Fault tracing method, system and equipment for power transmission and transformation equipment and medium

Abstract

The invention discloses a fault tracing method, system and device for power transmission and transformation equipment and a medium, and the method comprises the following steps: constructing a digital twin model corresponding to each piece of power transmission and transformation equipment, and determining a target fault device according to the digital twin model; obtaining operation data corresponding to the target fault equipment, and determining an initial fault point corresponding to the target fault equipment according to the operation data; determining a fault point screening mode according to the fault equipment type corresponding to the target fault equipment; and screening the initial fault point through the fault point screening mode to obtain a target fault point corresponding to the target fault equipment. According to the method, the target fault equipment possibly having the fault is determined according to the digital twin model, then the initial fault point is preliminarily determined according to the operation data corresponding to the target fault equipment, and then the initial fault point is screened in the fault point screening mode, so that the target fault point corresponding to the power transmission and transformation equipment is accurately and effectively obtained.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, system, equipment and medium for tracing faults in power transmission and transformation equipment. Background Technology

[0002] As a core component of modern power systems, power transmission and transformation systems play a vital role in ensuring economic activities and daily life. The reliability of transmission and transformation equipment directly affects the stability and security of the entire power system. However, due to equipment aging, environmental factors, and improper operation, these transmission and transformation equipment may fail, causing power supply interruptions and even serious economic losses and social impacts. Therefore, accurately and effectively identifying the fault points of transmission and transformation equipment has become an urgent problem to be solved. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method, system, equipment and medium for tracing the source of faults in power transmission and transformation equipment to solve the problem of how to accurately and effectively determine the fault point of power transmission and transformation equipment.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for tracing the source of faults in power transmission and transformation equipment, comprising the following steps: constructing a digital twin model corresponding to each power transmission and transformation equipment, and determining a target faulty equipment based on the digital twin model; acquiring operating data corresponding to the target faulty equipment, and determining an initial fault point corresponding to the target faulty equipment based on the operating data; determining a fault point filtering method based on the faulty equipment type corresponding to the target faulty equipment; and filtering the initial fault point using the fault point filtering method to obtain the target fault point corresponding to the target faulty equipment.

[0006] As a preferred embodiment of the fault tracing method for power transmission and transformation equipment according to the present invention, the step of determining the target faulty device based on the digital twin model includes: constructing a digital twin model corresponding to each power transmission and transformation equipment, wherein the digital twin model includes a physical driving model and a data driving model; obtaining initial physical information corresponding to the physical driving model and initial data information corresponding to the data driving model; determining a first faulty device based on the initial physical information and determining a second faulty device based on the initial data information; and determining the target faulty device based on the first faulty device and the second faulty device.

[0007] The beneficial effects of this preferred technical solution are as follows: by using physical-driven and data-driven dual-model collaborative verification, multi-dimensional and cross-verification of faulty equipment is achieved, which improves the comprehensiveness and accuracy of faulty equipment identification and effectively reduces the risk of missed and false judgments.

[0008] In a preferred embodiment of the fault tracing method for power transmission and transformation equipment according to the present invention, the steps for determining the first faulty device and the second faulty device include: determining the equipment type corresponding to each power transmission and transformation equipment and obtaining the standard physical information corresponding to the equipment type; comparing the initial physical information with the standard physical information and determining the first faulty device based on the comparison result; determining each type of data information in the initial data information and obtaining the data change situation corresponding to each type of data information; and determining the second faulty device based on the data change situation.

[0009] In a preferred embodiment of the fault tracing method for power transmission and transformation equipment according to the present invention, the step of determining the initial fault point corresponding to the target faulty equipment based on the operating data includes: acquiring the operating data corresponding to the target faulty equipment and identifying abnormal data in the operating data; determining the abnormal data type corresponding to the abnormal data and determining the abnormal factors based on the abnormal data type; and determining the initial fault point corresponding to the target faulty equipment based on the abnormal factors.

[0010] The beneficial effects of this preferred technical solution are as follows: by using a progressive diagnostic logic from abnormal data to abnormal factors and then to the fault point, the complex fault phenomenon is decomposed layer by layer, realizing accurate tracing from "appearance" to "root cause", and improving the efficiency and accuracy of the initial fault point location.

[0011] As a preferred embodiment of the fault tracing method for power transmission and transformation equipment according to the present invention, the step of obtaining the target fault point corresponding to the target fault equipment includes: adjusting the fault point screening method according to the environmental information of the target fault equipment to obtain the adjusted screening method; screening the initial fault point through the adjusted screening method to obtain the screened fault point; and selecting the target fault point from the screened fault point.

[0012] The beneficial effects of this preferred technical solution are as follows: by introducing environmental information to adjust the screening strategy, the fault tracing method has environmental adaptability, effectively eliminates false fault points caused by external environmental interference, and improves the robustness and practicality of fault location under complex real-world working conditions.

[0013] As a preferred embodiment of the fault tracing method for power transmission and transformation equipment described in this invention, the step of selecting a target fault point from the screened fault points includes: determining the optimization strategy corresponding to the screened fault points; optimizing the screened fault points using the optimization strategy to obtain optimized fault points; and when the optimized fault point is in a fault state, using the optimized fault point as the target fault point.

[0014] As a preferred embodiment of the fault tracing method for power transmission and transformation equipment described in this invention, when the optimized fault point is used as the target fault point, the method further includes: performing state simulation on the optimized fault point through the digital twin model, and when the matching degree between the simulation result and the operating data reaches a preset threshold, using the optimized fault point as the target fault point.

[0015] Secondly, the present invention provides a fault tracing system for power transmission and transformation equipment, comprising: The equipment identification module is used to construct digital twin models corresponding to each power transmission and transformation equipment, and to identify the target faulty equipment based on the digital twin models. The fault point determination module is used to determine the operating data corresponding to the target faulty device, and determine the initial fault point corresponding to the target faulty device based on the operating data; The method determination module is used to determine the fault point filtering method based on the fault equipment type corresponding to the target fault equipment. The fault point filtering module is used to filter the initial fault points through the fault point filtering method to obtain the target fault point corresponding to the target fault device.

[0016] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the fault tracing method for power transmission and transformation equipment.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the fault tracing method for the power transmission and transformation equipment.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing digital twin models corresponding to each power transmission and transformation equipment, and determining the target faulty equipment based on the digital twin models, then determining the operating data corresponding to the target faulty equipment, and determining the initial fault point corresponding to the target faulty equipment based on the operating data, then determining the fault point screening method based on the faulty equipment type corresponding to the target faulty equipment, and then screening the initial fault point through the fault point screening method to obtain the target fault point corresponding to the target faulty equipment. This application first determines the target faulty equipment that may have a fault based on the digital twin model, then preliminarily determines the initial fault point based on the operating data corresponding to the target faulty equipment, and then screens the initial fault point through the fault point screening method, thereby accurately and effectively obtaining the target fault point corresponding to the power transmission and transformation equipment. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall process of a fault tracing method for power transmission and transformation equipment according to an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a fault tracing method for power transmission and transformation equipment is provided, comprising the following steps S100~S400: S100. Construct digital twin models corresponding to each power transmission and transformation equipment, and determine the target faulty equipment based on the digital twin models.

[0023] S200. Obtain the operating data corresponding to the target faulty device, and determine the initial fault point corresponding to the target faulty device based on the operating data.

[0024] S300. Determine the fault point screening method according to the fault equipment type corresponding to the target fault equipment.

[0025] S400. The initial fault points are filtered using the fault point filtering method to obtain the target fault point corresponding to the target fault device.

[0026] It should be noted that power transmission and transformation equipment is diverse and complex, and the mapping relationship between its internal faults and external operating data is highly nonlinear and uncertain. Traditional fault diagnosis methods often rely on single data analysis or physical models, which are prone to misjudgment or omission when faced with multi-source and heterogeneous fault information, and it is difficult to quickly locate the true root cause of the fault from a massive amount of alarms. At the same time, different equipment types and their external environments have a decisive influence on the manifestation of fault points and the screening logic, and a lack of targeted screening strategies will reduce the efficiency of source tracing.

[0027] Therefore, to address the aforementioned issues of inaccurate fault location and low source tracing efficiency, the steps S100-S400 are implemented as follows: First, a digital twin model is used to achieve accurate identification of faulty equipment driven by both physical and data aspects; then, in-depth mining of operational data is used to initially delineate the fault point; next, the optimal screening strategy is adaptively selected based on the equipment type and dynamically optimized in conjunction with environmental factors; finally, rapid, accurate, and automated source tracing from fault symptoms to fault root causes is achieved, thereby improving the intelligence level and safety and reliability of power grid operation and maintenance.

[0028] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a fault tracing method for power transmission and transformation equipment is provided.

[0029] In this embodiment of the application, step S100, which involves determining the target faulty device based on the digital twin model, includes steps A1 to A4: A1. Construct digital twin models corresponding to each power transmission and transformation equipment. The digital twin models include physical driving models and data driving models.

[0030] It should be understood that this embodiment can construct digital twin models corresponding to each power transmission and transformation equipment. The digital twin model may include a physical-driven model and a data-driven model. The physical-driven model is established based on the physical principles, mathematical models and engineering knowledge of the equipment or system. The data-driven model relies on a large amount of historical data and real-time data, and uses statistical methods, machine learning algorithms and other methods to learn patterns and rules from the data.

[0031] A2. Obtain the initial physical information corresponding to the physical driving model and the initial data information corresponding to the data driving model.

[0032] Understandably, the initial physical information corresponding to a physics-driven model can include design and manufacturing data: equipment technical specifications, material properties, design drawings, etc., process parameters and quality control records during manufacturing; basic physical characteristic data: theoretical calculation results based on disciplines such as electromagnetics, thermodynamics, and materials science, and results from finite element analysis and computational fluid dynamics simulations; environmental condition data: climate data such as temperature, humidity, and wind speed, and geographical location information; operating parameters: electrical parameters such as voltage, current, and power monitored in real time, and mechanical parameters under operating conditions such as vibration and pressure. The initial data information corresponding to a data-driven model can include historical operating data: long-term accumulated equipment operating parameters such as voltage, current, and temperature, fault records, maintenance history, and other data that help identify potential problems; real-time sensor monitoring data: real-time data from various sensors, such as immediate feedback provided by temperature sensors, vibration sensors, and partial discharge sensors; and external environmental data: such as meteorological data.

[0033] A3. Determine the first faulty device based on the initial physical information, and determine the second faulty device based on the initial data information.

[0034] It should be understood that the first faulty device is the device in the power transmission and transformation equipment that may be faulty, as determined based on the initial physical information, and the second faulty device is the device in the power transmission and transformation equipment that may be faulty, as determined based on the initial data information.

[0035] Furthermore, in order to accurately determine the first faulty device and the second faulty device, in this embodiment, step S103 includes: determining the device type corresponding to each power transmission and transformation device, and determining the standard physical information corresponding to the device type; comparing the initial physical information with the standard physical information, and determining the first faulty device based on the comparison result; determining each type of data information in the initial data information, and determining the data change situation corresponding to each type of data information; and determining the second faulty device based on the data change situation.

[0036] Understandably, the equipment type corresponding to each power transmission and transformation device is first determined, such as transformer, circuit breaker, disconnector, etc., and the standard physical information corresponding to the equipment type is determined. This standard physical information can be information that remains basically unchanged, such as the technical specifications and physical structure of the equipment. Then, the initial physical information is compared with the standard physical information. When the two are basically consistent, it is determined that there is no first fault device in the power transmission and transformation equipment. When the two are inconsistent, the corresponding power transmission and transformation equipment is regarded as the first fault device.

[0037] It should be understood that the initial data information may include various types of data information, such as voltage data and vibration data. The data change situation corresponding to each type of data information can be determined, and then the second faulty equipment can be identified based on the data change situation. For example, if the data change situation is a sudden change or the data change situation is a low value, the corresponding power transmission and transformation equipment can be identified as the second faulty equipment.

[0038] A4. Determine the target faulty device based on the first faulty device and the second faulty device.

[0039] In a practical implementation, there may be multiple first fault devices and multiple second fault devices. The intersection between the first fault devices and the second fault devices can be taken as the target fault device. There may also be multiple target fault devices.

[0040] This embodiment constructs digital twin models corresponding to each power transmission and transformation device. These digital twin models include physical-driven models and data-driven models. Then, it determines the initial physical information corresponding to the physical-driven model and the initial data information corresponding to the data-driven model. Based on the initial physical information, it identifies the first faulty device, and based on the initial data information, it identifies the second faulty device. Finally, based on the first and second faulty devices, it identifies the target faulty device. This embodiment first constructs physical-driven and data-driven models corresponding to each power transmission and transformation device, then determines the first faulty device based on the initial physical information and the second faulty device based on the initial data information. This allows for the identification of the first and second faulty devices in the power transmission and transformation equipment through different methods, thereby accurately and effectively identifying the target faulty device based on the first and second faulty devices.

[0041] In an optional implementation, step S100, which determines the target faulty device based on the digital twin model, can also perform fault prediction based on multiphysics coupling simulation. Specifically, when constructing the physical driving model, not only are single electromagnetic or mechanical characteristics considered, but a high-fidelity model of electro-thermal-mechanical multiphysics coupling is established. This model simulates the dynamic distribution and interaction of the temperature field, electromagnetic field, and structural stress field inside the target device under extreme conditions (such as short-circuit impact and overload operation). The multiphysics results obtained from the simulation (such as hotspot temperature and deformation distribution) are used as initial physical information and compared with the tolerance threshold of the device material. When the simulation results show that the physical quantity of a device exceeds the safety threshold, even if the current real-time data does not show obvious abnormalities, the device can be predicted as the first faulty device, thus achieving an advancement from "post-event diagnosis" to "pre-event warning," which is particularly suitable for identifying early latent faults in core main equipment (such as transformers and GIS).

[0042] In another optional implementation, in step S100, the target faulty device is determined based on the digital twin model, and a graph neural network can also be used to mine the device correlation relationships. Specifically, the entire power transmission and transformation network topology is regarded as a graph, where nodes represent power transmission and transformation equipment and edges represent electrical connections. Based on this, a data-driven model based on a graph neural network is constructed. This model not only learns the historical and real-time data of individual devices (node ​​features), but also learns the electrical correlations and influences between devices (edge ​​features). When a fault occurs somewhere in the network, the fault features will propagate along the topological path. The graph neural network can capture this complex spatial correlation and fault propagation pattern, identify abnormal correlation states from the initial data information of the entire network, and thus more accurately locate the fault source device, i.e., the second faulty device. This method is particularly suitable for solving the problem of fault point ambiguity caused by protection failure to operate, over-level tripping, etc., and can effectively improve the accuracy of fault tracing in complex power grid structures.

[0043] In this embodiment of the application, step S200, which involves determining the initial fault point corresponding to the target faulty device based on the operating data, includes: acquiring the operating data corresponding to the target faulty device and determining abnormal data in the operating data; determining the abnormal data type corresponding to the abnormal data and determining the abnormal factors based on the abnormal data type; and determining the initial fault point corresponding to the target faulty device based on the abnormal factors.

[0044] Understandably, the operating data obtained may differ for different types of target fault equipment. For example, the operating data for transformers includes load rate, voltage, and current, while the operating data for circuit breakers includes the number of opening and closing cycles, opening time, and closing time.

[0045] It should be understood that abnormal data in the operating data can be identified. Specifically, the operating data can be fitted, and abrupt changes in the fitted curve can be considered abnormal data. Alternatively, a threshold range can be set for the operating data, and if the operating data is outside this range, it can be identified as abnormal data.

[0046] In practical implementation, the abnormal data type corresponding to the abnormal data can be determined. Abnormal data types may include those mentioned above, such as load rate, voltage, current, and number of closing cycles. Based on the abnormal data type, the abnormal factors can be determined. These factors can be the causes that generated the abnormal data. For example, if the number of circuit breaker closing cycles is abnormal, the abnormal factors could be factors such as the power system needing to restore power or switch loads, short circuits, or overloads. Then, based on the abnormal factors, the initial fault point corresponding to the target faulty equipment can be determined. The initial fault point is the location on the transmission and transformation equipment that caused the abnormal factor, such as contact parts or insulation materials.

[0047] In this embodiment of the application, step S400, the step of obtaining the target fault point corresponding to the target faulty device, includes B1~B3: B1. Based on the environmental information of the target faulty device, adjust the fault point screening method to obtain the adjusted screening method.

[0048] Understandably, the environmental information of the target faulty device can be determined, including weather and temperature information. The fault point selection method can be adjusted based on this environmental information. For example, severe weather may affect the effectiveness of drone inspections and increase the difficulty of cable path identification; changes in ground conditions may also affect the accuracy of the audio sensing method. Therefore, the fault point selection method that affects environmental information can be adjusted, or alternative fault point selection methods for the same equipment type can be used instead.

[0049] B2. The initial fault points are filtered using the adjusted filtering method to obtain the filtered fault points.

[0050] It should be understood that the adjusted fault points can be determined by the adjusted screening method, and the fault points that are the same as the initial fault points and the adjusted fault points are used as the screened fault points.

[0051] B3. Select the target fault point from the filtered fault points.

[0052] Furthermore, in order to effectively select target fault points, in this embodiment, step B3 includes: determining the optimization strategy corresponding to the screened fault points; optimizing the screened fault points through the optimization strategy to obtain optimized fault points; and when the optimized fault points are in a fault state, using the optimized fault points as target fault points.

[0053] Understandably, after identifying the fault points, some fault points can be automatically optimized. For example, for transient faults, such as flashovers caused by lightning strikes, the automatic reclosing device can attempt to reclose the circuit breaker after it trips in order to quickly restore power supply.

[0054] In practical implementation, the optimization strategy for the selected fault points can be determined. The optimization strategy refers to the strategy for automatically optimizing the fault points. Through the optimization strategy, the selected fault points can be optimized to obtain optimized fault points. When the optimized fault point is not in a fault state, it means that the fault corresponding to the optimized fault point has disappeared and the optimized fault point can not be used as the target fault point. When the optimized fault point is in a fault state, it means that the fault corresponding to the optimized fault point still exists and the optimized fault point can be used as the target fault point.

[0055] When using the optimized fault point as the target fault point, the method further includes: performing state simulation on the optimized fault point using the digital twin model, and using the optimized fault point as the target fault point when the matching degree between the simulation result and the running data reaches a preset threshold.

[0056] In an optional implementation, step S400, which involves selecting a target fault point from the screened fault points, can also employ a dynamic weighted decision-making method based on multi-source information fusion. Specifically, a comprehensive evaluation system is constructed for each screened fault point. This system integrates multi-dimensional information sources, such as: the simulation confidence of the fault point in the digital twin model, the frequency of its historical fault occurrences, the matching degree between real-time sensor data and the fault mode, and the estimated cost and time of maintenance operations. A dynamic weight is assigned to each of the above dimensions, which can be adaptively adjusted according to the current power grid operation mode (such as during power supply protection or maintenance) and the urgency of the fault. Finally, a comprehensive score is calculated for each fault point, and the fault point with the highest score exceeding a preset threshold is determined as the target fault point. This method, through multi-objective optimization decision-making, ensures that the selected fault point in complex scenarios is not only technically accurate but also optimal in terms of operation and maintenance strategy.

[0057] In another optional implementation, step S400, which selects a target fault point from the filtered fault points, can also introduce a reverse verification mechanism based on digital twins. Specifically, after obtaining the optimized fault point, it is not immediately used as the final target. Instead, this fault point and its corresponding fault mode are reverse-injected into the digital twin model corresponding to the target fault device to simulate the full-dimensional response data (i.e., "fault signature") that the entire device should generate when a set fault occurs at this fault point. Subsequently, the simulated "fault signature" is holographically compared with the real operating data obtained in step S200. The fault point with the highest matching degree with the real data in terms of temporal pattern, spectral characteristics, amplitude changes, etc., is selected as the final confirmed target fault point. This method achieves closed-loop verification of candidate fault points by creating a "virtual fault test field," greatly improving the certainty and accuracy of fault location and effectively avoiding misjudgments caused by correlated or concurrent faults.

[0058] In summary, by constructing digital twin models for each power transmission and transformation device, identifying target faulty devices based on these models, determining the corresponding operational data for each target faulty device, and identifying the initial fault point based on this data, then determining the fault point filtering method based on the fault type of the target faulty device, and finally filtering the initial fault points using this method, the target fault point corresponding to the target faulty device is obtained. This application first identifies potential target faulty devices based on the digital twin model, then preliminarily determines the initial fault point based on the operational data of the target faulty device, and finally filters the initial fault point using a fault point filtering method, thereby accurately and effectively obtaining the target fault point corresponding to the power transmission and transformation equipment.

[0059] Example 3 illustrates a schematic scheme for a fault tracing method for power transmission and transformation equipment. It should be noted that the technical solution of this fault tracing system for power transmission and transformation equipment is based on the same concept as the technical solution of the aforementioned fault tracing method. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned fault tracing method.

[0060] This embodiment also provides a fault tracing system for power transmission and transformation equipment, including: The equipment identification module is used to construct digital twin models corresponding to each power transmission and transformation equipment, and to identify the target faulty equipment based on the digital twin models. The fault point determination module is used to determine the operating data corresponding to the target faulty device, and determine the initial fault point corresponding to the target faulty device based on the operating data; The method determination module is used to determine the fault point filtering method based on the fault equipment type corresponding to the target fault equipment. The fault point filtering module is used to filter the initial fault points through the fault point filtering method to obtain the target fault point corresponding to the target fault device.

[0061] This embodiment also provides an electronic device suitable for fault tracing of power transmission and transformation equipment, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the fault tracing method for power transmission and transformation equipment as proposed in the above embodiment.

[0062] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the fault tracing method for power transmission and transformation equipment as proposed in the above embodiments.

[0063] The storage medium proposed in this embodiment and the fault tracing method for power transmission and transformation equipment proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0064] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for tracing a fault of a power transmission and distribution equipment, characterized by, The method comprises the following steps: constructing a digital twin model corresponding to each power transmission and transformation device, and determining a target fault device according to the digital twin model; obtaining operation data corresponding to the target fault device, and determining an initial fault point corresponding to the target fault device according to the operation data; determining a fault point screening mode according to a fault device type corresponding to the target fault device; screening the initial fault point through the fault point screening mode to obtain a target fault point corresponding to the target fault device.

2. The power transmission equipment failure tracing method of claim 1, wherein, The step of determining a target fault device according to the digital twin model comprises: constructing a digital twin model corresponding to each power transmission and transformation device, wherein the digital twin model comprises a physical driving model and a data driving model; obtaining initial physical information corresponding to the physical driving model and initial data information corresponding to the data driving model; determining a first fault device according to the initial physical information, and determining a second fault device according to the initial data information; determining a target fault device according to the first fault device and the second fault device.

3. The power transmission equipment failure tracing method of claim 2, wherein, The step of determining the first fault device and the second fault device comprises: judging a device type corresponding to each power transmission and transformation device, and obtaining standard physical information corresponding to the device type; comparing the initial physical information with the standard physical information, and determining a first fault device according to a comparison result; determining each type of data information in the initial data information, and obtaining a data change condition corresponding to each type of data information; determining a second fault device according to the data change condition.

4. The power transmission equipment failure tracing method of claim 3, wherein, The step of determining an initial fault point corresponding to the target fault device according to the operation data comprises: obtaining operation data corresponding to the target fault device, and determining abnormal data in the operation data; judging an abnormal data type corresponding to the abnormal data, and determining an abnormal factor according to the abnormal data type; determining an initial fault point corresponding to the target fault device according to the abnormal factor.

5. The method of claim 4, wherein the step of identifying the faulted component comprises the steps of: determining a faulted component based on the faulted component information; and determining a faulted component based on the faulted component information and the faulted component information of the power transmission equipment. The step of obtaining a target fault point corresponding to the target fault device comprises: adjusting the fault point screening mode according to environmental information in which the target fault device is located, to obtain an adjusted screening mode; screening the initial fault point through the adjusted screening mode to obtain a screened fault point; selecting a target fault point from the screened fault point.

6. The method of claim 5, wherein the step of identifying the faulted component comprises: The step of selecting a target fault point from the screened fault point comprises: determining an optimization strategy corresponding to the screened fault point; optimizing the screened fault point through the optimization strategy to obtain an optimized fault point; when the optimized fault point is in a fault state, regarding the optimized fault point as a target fault point.

7. The power transmission equipment failure tracing method of claim 6, wherein, When the optimized fault point is regarded as a target fault point, the method further comprises: performing state simulation on the optimized fault point through the digital twin model, and when a matching degree between a simulation result and the operation data reaches a preset threshold, regarding the optimized fault point as a target fault point.

8. A fault tracing system for power transmission equipment, applying the method according to any one of claims 1 to 7, characterized in that, The method comprises: A device determination module is configured to construct a digital twin model corresponding to each power transmission and transformation device, and determine a target fault device according to the digital twin model; A fault point determination module is configured to determine operation data corresponding to the target fault device, and determine an initial fault point corresponding to the target fault device according to the operation data; A mode determination module is configured to determine a fault point screening mode according to a fault device type corresponding to the target fault device; A fault point screening module is configured to screen the initial fault point through the fault point screening mode, and obtain a target fault point corresponding to the target fault device.

9. An electronic device, comprising: a memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which realize the steps of the fault tracing method of the power transmission and transformation device according to any one of claims 1 to 7 when executed by the processor.

10. A computer readable storage medium storing computer executable instructions, which realize the steps of the fault tracing method of the power transmission and transformation device according to any one of claims 1 to 7 when executed by a processor.

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