Power grid fault intelligent disposal plan automatic generation method, system and medium

Abstract

The invention relates to a method and a system for automatically generating a power grid fault intelligent disposal plan, and a medium. The method comprises the following specific steps: fault set management: quickly identifying and classifying faults by monitoring the state of a power grid in real time, collecting detailed fault data, performing influence evaluation, and generating and optimizing fault set information; intelligent fault early warning: performing real-time monitoring and data analysis and prediction on the operation state of the power equipment, and warning possible fault risks; risk assessment: assessing the influence of the fault on the power grid according to the fault type and the topological structure of the power grid; fault plan online generation: according to the result of the fault risk assessment, automatically starting and applying preset logic and an expert knowledge base, and dynamically constructing one or more sets of risk disposal plans; performing plan process management; and updating the plan library. Through the intelligent plan generation method, corresponding disposal plans can be quickly and accurately generated for different types of faults, so that a power system can operate more stably and reliably.

Description

Technical Field

[0001] The present application relates to the field of power system automation, and more specifically, to a method, system and medium for automatically generating an intelligent emergency plan for power grid fault disposal. Background Art

[0002] Traditional fault detection and response plan preparation is highly dependent on the experience and knowledge of power engineers and lacks standardized and automated processes. When a power grid fault occurs, existing technologies usually take a long time to identify the type and location of the fault, resulting in delays in the generation and execution of response plans. Existing response plans are often based on historical data and experience and lack adaptability to the real-time status of the power grid and future changes. Although technologies such as artificial intelligence and big data analysis have been widely used in other fields, their application in the field of power grid fault handling is not mature enough and has failed to fully realize their potential.

[0003] The shortcomings of the prior art mainly include the following aspects:

[0004] Slow response speed of disposal plans: Traditional power grid fault disposal plans often need to rely on manual experience to formulate, which takes a certain amount of time and cannot meet the needs of rapid response to faults.

[0005] The plan is not targeted: Traditional plans are too general and difficult to deal with specific faults quickly and accurately. In this case, it may not be able to effectively deal with complex and changing fault situations.

[0006] The plan is not updated in time: With the development of power grid technology, new fault types may appear, and the old plan may not be able to adapt to these new fault types. Therefore, the update speed of the plan does not match the evolution speed of the fault type. Summary of the invention

[0007] The purpose of the embodiments of the present application is to provide a method, system and medium for automatically generating an intelligent power grid fault disposal plan, thereby improving the power grid fault response speed and disposal efficiency and reducing the losses caused by the fault.

[0008] To achieve the above objectives, this application provides the following technical solutions:

[0009] In a first aspect, an embodiment of the present application provides a method for automatically generating an intelligent power grid fault disposal plan, comprising the following specific steps:

[0010] Fault Set Management:

[0011] Rapidly identify and classify faults through real-time monitoring of the grid status, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information;

[0012] Intelligent fault warning:

[0013] Real-time monitoring and data analysis of the operating status of power equipment, identification of abnormal behavior and potential fault signals of equipment, intelligent perception and collection of key fault-related information in the power grid system, and prediction and warning of possible fault risks through continuous self-learning and pattern recognition;

[0014] risk assessment:

[0015] Evaluate the impact of faults on the power grid based on the fault type and power grid topology;

[0016] Online generation of fault plans:

[0017] Based on the results of the fault risk assessment, automatically start and use the preset logic and expert knowledge base to dynamically build one or more risk disposal plans;

[0018] Plan process management:

[0019] Plan preparation: The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is prepared, it can be submitted for multi-level review. Plan review process management: After the prepared plan is submitted, it will follow the review process set in the system. Plan termination: When the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated, the plan will be officially terminated.

[0020] Update of plan library:

[0021] By continuously monitoring the operating status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate.

[0022] By monitoring the power grid status in real time, we can quickly identify and classify faults, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information.

[0023] By integrating SCADA and PMS3.0 systems, the grid operation data, including active power, reactive power, voltage, current and power factor information, is collected and cleaned to filter out noise and abnormal values, and the data format is standardized for easy analysis and processing;

[0024] Using machine learning algorithms, the extracted features are learned and trained to achieve automatic classification of fault modes;

[0025] Adopt risk assessment methods such as Monte Carlo method, build risk assessment model, combine actual operation data of power system to assess the risk of accident, and determine the severity of accident accordingly;

[0026] According to the historical fault data and real-time monitoring data, the fault set information is generated based on the above steps.

[0027] The real-time monitoring and data analysis of the operating status of power equipment can identify abnormal behavior and potential fault signals of equipment, realize intelligent perception and collection of key fault-related information in the power grid system, and predict and warn of possible fault risks through continuous self-learning and pattern recognition.

[0028] Filter out representative and discriminative features from power data and reduce data dimensions to facilitate model recognition of anomalies;

[0029] Use machine learning algorithms to train models to recognize and distinguish between normal and abnormal power system behavior;

[0030] Based on the model output, an anomaly degree score is assigned to each data point to identify anomalies. The anomaly detection capability of the model is evaluated through performance indicators, and the model is improved and perfected in a timely manner to ensure its effectiveness in practical applications.

[0031] The evaluation of the impact of the fault on the power grid according to the fault type and the power grid topology is specifically as follows:

[0032] In order to accurately locate faults in the power system and develop effective handling strategies, it is first necessary to identify and analyze the faulty equipment and its location in the grid topology, and then quickly search and identify the affected tripped switches;

[0033] Next, we simulate the impact of the fault, evaluate the scope of the power outage, and develop multiple power restoration plans for each fault based on the preset power restoration principles. We conduct a detailed risk assessment of these plans to select the best solution. Once determined, we implement the solution as the standard operating procedure for fault handling, and continuously optimize the plan through post-evaluation and feedback to ensure that the power system can quickly and safely resume normal operation.

[0034] According to the result of the fault risk assessment, the preset logic and expert knowledge base are automatically started and used to dynamically construct one or more risk disposal plans, specifically:

[0035] Combine expert experience and historical data to build an expert knowledge base and select appropriate knowledge base software. The software provides the functions of creating and managing knowledge bases, supports full-text search and tag search, and facilitates the management and maintenance of content.

[0036] Design one or more risk disposal plans based on the risk assessment results and the expert knowledge base. These plans should include specific action steps, responsibility allocation, and resource allocation when risks occur.

[0037] By using genetic algorithms or particle swarm algorithms, the generation of emergency plans is optimized through the process of autonomous learning and finding the best solution. The algorithm is continuously improved according to the characteristics of actual problems.

[0038] In a second aspect, an embodiment of the present application provides a system for automatically generating an intelligent emergency plan for power grid fault disposal, comprising:

[0039] Fault Set Management Module:

[0040] Rapidly identify and classify faults through real-time monitoring of the grid status, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information;

[0041] Fault intelligent warning module:

[0042] Real-time monitoring and data analysis of the operating status of power equipment, identification of abnormal behavior and potential fault signals of equipment, intelligent perception and collection of key fault-related information in the power grid system, and prediction and warning of possible fault risks through continuous self-learning and pattern recognition;

[0043] Risk Assessment Module:

[0044] Evaluate the impact of faults on the power grid based on the fault type and power grid topology;

[0045] Fault plan online generation module:

[0046] Based on the results of the fault risk assessment, automatically start and use the preset logic and expert knowledge base to dynamically build one or more risk disposal plans;

[0047] Plan process management module:

[0048] Plan preparation: The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is prepared, it can be submitted for multi-level review. Plan review process management: After the prepared plan is submitted, it will follow the review process set in the system. Plan termination: When the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated, the plan will be officially terminated.

[0049] Plan library update module:

[0050] By continuously monitoring the operating status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate.

[0051] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores program code, and when the program code is executed by a processor, the steps of the method for automatically generating an intelligent power grid fault disposal plan as described above are implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] High degree of automation: The present invention realizes a fully automated process from fault detection to the generation of disposal plans, reducing the reliance on manual operations, thereby reducing errors and delays caused by human factors.

[0054] Rapid response capability: By utilizing efficient algorithms and real-time data processing technology, the present invention can quickly identify power grid faults and locate the source of the fault, greatly shortening the fault response time.

[0055] Technology integration advantages: The present invention deeply integrates advanced technologies such as artificial intelligence, machine learning, and big data analysis, giving full play to the advantages of these technologies in power grid fault handling and improving the intelligence level of handling plan generation.

[0056] User interaction experience: Through the user interaction module, operators can easily review, modify and execute plans, providing a more user-friendly and customized operation experience.

[0057] Improve the stability and reliability of the power grid: Through the above advantages, the present invention significantly improves the efficiency and effect of power grid fault handling, enhances the stability and reliability of the power grid, and provides a strong guarantee for the safe operation of the power system.

[0058] These advantages reflect the innovation and practicality of the present invention in the field of power grid fault handling, and provide a more advanced and effective solution for power grid management and power system operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 A flow chart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0062] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0063] The terms "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and should not be understood as indicating or implying relative importance, nor should they be understood as requiring or implying any such actual relationship or order between these entities or operations.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a method for automatically generating an intelligent emergency plan for power grid fault disposal, and the method comprises the following steps:

[0065] The specific technical solutions are as follows:

[0066] (1) Fault set management

[0067] By real-time monitoring of the power grid status, faults can be quickly identified and classified, detailed fault data can be collected, impact assessment can be performed, and fault set information can be intelligently generated and optimized based on advanced algorithms and historical cases to achieve management and maintenance of fault set information.

[0068] (2) Intelligent Fault Warning

[0069] The intelligent fault warning module monitors the operating status of power equipment in real time and performs data analysis, using advanced computer technology and in-depth data mining algorithms to analyze the operating data of power equipment. The module can accurately identify abnormal behavior and potential fault signals of equipment, and realize intelligent perception and collection of key fault-related information in the power grid system. Through continuous self-learning and pattern recognition, it can predict and warn of possible fault risks, thereby providing timely warning information, enhancing the preventive maintenance capability and response speed of the power grid, and ensuring the stability and safety of the power system.

[0070] (3) Risk Assessment Module

[0071] The impact of a fault on the grid is evaluated based on the type of fault and the grid topology. This starts with the construction of a grid model that details the grid topology, component characteristics, and their interrelationships. Subsequently, the fault information provided by the real-time monitoring system is input into the module. This information may include the time and location of the fault, the type of fault, and the abnormal parameters detected.

[0072] Next, the module uses quantitative analysis methods, such as fault tree analysis (FTA), event tree analysis (ETA) or Monte Carlo simulation, to simulate the direct impact of the fault on the operation of the power grid and the chain reactions that may be triggered. These analysis methods can evaluate the quantitative impact of the fault on the stability, reliability and quality of the power grid, such as predicting the power supply interruption, voltage reduction or frequency deviation that may be caused by the fault.

[0073] The risk assessment module also considers the grid's operating status and external conditions, such as load levels, grid backup capacity and environmental factors, to further refine the assessment results. During the assessment process, the system dynamically updates the grid model to reflect changes in the grid status after a fault occurs.

[0074] Finally, the risk assessment module generates a comprehensive risk assessment report, which includes a detailed analysis of the impact of failures, quantitative risk indicators and risk level classification.

[0075] (4) Online generation of fault plans

[0076] According to the results of the fault risk assessment, the module automatically starts and uses the preset logic and expert knowledge base to dynamically build one or more risk disposal plans. The module comprehensively considers the fault type, impact range, grid topology and historical cases, and uses intelligent algorithms to optimize the content of the plan to ensure that the generated plan is both scientific and reasonable and highly adaptable and executable. By simulating the grid response under fault conditions, the module can also simulate and test the plan, evaluate its effectiveness and make necessary adjustments to improve the success rate of disposal measures. Finally, the module will output a detailed plan report, including key information such as fault handling steps, resource allocation, and expected recovery time, to provide decision support for grid operators and ensure that they can respond quickly and orderly when a fault occurs.

[0077] (5) Plan process management

[0078] Plan preparation

[0079] The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is written, it can be submitted for multi-level review.

[0080] Plan review process management

[0081] After the prepared plan is submitted, it will be subject to multi-level review, counter-signing, issuance and archiving in accordance with the review process set up in the system. During the review process, relevant parties can fill in the review opinions, and the preparers can make adjustments to the rejected plans.

[0082] Termination of the plan

[0083] When the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated, the plan will be officially ended. Or in some cases, if new circumstances arise during the fault handling process or the original plan is no longer applicable, the current plan may need to be terminated and the plan needs to be ended and terminated.

[0084] The plan process management module will make the power grid fault handling plan scientific, systematic and effective, and improve the power grid's ability to respond to emergencies.

[0085] (6) Update of plan library

[0086] By continuously monitoring the operation status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate. This module regularly reviews and evaluates the effectiveness of existing plans through automated tools, identifying areas that need to be updated or improved. At the same time, it also allows dispatchers and technicians to manually add, modify or delete plans based on new fault cases, technological advances or changes in the power grid structure. The updated plans are rigorously verified and tested to ensure that they can provide effective fault handling guidance in actual applications. In addition, the module also includes a user feedback mechanism to collect practical experience and suggestions to further optimize the content and structure of the plan library. Through this continuous updating and maintenance, the plan library can adapt to the needs of power grid development and improve the efficiency and success rate of fault handling.

[0087] The embodiment of the present application provides a system for automatically generating an intelligent emergency plan for power grid fault disposal, including a fault set management module:

[0088] Rapidly identify and classify faults through real-time monitoring of the grid status, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information;

[0089] Fault intelligent warning module:

[0090] Real-time monitoring and data analysis of the operating status of power equipment, identification of abnormal behavior and potential fault signals of equipment, intelligent perception and collection of key fault-related information in the power grid system, and prediction and warning of possible fault risks through continuous self-learning and pattern recognition;

[0091] Risk Assessment Module:

[0092] Evaluate the impact of faults on the power grid based on the fault type and power grid topology;

[0093] Fault plan online generation module:

[0094] Based on the results of the fault risk assessment, automatically start and use the preset logic and expert knowledge base to dynamically build one or more risk disposal plans;

[0095] Plan process management module:

[0096] Plan preparation: The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is prepared, it can be submitted for multi-level review. Plan review process management: After the prepared plan is submitted, it will follow the review process set in the system. Plan termination: When the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated, the plan will be officially terminated.

[0097] Plan library update module:

[0098] By continuously monitoring the operating status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate.

[0099] An embodiment of the present application provides a computer-readable storage medium, which stores program code. When the program code is executed by a processor, the steps of the method for automatically generating an intelligent power grid fault disposal plan as described above are implemented.

[0100] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0101] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0105] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0106] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0107] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for automatically generating an intelligent emergency plan for power grid faults, characterized in that: The specific steps include: Fault Set Management: Rapidly identify and classify faults through real-time monitoring of the grid status, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information; Intelligent fault warning: Real-time monitoring and data analysis of the operating status of power equipment, identification of abnormal behavior and potential fault signals of equipment, intelligent perception and collection of key fault-related information in the power grid system, and prediction and warning of possible fault risks through continuous self-learning and pattern recognition; risk assessment: Evaluate the impact of faults on the power grid based on the fault type and power grid topology; Online generation of fault plans: Based on the results of the fault risk assessment, automatically start and use the preset logic and expert knowledge base to dynamically build one or more risk disposal plans; Plan process management: Plan preparation: The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is prepared, it can be submitted for multi-level review. Plan review process management: After the prepared plan is submitted, it follows the review process set in the system; The plan will be terminated when the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated. Update of plan library: By continuously monitoring the operating status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate.

2. A method for automatically generating an intelligent power grid fault disposal plan according to claim 1, characterized in that: By monitoring the power grid status in real time, we can quickly identify and classify faults, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information. By integrating SCADA and PMS3.0 systems, the grid operation data, including active power, reactive power, voltage, current and power factor information, is collected and cleaned to filter out noise and abnormal values, and the data format is standardized for easy analysis and processing; Using machine learning algorithms, the extracted features are learned and trained to achieve automatic classification of fault modes; Adopt risk assessment methods such as Monte Carlo method, build risk assessment model, combine actual operation data of power system to assess the risk of accident, and determine the severity of accident accordingly; According to the historical fault data and real-time monitoring data, the fault set information is generated based on the above steps.

3. The method for automatically generating an intelligent emergency plan for power grid fault according to claim 1, characterized in that: The real-time monitoring and data analysis of the operating status of power equipment can identify abnormal behavior and potential fault signals of equipment, realize intelligent perception and collection of key fault-related information in the power grid system, and predict and warn of possible fault risks through continuous self-learning and pattern recognition. Filter out representative and discriminative features from power data and reduce data dimensions to facilitate model recognition of anomalies; Use machine learning algorithms to train models to recognize and distinguish between normal and abnormal power system behavior; Based on the model output, an anomaly degree score is assigned to each data point to identify anomalies. The anomaly detection capability of the model is evaluated through performance indicators, and the model is improved and perfected in a timely manner to ensure its effectiveness in practical applications.

4. The method for automatically generating an intelligent emergency plan for power grid fault disposal according to claim 1, characterized in that: The evaluation of the impact of the fault on the power grid according to the fault type and the power grid topology is specifically as follows: In order to accurately locate faults in the power system and develop effective handling strategies, it is first necessary to identify and analyze the faulty equipment and its location in the grid topology, and then quickly search and identify the affected tripped switches; Next, we simulate the impact of the fault, evaluate the scope of the power outage, and develop multiple power restoration plans for each fault based on the preset power restoration principles. We conduct a detailed risk assessment of these plans to select the best solution. Once determined, we implement the solution as the standard operating procedure for fault handling, and continuously optimize the plan through post-evaluation and feedback to ensure that the power system can quickly and safely resume normal operation.

5. The method for automatically generating an intelligent emergency plan for power grid fault disposal according to claim 1, characterized in that: According to the result of the fault risk assessment, the preset logic and expert knowledge base are automatically started and used to dynamically construct one or more risk disposal plans, specifically: Combine expert experience and historical data to build an expert knowledge base and select appropriate knowledge base software. The software provides the functions of creating and managing knowledge bases, supports full-text search and tag search, and facilitates the management and maintenance of content. Design one or more risk disposal plans based on the risk assessment results and the expert knowledge base. These plans should include specific action steps, responsibility allocation, and resource allocation when risks occur. By applying genetic algorithms or particle swarm algorithms, the generation of emergency plans is optimized through the process of autonomous learning and finding the best solution. The algorithm is continuously improved according to the characteristics of actual problems.

6. A system for automatically generating intelligent emergency plans for power grid faults, characterized in that: include, Fault Set Management Module: Rapidly identify and classify faults through real-time monitoring of the grid status, collect detailed fault data, conduct impact assessment, and generate and optimize fault set information; Fault intelligent warning module: Real-time monitoring and data analysis of the operating status of power equipment, identification of abnormal behavior and potential fault signals of equipment, intelligent perception and collection of key fault-related information in the power grid system, and prediction and warning of possible fault risks through continuous self-learning and pattern recognition; Risk Assessment Module: Evaluate the impact of faults on the power grid based on the fault type and power grid topology; Fault plan online generation module: Based on the results of the fault risk assessment, automatically start and use the preset logic and expert knowledge base to dynamically build one or more risk disposal plans; Plan process management module: Plan preparation: The system automatically generates fault handling plans online and supports manual modification and adjustment. After the plan is prepared, it can be submitted for multi-level review. Plan review process management: After the prepared plan is submitted, it follows the review process set in the system; The plan will be terminated when the power grid fault is successfully handled and the power grid returns to normal operation, or the impact of the fault is completely eliminated. Plan library update module: By continuously monitoring the operating status of the power grid and changes in the external environment, new data is collected and analyzed to ensure that the information in the plan library remains up-to-date and accurate.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, and when the program code is executed by a processor, the steps of the method for automatically generating an intelligent power grid fault disposal plan as described in any one of claims 1 to 5 are implemented.

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