Electromagnetic transient fault analysis method, device and equipment based on large model

Abstract

The invention discloses an electromagnetic transient fault analysis method, device and equipment based on a large model, and the method comprises the steps: carrying out the preprocessing operation of real-time operation information obtained from a power grid, and obtaining time sequence operation information; fault inference analysis is carried out according to the time sequence operation information based on a preset fault analysis large model and a thinking chain technology, and a fault analysis result is obtained and comprises a fault position, a fault type and a fault root cause; and according to a space-time feedback reasoning chain in a preset fault analysis large model, combining the topological structure information of the power grid and the fault analysis result to analyze the cascading influence of the fault, and obtaining a fault influence analysis result. The technical problems that in the prior art, the analysis precision is insufficient, the calculation amount is large, and influence analysis and evaluation are difficult to carry out on various different types of faults can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of power grid fault analysis, and in particular to a method, device and equipment for analyzing electromagnetic transient faults based on a large model. Background Art

[0002] Dynamic fault analysis of electromagnetic transients in power grids is to study electromagnetic transient phenomena in power systems when faults occur, including instantaneous changes in voltage and current and their impact on the stable operation of the system. Electromagnetic transient fault processes usually occur between milliseconds and seconds, and have instantaneous, high-frequency and nonlinear characteristics, which may cause impact and damage to power equipment and system operations.

[0003] Existing power grid electromagnetic transient fault analysis mainly relies on traditional numerical calculations and predefined models. These methods are prone to problems such as insufficient accuracy, large amount of calculation and inability to achieve real-time response during the analysis process. Moreover, the types of power grid faults are diverse and the impact range is wide. Existing technologies make it difficult to accurately analyze the impact of faults. As a result, existing power grid electromagnetic transient solid-state analysis solutions are difficult to meet the application requirements of actual scenarios. Summary of the invention

[0004] The present application provides a large-model-based electromagnetic transient fault analysis method, device and equipment to solve the technical problems of the prior art that the analysis accuracy is insufficient, the calculation amount is large and it is difficult to perform impact analysis and evaluation on various types of faults.

[0005] In view of this, the first aspect of the present application provides an electromagnetic transient fault analysis method based on a large model, comprising:

[0006] Preprocessing the real-time operation information obtained from the power grid to obtain time-series operation information;

[0007] Based on the preset fault analysis big model and the thinking chain technology, a fault reasoning analysis is performed according to the timing operation information to obtain a fault analysis result, wherein the fault analysis result includes a fault location, a fault type and a fault root cause;

[0008] According to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, the chain effect of the fault is analyzed in combination with the power grid topology information and the fault analysis result to obtain the fault impact analysis result.

[0009] Preferably, the preprocessing operation of the real-time operation information obtained from the power grid to obtain the time sequence operation information includes:

[0010] Obtain real-time operating information from the grid’s sensors and measurement systems;

[0011] The real-time operation information is respectively subjected to data cleaning, noise reduction, outlier detection and standardization processing to obtain time series operation information.

[0012] Preferably, the fault analysis is performed based on the preset fault analysis big model and the thought chain technology according to the timing operation information to obtain the fault analysis result, including:

[0013] Using the fault detection mechanism based on the thinking chain technology in the preset fault analysis model to perform fault detection and analysis according to the timing operation information, and using the multi-layer analysis mechanism to determine the fault location and fault type;

[0014] The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thought chain technology to obtain the root cause of the fault.

[0015] Preferably, the method further comprises: analyzing the chain effect of the fault based on the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combining the power grid topology information and the fault analysis result, and obtaining the fault impact analysis result; and then:

[0016] Predicting electromagnetic transient faults at future times according to the fault analysis results and the fault impact analysis results to obtain fault prediction results;

[0017] Early warning information is generated according to the fault prediction result, and a fault early warning is performed.

[0018] Preferably, the method further comprises: analyzing the chain effect of the fault based on the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combining the power grid topology information and the fault analysis result, and obtaining the fault impact analysis result; and then:

[0019] The grid dispatching strategy is optimized in combination with the real-time grid operation status and the fault impact analysis result to obtain an optimized grid strategy.

[0020] The second aspect of the present application provides an electromagnetic transient fault analysis device based on a large model, comprising:

[0021] An information processing unit, used for preprocessing the real-time operation information obtained from the power grid to obtain time-series operation information;

[0022] A fault analysis unit, configured to perform fault reasoning analysis according to the timing operation information based on a preset fault analysis large model and a thinking chain technology, and obtain a fault analysis result, wherein the fault analysis result includes a fault location, a fault type and a fault root cause;

[0023] The impact analysis unit is used to analyze the chain effect of the fault according to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combined with the power grid topology information and the fault analysis result, to obtain the fault impact analysis result.

[0024] Preferably, the fault analysis unit is specifically used for:

[0025] Using the fault detection mechanism based on the thinking chain technology in the preset fault analysis model to perform fault detection and analysis according to the timing operation information, and using the multi-layer analysis mechanism to determine the fault location and fault type;

[0026] The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thought chain technology to obtain the root cause of the fault.

[0027] Preferably, it also includes:

[0028] A fault prediction unit, used to predict electromagnetic transient faults at future moments according to the fault analysis result and the fault impact analysis result, to obtain a fault prediction result;

[0029] The warning generation unit is used to generate warning information according to the fault prediction result and perform fault warning.

[0030] Preferably, it also includes:

[0031] The strategy optimization unit is used to optimize the power grid dispatching strategy in combination with the real-time power grid operation status and the fault impact analysis result to obtain an optimized power grid strategy.

[0032] A third aspect of the present application provides an electromagnetic transient fault analysis device based on a large model, the device comprising a processor and a memory;

[0033] The memory is used to store program code and transmit the program code to the processor;

[0034] The processor is used to execute the large model-based electromagnetic transient fault analysis method described in the first aspect according to the instructions in the program code.

[0035] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0036] In the present application, a large-model-based electromagnetic transient fault analysis method is provided, including: preprocessing real-time operation information obtained from the power grid to obtain timing operation information; performing fault reasoning analysis based on the timing operation information based on a preset fault analysis large model and thinking chain technology to obtain a fault analysis result, wherein the fault analysis result includes the fault location, fault type and root cause of the fault; analyzing the chain effect of the fault based on the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combining the power grid topology information and the fault analysis result, and obtaining a fault impact analysis result.

[0037] The electromagnetic transient fault analysis method based on a large model provided by the present application uses a preset fault analysis large model to conduct a comprehensive analysis of the fault. This process introduces the thinking chain technology and also uses a feedback mechanism, which can ensure that the fault analysis of each stage is derived step by step, the analysis process is interpretable, and the obtained analysis results are more accurate and reliable; moreover, this process relies on the construction of a large model after training, so the actual amount of calculation is not large; in addition, the process can perform chain impact analysis on a variety of different types of faults, and it is implemented based on the feedback reasoning chain in the large model, which can ensure the accuracy of the analysis results. Therefore, the present application can solve the technical problems of the existing technology that the analysis accuracy is insufficient, the calculation amount is large, and it is difficult to perform impact analysis and evaluation on a variety of different types of faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of a large model-based electromagnetic transient fault analysis method provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of an electromagnetic transient fault analysis device based on a large model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] Terminology explanation:

[0042] Thinking chain: An analytical method based on logical reasoning that breaks down complex problems into multiple logically related steps and gradually derives solutions.

[0043] Electromagnetic transient: refers to the phenomenon that when the power system is operating normally, the system state changes significantly in a short period of time due to external interference or internal faults.

[0044] Dynamic fault: refers to instantaneous or short-term faults occurring in the power system, such as lightning strikes, short circuits, etc.

[0045] For easier understanding, see Figure 1 , an embodiment of the electromagnetic transient fault analysis method based on a large model provided by the present application includes:

[0046] Step 101: preprocess the real-time operation information obtained from the power grid to obtain time-series operation information.

[0047] Furthermore, step 101 includes:

[0048] Obtain real-time operating information from the grid’s sensors and measurement systems;

[0049] The real-time operation information is cleaned, denoised, detected for outliers and standardized to obtain the time series operation information.

[0050] It should be noted that the process of acquiring data from the power grid is not limited to power grid equipment, and the measurement system includes PMU or SCADA system, etc., as long as the relevant data can be acquired. In addition, in the data acquisition stage, all data that need to be extracted from the power grid in this embodiment can also be acquired together. Although only real-time operation information is mentioned here, it can also include the subsequent required power grid topology information, real-time power grid operation status, and operation data or historical data required for model training, etc., and the specific details will not be repeated. It can be understood that real-time operation information includes but is not limited to key electromagnetic parameters such as voltage, current, and frequency.

[0051] In order to improve the accuracy and reliability of fault analysis, this embodiment uniformly performs preprocessing operations on all acquired data, including but not limited to data cleaning, noise reduction, outlier detection and standardization. Among them, data cleaning can remove data noise and invalid data, and can be implemented by filtering algorithms such as Kalman filtering and low-pass filtering. Noise reduction can reduce electromagnetic interference in information and ensure the accuracy of data. Outlier detection uses statistical analysis or machine learning algorithms to identify abnormal data values, such as isolation forests and DBSCAN. Standardization of data formats unifies data formats to ensure the compatibility of data from different sources.

[0052] It can be understood that the data after acquisition and data processing is in the form of a time series and can be directly input into the subsequent large model for fault analysis. Since the data has undergone various preprocessing operations, it is high-quality data, which can ensure the accuracy and reliability of fault analysis based on this data.

[0053] Step 102: Based on the preset fault analysis big model and the thinking chain technology, a fault reasoning analysis is performed according to the timing operation information to obtain a fault analysis result, which includes a fault location, a fault type and a fault root cause.

[0054] Furthermore, step 102 includes:

[0055] The fault detection mechanism based on the thinking chain technology in the preset fault analysis model is used to perform fault detection and analysis according to the timing operation information, and the fault location and fault type are determined using a multi-layer analysis mechanism;

[0056] The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thinking chain technology to obtain the root cause of the fault.

[0057] The preset fault analysis model of this embodiment adopts a feedback mechanism and introduces the thinking chain technology, which can realize the feedback and reasoning of the power grid data in time and space, ensuring that the relevant characteristics between the related data can be gradually analyzed, thereby ensuring the reliability of the fault analysis. The preset fault analysis model of this embodiment can be constructed based on a multi-layer neural network or a graph neural network, such as a convolutional neural network, a recurrent neural network, etc.; these large models designed on the network architecture can accurately capture the spatiotemporal characteristics of data information.

[0058] The entire preset fault analysis model involves multiple stages, such as fault detection, fault location, fault classification, fault root cause analysis, and subsequent chain effect analysis. The thinking chain technology can decompose the fault analysis process into the above stages, and these stages can form a chain reasoning structure; this step-by-step derivation and analysis method makes the model more interpretable and the analysis process more reliable.

[0059] The preset fault analysis model of this embodiment can use historical fault data for supervised learning or reinforcement learning in advance; through the reinforcement learning mechanism, the reasoning chain can be continuously optimized according to fault feedback to improve the model's adaptability and analysis accuracy; the cross-validation method is used to optimize model parameters and improve model analysis accuracy. Specific model evaluation can be achieved based on indicators such as accuracy, recall rate, F1 score, and then hyperparameter tuning can be performed.

[0060] The timing operation information input into the large model in this embodiment is real-time information. After input into the model, fault detection analysis will be performed first, that is, to determine whether there is a fault in the power grid at this time; if there is a fault, the multi-layer analysis mechanism in the thinking chain will analyze the location and type of the fault, so as to obtain the specific fault location and fault type. The subsequent fault root cause analysis mechanism that is gradually derived will further analyze the root cause of the fault according to the fault location and fault type detected and analyzed, so as to obtain the root cause of the fault. It can be understood that the large model can also combine the electromagnetic transient characteristics with the above-mentioned fault analysis results to briefly analyze the severity of the fault as needed; wherein the electromagnetic transient characteristics can be calculated according to the existing technology, generally within the time range of 20ms-100ms. This is of certain help to the subsequent fault impact analysis. The specific process is not limited here and can be set as needed.

[0061] Step 103: Analyze the chain effect of the fault based on the spatiotemporal feedback reasoning chain in the preset fault analysis model, combined with the grid topology information and the fault analysis result, to obtain the fault impact analysis result.

[0062] The space-time feedback reasoning chain in the preset fault analysis model has actually been mentioned in the fault analysis stage. It combines the concepts of thinking chain, feedback mechanism and neural network to conduct time and space feedback reasoning analysis on the data to ensure that all related information in the fault process can be taken into account, thereby ensuring the reliability of chain impact analysis.

[0063] The chain reaction analysis of this embodiment mainly includes two parts: basic impact analysis and chain reaction prediction. The basic impact analysis mainly analyzes the dynamic impact of the fault on load distribution, voltage stability, frequency, etc. based on the fault type and grid topology information. The chain reaction prediction predicts the subsequent impact that the fault may cause, providing theoretical guidance for subsequent grid operation and maintenance.

[0064] Furthermore, step 103 further includes:

[0065] According to the fault analysis results and the fault impact analysis results, the electromagnetic transient faults at the future moment are predicted to obtain the fault prediction results;

[0066] Generate early warning information based on the fault prediction results and issue fault warnings.

[0067] The fault prediction process of this embodiment is to conduct a comprehensive analysis of the existing fault information and predict possible future faults. This part can be implemented based on the graph neural network model, and the prediction accuracy can be ensured. The predicted faults include but are not limited to overload, voltage fluctuation, voltage instability, and load transfer. Different warning information can be generated according to different prediction results, and fault warnings can be issued to remind power grid dispatchers to take preventive measures in advance and improve the stability of system operation.

[0068] Furthermore, step 103 further includes:

[0069] The grid dispatching strategy is optimized by combining the real-time grid operation status and fault impact analysis results to obtain the optimized grid strategy.

[0070] The fault impact analysis results of this embodiment can be used for subsequent power grid dispatching strategy optimization. The strategy optimization proposed in this embodiment is only one use scenario of the fault analysis results. The fault analysis results can also be applied to other scenarios according to actual conditions, without specific limitation.

[0071] In addition to the results of fault impact analysis, the optimization process also needs to be analyzed in combination with the real-time grid operation status. In this process, an optimized grid strategy can be generated, such as clearly switching switches and adjusting generator output; in addition, a detailed fault handling plan can be formulated to ensure stable grid operation.

[0072] The optimized power grid strategy generated according to this embodiment can also be converted into specific automated control instructions and sent to the power grid equipment for dynamic adjustment; the real-time multiple verification mechanism ensures the security and accuracy of the control instructions. The power grid equipment is directly adjusted through automated control instructions to achieve real-time dynamic response. And a multiple verification mechanism is used to ensure the security and accuracy of instruction execution to prevent misoperation. This closed-loop control from analysis to automated execution enhances the efficiency and security of fault handling. It can be seen that the fault analysis method provided in this embodiment can provide optimized decision support for the stable operation of the power grid, and the optimization effect of the dispatching strategy improves the overall reliability of the power grid and reduces the time of fault power outages.

[0073] It is understandable that if the electromagnetic transient fault analysis method of this embodiment is deployed in a high-voltage transmission line, the line operation status can be monitored in real time. Through the large model thinking chain construction module, the system can identify transient faults caused by lightning strikes and predict the possible impact of the fault on the downstream regional power grid. The system automatically generates a switching strategy to cut off the affected line to prevent the fault from spreading to the entire power grid, while adjusting the load distribution of other lines to ensure the continuity and stability of power supply.

[0074] The electromagnetic transient fault analysis method based on a large model provided in the embodiment of the present application uses a preset fault analysis large model to conduct a comprehensive analysis of the fault. This process introduces the thinking chain technology and also uses a feedback mechanism, which can ensure that the fault analysis of each stage is derived step by step, the analysis process is interpretable, and the obtained analysis results are more accurate and reliable; moreover, this process relies on the construction of a trained large model, so the actual amount of calculation is not large; in addition, the process can perform chain impact analysis on a variety of different types of faults, and is implemented based on the feedback reasoning chain in the large model, which can ensure the accuracy of the analysis results. Therefore, the embodiment of the present application can solve the technical problems of the prior art that the analysis accuracy is insufficient, the calculation amount is large, and it is difficult to perform impact analysis and evaluation on a variety of different types of faults.

[0075] For easier understanding, see Figure 2 The present application provides an embodiment of an electromagnetic transient fault analysis device based on a large model, including:

[0076] The information processing unit 201 is used to perform preprocessing operations on the real-time operation information obtained from the power grid to obtain time-series operation information;

[0077] The fault analysis unit 202 is used to perform fault reasoning analysis based on the preset fault analysis model and the thinking chain technology according to the timing operation information to obtain the fault analysis result, which includes the fault location, fault type and fault root cause;

[0078] The impact analysis unit 203 is used to analyze the chain effect of the fault according to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combined with the power grid topology information and the fault analysis result, to obtain the fault impact analysis result.

[0079] Further, the fault analysis unit 202 is specifically configured to:

[0080] The fault detection mechanism based on the thinking chain technology in the preset fault analysis model is used to perform fault detection and analysis according to the timing operation information, and the fault location and fault type are determined using a multi-layer analysis mechanism;

[0081] The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thinking chain technology to obtain the root cause of the fault.

[0082] Furthermore, it also includes:

[0083] The fault prediction unit 204 is used to predict the electromagnetic transient fault at a future time according to the fault analysis result and the fault impact analysis result to obtain a fault prediction result;

[0084] The warning generation unit 205 is used to generate warning information according to the fault prediction result and perform fault warning.

[0085] Furthermore, it also includes:

[0086] The strategy optimization unit 206 is used to optimize the power grid dispatching strategy in combination with the real-time power grid operation status and the fault impact analysis result to obtain an optimized power grid strategy.

[0087] The present application also provides an electromagnetic transient fault analysis device based on a large model, the device comprising a processor and a memory;

[0088] The memory is used to store the program code and transmit the program code to the processor;

[0089] The processor is used to execute the large model-based electromagnetic transient fault analysis method in the above method embodiment according to the instructions in the program code.

[0090] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0094] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An electromagnetic transient fault analysis method based on a large model, characterized in that: include: Preprocessing the real-time operation information obtained from the power grid to obtain time-series operation information; Based on the preset fault analysis big model and the thinking chain technology, a fault reasoning analysis is performed according to the timing operation information to obtain a fault analysis result, wherein the fault analysis result includes a fault location, a fault type and a fault root cause; According to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, the chain effect of the fault is analyzed in combination with the power grid topology information and the fault analysis result to obtain the fault impact analysis result.

2. The electromagnetic transient fault analysis method based on a large model according to claim 1 is characterized in that: The preprocessing operation is performed on the real-time operation information obtained from the power grid to obtain the time sequence operation information, including: Obtain real-time operating information from the grid’s sensors and measurement systems; The real-time operation information is respectively subjected to data cleaning, noise reduction, outlier detection and standardization processing to obtain time series operation information.

3. The electromagnetic transient fault analysis method based on a large model according to claim 1 is characterized in that: The fault analysis is performed based on the preset fault analysis big model and the thinking chain technology according to the timing operation information to obtain the fault analysis result, including: Using the fault detection mechanism based on the thinking chain technology in the preset fault analysis model to perform fault detection and analysis according to the timing operation information, and using the multi-layer analysis mechanism to determine the fault location and fault type; The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thought chain technology to obtain the root cause of the fault.

4. The large model-based electromagnetic transient fault analysis method according to claim 1 is characterized in that: The method further comprises: analyzing the chain effect of the fault according to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combining the power grid topology information and the fault analysis result, and obtaining the fault impact analysis result; and then: Predicting electromagnetic transient faults at future times according to the fault analysis results and the fault impact analysis results to obtain fault prediction results; Early warning information is generated according to the fault prediction result, and a fault early warning is performed.

5. The large model-based electromagnetic transient fault analysis method according to claim 1, characterized in that: The method further comprises: analyzing the chain effect of the fault according to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combining the power grid topology information and the fault analysis result, and obtaining the fault impact analysis result; and then: The grid dispatching strategy is optimized in combination with the real-time grid operation status and the fault impact analysis result to obtain an optimized grid strategy.

6. An electromagnetic transient fault analysis device based on a large model, characterized in that: include: An information processing unit, used for preprocessing the real-time operation information obtained from the power grid to obtain time-series operation information; A fault analysis unit, configured to perform fault reasoning analysis according to the timing operation information based on a preset fault analysis large model and a thinking chain technology, and obtain a fault analysis result, wherein the fault analysis result includes a fault location, a fault type and a fault root cause; The impact analysis unit is used to analyze the chain effect of the fault according to the spatiotemporal feedback reasoning chain in the preset fault analysis large model, combined with the power grid topology information and the fault analysis result, to obtain the fault impact analysis result.

7. The large model-based electromagnetic transient fault analysis device according to claim 6, characterized in that: The fault analysis unit is specifically used for: Using the fault detection mechanism based on the thinking chain technology in the preset fault analysis model to perform fault detection and analysis according to the timing operation information, and using the multi-layer analysis mechanism to determine the fault location and fault type; The root cause of the fault is analyzed by using the fault root cause analysis mechanism based on the thought chain technology to obtain the root cause of the fault.

8. The large model-based electromagnetic transient fault analysis device according to claim 6, characterized in that: Also includes: A fault prediction unit, used to predict electromagnetic transient faults at future moments according to the fault analysis result and the fault impact analysis result, to obtain a fault prediction result; The warning generation unit is used to generate warning information according to the fault prediction result and perform fault warning.

9. The large model-based electromagnetic transient fault analysis device according to claim 6, characterized in that: Also includes: The strategy optimization unit is used to optimize the power grid dispatching strategy in combination with the real-time power grid operation status and the fault impact analysis result to obtain an optimized power grid strategy.

10. Electromagnetic transient fault analysis equipment based on large model, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the large model-based electromagnetic transient fault analysis method described in any one of claims 1 to 5 according to the instructions in the program code.

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