expected operating state. Specific control instructions are formulated according to the switch configuration information, including the equipment to be operated, the type of operation (on or off), and the order of operations. The control instructions are sent to the power distribution equipment to be controlled through the automated control system of the distribution network. The power distribution equipment to be controlled performs corresponding operations according to the received control instructions, such as opening or closing of the circuit breaker.

[0081] Optionally, in some embodiments of the present application, the step of “controlling the power distribution device to be controlled according to the configuration strategy corresponding to the switch configuration information” may specifically include:

[0082] Monitor the power supply situation in non-fault areas after executing power distribution control;

[0083] Generate secondary processing strategy based on power supply conditions.

[0084] For example, specifically, use smart meters, sensors, and other monitoring devices to collect real-time power supply data in non-fault areas, including parameters such as voltage, current, and power quality. Monitor the power supply status in non-fault areas to check whether all users have been restored to power and whether the power supply quality meets the standards. Integrate the collected data into a central monitoring system for easy analysis. Evaluate the performance after power restoration, including power supply stability, voltage and frequency accuracy, etc. Identify problems that may occur during power restoration, such as unstable power supply, abnormal voltage, or insufficient power supply. Analyze the cause of the power supply problem, which may be due to equipment failure, load changes, or other external factors. Develop secondary processing strategies based on the power supply situation and the cause of the problem. This may include adjusting load distribution, optimizing power generation resources, or reconfiguring the grid topology. Generate specific control commands based on the determined secondary processing strategy. Execute the control commands through the automated control system of the distribution network, such as adjusting the transformer tap switch or reconfiguring the grid switch. Monitor the execution effect of the secondary processing strategy to ensure that the power supply situation is improved.

[0085] The above is the distribution network fault handling process provided in the embodiment of the present application.

[0086] In summary, after determining the fault information corresponding to the target distribution network of the fault, the embodiment of the present application determines the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information, and finally, performs fault processing on the target distribution network according to the switch configuration information. In the distribution network fault processing scheme provided by the present application, based on the preset random forest algorithm model and the fault information, the switch configuration information of the target distribution network is determined, and then the fault target distribution network is processed according to the switch configuration information, without the need for manual on-site fault processing, which can improve the efficiency and reliability of fault processing.

[0087] In order to further understand the distribution network fault handling solution of the present application, the following is an example of an intelligent distribution network secondary self-healing execution management scenario based on multi-source information fusion analysis. First, based on multi-source information fusion, intelligent fault diagnosis and positioning of the distribution network are carried out to determine the type and precise location of the fault. Accurate positioning of the fault location is the basis of the self-healing solution; secondly, blockchain technology is used to establish a secure information sharing platform, which is conducive to fault data exchange and collaboration of various power dispatch; thirdly, the random forest algorithm is used to adaptively configure the switch self-healing type and adaptively reconstruct the grid. In the event of a fault, the best self-healing switch type is selected to improve the accuracy of self-healing; fourthly, after fault diagnosis and positioning, the self-healing strategy intelligently generated by the distribution network OMS system is used to analyze the self-healing execution method. This analysis provides two execution plans: self-healing remote control group execution and full one-time execution of self-healing plans, providing different execution plans for self-healing of different faults; fifth, when the first self-healing is unsuccessful, the distribution network OCS returns the result to the distribution network OMS, redefines the secondary fault area and generates and executes the secondary self-healing plan. It not only analyzes the cause of the self-healing failure, but also generates and executes the secondary self-healing plan, and finally transmits the result to the information sharing platform, which is conducive to the subsequent management of the distribution network.

[0088] (1) Intelligent fault diagnosis and location based on multi-source information fusion. By integrating real-time data from the distribution network OMS, different sensors and devices, the machine learning algorithm is used to analyze historical data and real-time monitoring data to accurately determine the fault type and fault location. For example, after the data of current sensors, voltage sensors and other devices at different locations are collected, they are input into the trained machine learning model after data cleaning and feature extraction. The model determines the fault type (such as short circuit, open circuit, etc.), whether it can be self-healed, and the specific location of the fault based on the data pattern and characteristics. The process of using machine learning algorithms for fault diagnosis and location is as follows: Figure 2 shown.

[0089] (2) Use blockchain technology to share fault information. Use blockchain technology to establish a secure information sharing platform. On this platform, different power entities can exchange and collaborate on fault data safely and efficiently. Each power entity uploads the fault data it monitors to the blockchain, and the data is encrypted and verified before being stored in each node of the blockchain. When adaptive grid reconstruction is required, the relevant power entities can obtain comprehensive and reliable fault data from the blockchain as data support to ensure the accuracy and reliability of the reconstruction process.

[0090] The information sharing platform is mainly based on the data collected by the distribution network OMS. It can display the real-time data information of the distribution network and has query functions. The specific construction process is as follows: Figure 3 shown.

[0091] (3) Adaptive configuration of switch self-healing types and adaptive grid reconstruction. The implementation of adaptive configuration of switch self-healing types requires the use of random forest algorithm to select the optimal self-healing switch configuration, and of course it is inseparable from the support of communication. Specific functions implemented: The self-healing switch list selects the feeder self-healing type by default, and the switch list can be checked manually. After checking, the system supports one-click automatic filling in of the switch self-healing type according to the feeder self-healing type. When a fault is detected, the system can automatically adjust the grid topology to bypass the fault area and optimize the power supply efficiency in non-fault areas. For example, when a fault occurs, the switch state is quickly switched according to the fault location and switch configuration information, and the grid topology is rebuilt to ensure that the power supply in non-fault areas is not affected and minimize the power outage in the fault area. The implementation of using random forest algorithm to construct adaptive configuration of switch self-healing types is as follows: Figure 4 shown.

[0092] (4) Analysis of one-time self-healing execution method. After intelligent fault diagnosis and location are performed based on multi-source information fusion, the location information is transmitted back to the distribution network OMS, and the distribution network OMS intelligently generates a self-healing strategy, which is then executed by the distribution network OCS. There are two execution schemes, namely, self-healing remote control group execution and full one-time execution of the self-healing scheme.

[0093] 1) Self-healing remote control group execution: The self-healing operation is divided into multiple groups according to the isolation boundary, each isolation boundary is a group, and each group contains related remote control instructions. During the self-healing process, remote control operations are performed on each group in turn to ensure that the operations in each group are carried out in an orderly manner. If a device in a group fails to execute during remote control execution, other groups can continue to execute, which can improve the efficiency and reliability of self-healing execution and avoid interference between different operations.

[0094] 2) Execute all the self-healing solutions at once: Execute all the operations in the generated self-healing solution at once. If the remote control fails and multiple attempts at remote control fail, if there are still steps to be executed in the solution, continue to execute other steps until the entire solution is executed. This method is suitable for some simple fault situations or when the self-healing solution has a high success rate, and can quickly restore the normal operation of the power grid.

[0095] Different self-healing strategies use different execution methods to fully and accurately identify the fault self-healing type and improve the success rate of self-healing in a targeted manner. Among them, the two execution schemes have similarities. When an execution failure occurs, the failed command or device will be skipped to continue executing other steps. This involves the need to design a fault-tolerant processing mechanism. The design of a fault-tolerant processing mechanism is to deal with problems such as device failure or communication interruption that may occur during the execution process. Methods for designing a fault-tolerant processing mechanism are as follows: Figure 5 shown.

[0096] (5) Redefinition of secondary fault area and generation and execution of secondary self-healing plan. When the first self-healing fails, a new grid topology is generated based on the adaptive reconstruction of the grid structure. A new secondary fully automatic self-healing plan is generated based on the new grid topology. If the secondary self-healing fully automatic self-healing plan fails, the cause of the failure of self-healing is analyzed according to the execution steps of the self-healing plan, and the fault is relocated and partitioned.

[0097] For example, after a self-healing failure, re-evaluate the state and fault conditions of the power grid, redivide the fault area according to the new grid structure, and generate a new self-healing plan. During the execution of the secondary self-healing plan, pay close attention to the execution results. If it fails, determine whether it is upstream or downstream of the fault area according to the remote control steps. If it is an upstream device, if the failed device is a restored device, do not perform a secondary analysis of the group. If it is an isolated device, perform a secondary analysis, search for the isolation device upstream in the power supply direction, find the available isolation device, and then analyze the restored device to form a new isolation and recovery plan for the upstream, and replace the original plan with the new plan; if it is a downstream device, analyze both the isolation device and the restoration device, starting with the isolation device. If the isolation is successful, do not analyze the isolation anymore, and analyze the transfer device instead. If the available transfer device is found, replace the original group plan with this plan.

[0098] This precise control of the isolation and recovery operations of the fault area can effectively prevent the spread of faults. The random forest algorithm has been used to adaptively configure the self-healing type of switches and their adaptive grid reconstruction. The clustering algorithm is now combined to analyze the execution steps of the self-healing solution, and the analysis results are fed back to the distribution network OMS to generate a new self-healing strategy. The clustering algorithm is used to analyze the execution steps of the self-healing solution. This application uses the K-means algorithm to implement the K-means algorithm. The K-means algorithm can cluster complex data into several groups, which helps to identify different fault modes. It can also find out which steps are most effective in actual operations and which steps may need to be improved or replaced. This helps to optimize the self-healing process and improve the success rate of self-healing. The execution steps of the self-healing solution are analyzed using the K-means algorithm. Figure 6 shown.

[0099] In order to better implement the distribution network fault processing method of the embodiment of the present application, the embodiment of the present application also provides a distribution network fault processing device. The meanings of the terms are the same as those in the above-mentioned distribution network fault processing system, and the specific implementation details can refer to the description in the system embodiment.

[0100] See also Figure 7 , Figure 7 A schematic diagram of the structure of a distribution network fault processing device provided in an embodiment of the present application, wherein the distribution network fault processing device may specifically include a first determination module 201, a second determination module 202 and a processing module 203, which may be specifically as follows:

[0101] A first determination module 201 is used to determine fault information corresponding to a target distribution network with a fault;

[0102] A second determination module 202, configured to determine switch configuration information of a target distribution network based on a preset random forest algorithm model and fault information;

[0103] The processing module 203 is used to perform fault processing on the target distribution network according to the switch configuration information.

[0104] Optionally, in some embodiments of the present application, the first determining module 201 may specifically include:

[0105] A collection unit, used to collect real-time data from the distribution network system;

[0106] A first determination unit is used to detect the real-time data based on a pre-trained fault detection model, and determine that the faulty distribution network is a target distribution network in the distribution network system;

[0107] The second determining unit is used to determine the fault information corresponding to the target distribution network based on the fault detection model.

[0108] Optionally, in some embodiments of the present application, the second determination unit can be specifically used to: obtain real-time fault data of the target distribution network through a preset blockchain; use a fault detection model to analyze and process the real-time fault data to determine the fault information corresponding to the target distribution network.

[0109] Optionally, in some embodiments of the present application, the second determining module 202 may be specifically configured to:

[0110] Determine the fault scenario corresponding to the fault information based on the preset random forest algorithm model;

[0111] The switch configuration information corresponding to the fault scenario is determined as the switch configuration information of the target distribution network.

[0112] Optionally, in some embodiments of the present application, the second determining module 202 may also be specifically configured to:

[0113] Obtain historical fault data of multiple distribution networks;

[0114] The initial model is trained using historical fault data to obtain a random forest algorithm model.

[0115] Optionally, in some embodiments of the present application, the processing module 203 may be specifically used to:

[0116] Determine the power distribution equipment to be controlled corresponding to the switch configuration information;

[0117] According to the configuration strategy corresponding to the switch configuration information, the controlled power distribution equipment is controlled.

[0118] Optionally, in some embodiments of the present application, the processing module 203 may be specifically used to:

[0119] Monitor the power supply situation in non-fault areas after executing power distribution control;

[0120] Generate secondary processing strategy based on power supply conditions.

[0121] The embodiment of the present application provides a distribution network fault processing device, after the first determination module 201 determines the fault information corresponding to the target distribution network of the fault, the second determination module 202 determines the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information, and finally, the processing module 203 performs fault processing on the target distribution network according to the switch configuration information. In the distribution network fault processing scheme provided by the present application, the switch configuration information of the target distribution network is determined based on the preset random forest algorithm model and the fault information, and then the fault target distribution network is processed according to the switch configuration information, without the need for manual on-site fault processing, which can improve the efficiency and reliability of fault processing.

[0122] In addition, the present application also provides an electronic device, such as Figure 8 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0123] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more processor-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that Figure 8 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0124] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0125] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and distribution network fault processing methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0126] The electronic device also includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power anomaly detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0127] The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0128] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0129] Determine the fault information corresponding to the target distribution network of the fault; determine the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information; and perform fault processing on the target distribution network according to the switch configuration information.

[0130] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0131] After determining the fault information corresponding to the target distribution network of the fault, the embodiment of the present application determines the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information, and finally, performs fault processing on the target distribution network according to the switch configuration information. In the distribution network fault processing scheme provided by the present application, based on the preset random forest algorithm model and the fault information, the switch configuration information of the target distribution network is determined, and then the fault processing is performed on the target distribution network according to the switch configuration information, without the need for manual on-site fault processing, which can improve the efficiency and reliability of fault processing.

[0132] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a processor-readable storage medium and loaded and executed by a processor.

[0133] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the distribution network fault processing methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0134] Determine the fault information corresponding to the target distribution network of the fault; determine the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information; and perform fault processing on the target distribution network according to the switch configuration information.

[0135] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0136] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0137] Since the instructions stored in the storage medium can execute the steps in any distribution network fault handling method provided in the embodiments of the present application, the beneficial effects that can be achieved by any distribution network fault handling method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0138] The above is a detailed introduction to a distribution network fault handling method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for handling distribution network faults, characterized in that: include: Determine the fault information corresponding to the target distribution network of the fault; Determine switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information; According to the switch configuration information, fault processing is performed on the target distribution network.

2. The method for handling distribution network faults according to claim 1, characterized in that: The fault information corresponding to the target distribution network determined to have a fault includes: Collect real-time data from the distribution network system; Detecting the real-time data based on a pre-trained fault detection model, and determining in the distribution network system that a faulty distribution network is a target distribution network; Fault information corresponding to the target power distribution network is determined based on the fault detection model.

3. The method for handling distribution network faults according to claim 2, characterized in that: The determining the fault information corresponding to the target distribution network based on the fault detection model includes: Acquire real-time fault data of the target distribution network through a preset blockchain; The real-time fault data is analyzed and processed using the fault detection model to determine fault information corresponding to the target distribution network.

4. The method for handling distribution network faults according to claim 1, characterized in that: The determining of the switch configuration information of the target distribution network based on the preset random forest algorithm model and the fault information includes: Determine the fault scenario corresponding to the fault information based on a preset random forest algorithm model; The switch configuration information corresponding to the fault scenario is determined as the switch configuration information of the target distribution network.

5. The method for handling distribution network faults according to claim 4, characterized in that: Before determining the fault scenario corresponding to the fault information based on the preset random forest algorithm model, the method further includes: Obtain historical fault data of multiple distribution networks; The historical fault data is used to train the initial model to obtain a random forest algorithm model.

6. The method for handling distribution network faults according to claim 1, characterized in that: The performing fault processing on the target distribution network according to the switch configuration information includes: Determine the power distribution equipment to be controlled corresponding to the switch configuration information; The power distribution equipment to be controlled is controlled according to the configuration strategy corresponding to the switch configuration information.

7. The method for handling distribution network faults according to claim 6, characterized in that: After controlling the power distribution equipment to be controlled according to the configuration strategy corresponding to the switch configuration information, the method further includes: Monitor the power supply situation in non-fault areas after executing power distribution control; A secondary processing strategy is generated according to the power supply situation.

8. A distribution network fault processing device, characterized in that: include: A first determination module is used to determine fault information corresponding to a target distribution network with a fault; A second determination module is used to determine the switch configuration information of the target distribution network based on a preset random forest algorithm model and fault information; A processing module is used to perform fault processing on the target distribution network according to the switch configuration information.

9. An electronic device, characterized in that: include: A memory, a processor and a processing program stored in the memory and executable on the processor, wherein the processor executes the program according to the steps of the distribution network fault processing method as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that: A computer processing program is stored which can be loaded by a processor and execute the method for handling a distribution network fault according to any one of claims 1 to 7.

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