Distribution network line fault detection method and system, electronic equipment and storage medium

Abstract

The invention provides a distribution network line fault detection method and system, electronic equipment and a storage medium. The method comprises the following steps: constructing a fault model library for a distribution network line; acquiring real-time magnetic field variable data of the distribution network line and performing data preprocessing; performing comparative analysis based on the real-time magnetic field variable data after data preprocessing through a fault model library to obtain a comparison result; and performing fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result. According to the distribution network line fault detection scheme provided by the invention, the operation efficiency and safety of a distribution network can be remarkably improved, the unplanned power failure time is reduced, the service life of equipment is prolonged, the maintenance cost is reduced, the maintenance plan is optimized, the power supply reliability is improved, and the development of an intelligent power grid is promoted.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network line inspection, and in particular to a distribution network line fault detection method, system, electronic equipment and storage medium. Background Art

[0002] In the daily operation of the power system, the stability and reliability of the distribution network lines are crucial. However, the traditional distribution network line operation fault inspection method faces many challenges, which not only limit the efficiency of the inspection, but also affect the quality and safety of power supply.

[0003] First, the existing inspection method is highly dependent on the personal experience and skills of the inspectors, which limits the accuracy of this method because the differences in professional level and experience of different inspectors may lead to misjudgment or omission of potential hidden dangers. This dependence not only increases the risk of human error, but also limits the consistency and reliability of inspection results.

[0004] Secondly, manual inspections pose safety risks. Inspectors may be exposed to high voltage electricity, severe weather or other dangerous environments when performing their tasks, which not only poses a threat to their personal safety, but may also cause inspections to be interrupted due to emergencies, affecting the continuity and integrity of the inspection work.

[0005] In addition, environmental factors pose significant limitations to traditional inspections. Inspections are often difficult to carry out under severe weather conditions, such as heavy rain, snow or fog, which may lead to delayed discovery of faults and hidden dangers and increase the risk of power grid operation.

[0006] The lag in technology updates is also an issue that cannot be ignored. With the continuous emergence of new technologies and new equipment, traditional inspection methods often find it difficult to adapt to these changes and fail to fully utilize the advantages brought by new technologies, thus affecting the effectiveness of inspections and the modernization level of the power grid. In terms of data processing, traditional methods also have obvious shortcomings. They are usually unable to effectively collect, process and analyze large amounts of data, which limits the ability to monitor and warn of the power grid status in real time. The lack of efficient data processing and analysis capabilities makes it difficult for power grid operators to respond to faults and abnormal situations in a timely manner, affecting the response speed and recovery capabilities of the power grid.

[0007] Finally, the low degree of automation makes it more difficult to inspect urban distribution networks in complex scenarios. In urban environments, the complexity and diversity of distribution lines require inspection methods to be more flexible and adaptable. However, traditional methods often fail to meet these requirements, resulting in low inspection efficiency and difficulty in coping with the growing demand for grid operations.

[0008] In summary, there is an urgent need for a new and efficient method for defect detection of distribution network line facilities to improve the accuracy, safety and automation level of inspections and ensure the stable operation of the power system and the quality of power supply to users.

[0009] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the invention

[0010] In response to the above technical problems, the present application provides a distribution network line fault detection method, system, electronic device and storage medium to solve the problems of low inspection efficiency, insufficient safety and low accuracy in existing inspection technologies, which lead to increased risks in power grid operation.

[0011] In order to solve the above technical problems, the present application provides a distribution network line fault detection method, comprising the following steps:

[0012] Build a fault model library for distribution network lines;

[0013] Acquiring real-time magnetic field variable data of the distribution network line and performing data preprocessing;

[0014] Comparison analysis is performed based on the real-time magnetic field variable data after data preprocessing by the fault model library to obtain a comparison result;

[0015] Based on the comparison result, fault diagnosis is performed on the distribution network line to obtain a fault diagnosis result.

[0016] Furthermore, in some embodiments of the present application, the method further includes:

[0017] Based on the fault diagnosis result, determining the early warning control strategy corresponding to the distribution network line;

[0018] Fault processing is performed on the distribution network line based on the early warning control strategy.

[0019] Furthermore, in some embodiments of the present application, the step of constructing a fault type library for distribution network lines includes:

[0020] Acquire magnetic field data of the distribution network line, wherein the magnetic field data includes simulated magnetic field data and historical magnetic field data;

[0021] Extracting fault characteristic parameters corresponding to the magnetic field data, wherein the fault characteristic parameters include magnetic field intensity, magnetic field distribution and magnetic field frequency;

[0022] Analyzing and filtering the fault characteristic parameters to obtain a fault characteristic quantity;

[0023] Constructing an initial fault type library corresponding to the distribution network line based on the fault characteristic quantity;

[0024] Model training is performed on the fault classification model in the initial fault type library based on the magnetic field data to obtain a trained fault type library.

[0025] Furthermore, in some embodiments of the present application, the acquiring of the real-time magnetic field variable data of the distribution network line and performing data preprocessing includes:

[0026] Acquiring real-time magnetic field variable data of the distribution network line through a non-contact electromagnetic induction module;

[0027] After data preprocessing is performed on the real-time magnetic field variable data, magnetic field characteristic data is obtained; the data preprocessing includes data cleaning, feature extraction and data standardization.

[0028] Further, in some embodiments of the present application, the comparison analysis is performed based on the real-time magnetic field variable data after data preprocessing by the fault model library to obtain a comparison result, including:

[0029] Acquire real-time magnetic field characteristic data corresponding to the real-time magnetic field variable data after the data preprocessing;

[0030] A comparison result is obtained based on a comparison analysis between the real-time magnetic field characteristic data and the magnetic field characteristic standard data in the fault model library.

[0031] Further, in some embodiments of the present application, the performing fault diagnosis on the distribution network line based on the comparison result to obtain the fault diagnosis result includes:

[0032] If it is detected that the comparison result is that there is no fault, determining the fault diagnosis result is that there is no fault in the distribution network line at present;

[0033] If it is detected that the comparison result is a fault, the real-time magnetic field characteristic data is fault classified by the fault classification model in the fault model library to obtain a fault classification result, and a fault diagnosis result is determined based on the fault classification result.

[0034] Further, in some embodiments of the present application, determining the early warning control strategy corresponding to the distribution network line based on the fault diagnosis result includes:

[0035] Pre-set fault thresholds corresponding to various types of faults based on distribution network requirements;

[0036] Obtaining fault classification information and corresponding fault characteristic parameters in the fault diagnosis result;

[0037] If it is detected that the fault characteristic parameter is greater than the fault threshold corresponding to the fault category, a warning message is issued;

[0038] Based on the warning information, a warning control strategy corresponding to the distribution network line is determined.

[0039] Accordingly, the present application also provides a distribution network line fault detection system, comprising:

[0040] A construction module is used to construct a fault model library for distribution network lines;

[0041] An acquisition module, used to acquire real-time magnetic field variable data of the distribution network line and perform data preprocessing;

[0042] A comparison module, used for performing comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain a comparison result;

[0043] The diagnosis module is used to perform fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result.

[0044] The present application also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the distribution network line fault detection method as described above when executing the program.

[0045] The present application also provides a storage medium storing a computer program that can be loaded by a processor and execute the distribution network line fault detection method as described above.

[0046] Implementing the embodiments of the present application has the following beneficial effects:

[0047] As described above, the present application provides a distribution network line fault detection method, system, electronic device and storage medium, and the distribution network line fault detection method includes: constructing a fault model library for the distribution network line; obtaining the real-time magnetic field variable data of the distribution network line and performing data preprocessing; performing comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain a comparison result; performing fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result. The present application provides a distribution network line fault detection scheme, which can quickly identify potential defects and faults by real-time detection of magnetic field changes in the distribution network line, improve the timeliness and accuracy of fault detection, and use the fault model library comparison diagnosis method and secondary verification analysis to effectively determine the distribution network line fault problem and fault type, and improve the efficiency and intelligence of fault detection. It can be seen that the present application can significantly improve the operating efficiency and safety of the distribution network, reduce unplanned power outage time, extend equipment life, reduce maintenance costs, optimize maintenance plans, improve power supply reliability, and promote the development of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0049] Figure 1 It is a flow chart of a distribution network line fault detection method provided in an embodiment of the present application;

[0050] Figure 2 is another flow chart of the distribution network line fault detection method provided in an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of the fault type provided in the embodiment of the present application;

[0052] Figure 4 It is a flowchart of data processing provided by an embodiment of the present application;

[0053] Figure 5 is a schematic diagram of a fault model library provided in an embodiment of the present application;

[0054] Figure 6 It is a schematic diagram of the principle of the Hall effect provided in an embodiment of the present application;

[0055] Figure 7 It is a flow chart of the implementation logic of the Hall electromagnetic induction module provided in the embodiment of the present application;

[0056] Figure 8 is a schematic diagram of a fault diagnosis process provided by an embodiment of the present application;

[0057] Fig. 9 It is a flowchart of the early warning mechanism provided by the embodiment of the present application;

[0058] Fig.10 It is a structural diagram of a distribution network line fault detection system provided in an embodiment of the present application;

[0059] Fig.11 is another structural schematic diagram of the distribution network line fault detection system provided in an embodiment of the present application;

[0060] Fig.12 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0061] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0064] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0066] Traditional distribution network line operation fault inspection methods face a series of challenges. Since the judgment of potential hidden dangers relies on manual experience, the accuracy is limited and misjudgment may occur due to differences in personal skills. In addition, manual inspections have safety risks, and the omission of minor problems may lead to large-scale service interruptions and economic losses. Environmental factors also limit traditional inspections, and it is difficult to conduct effective inspections in bad weather. The lag in technological updates further affects the inspection results, especially when dealing with new technologies and equipment. At the same time, in terms of data processing, traditional methods cannot effectively collect, process and analyze data to achieve real-time monitoring and early warning of power grid status. Finally, the low degree of automation makes urban distribution network inspections more difficult in complex scenarios.

[0067] In response to the above technical problems, the embodiments of the present application provide a distribution network line fault detection method, device, electronic device and storage medium, which judge whether there is a fault in the line by sensing the magnetic field variable characteristics of the distribution network line, solve the problem of limited accuracy caused by reliance on manual experience, and solve the impact of harsh environment on traditional detection methods.

[0068] See also Figure 1 , Figure 1 : is a flow chart of a distribution network line fault detection method provided in an embodiment of the present application. The distribution network line fault detection method provided in this embodiment may specifically include the following steps:

[0069] S1. Build a fault model library for distribution network lines;

[0070] Specifically, for step S1, first, by deeply studying the fault types of the distribution network lines, collecting and analyzing historical fault data and simulated fault data, and extracting fault characteristic parameters such as magnetic field intensity, magnetic field distribution and magnetic field frequency, an initial fault type library is constructed through these parameters, and then through model training, optimization and verification of the fault classification model, an accurate fault model library is formed.

[0071] S2. Obtain real-time magnetic field variable data of the distribution network line and perform data preprocessing;

[0072] Specifically, for step S2, a non-contact electromagnetic induction module or other equipment is used to collect magnetic field data of the distribution network line in real time, and pre-processing is performed through steps such as data cleaning, feature extraction and data standardization to obtain magnetic field feature data.

[0073] S3. Performing comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain a comparison result;

[0074] Specifically, for step S3, the pre-processed real-time magnetic field characteristic data is compared and analyzed with the standard data in the fault model library. This step uses advanced data analysis techniques, such as pattern recognition and machine learning algorithms, to identify deviations in magnetic field characteristics, thereby discovering potential fault signs.

[0075] S4. Perform fault diagnosis on the distribution network line based on the comparison results to obtain the fault diagnosis results;

[0076] Specifically, for step S4, based on the results of the comparison analysis, a fault diagnosis is performed. If the comparison result shows that the magnetic field characteristics are consistent with the magnetic field characteristics of the normal operating state, the diagnosis result is no fault; if there is a deviation, the fault classification model in the fault model library is used to classify the magnetic field characteristics, determine the fault type, and finally generate a fault diagnosis result.

[0077] The distribution network line fault detection method provided in this embodiment reduces the reliance on manual experience and improves the accuracy of fault detection through quantitative analysis of magnetic field variable characteristics; adopts non-contact data acquisition technology to reduce the safety risks of manual inspections; real-time monitoring and rapid diagnosis capabilities significantly improve the fault response speed, reduce the time for fault detection and processing, and improve fault detection efficiency; early identification of facility defects can prevent faults from occurring, reduce unplanned power outages, extend equipment life, and reduce maintenance costs; supports the automation and intelligence of smart grids, helps to achieve real-time monitoring and early warning of grid status, and promotes the development of power systems in a more efficient, environmentally friendly, and user-friendly direction.

[0078] Furthermore, if Figure 2 As shown, in some embodiments of the present application, the method further includes:

[0079] S5. Based on the fault diagnosis results, determine the early warning control strategy corresponding to the distribution network line;

[0080] S6. Handle distribution network line faults based on early warning control strategies;

[0081] Specifically, the distribution network line fault detection method provided in this embodiment also includes the steps of determining the early warning control strategy corresponding to the distribution network line based on the fault diagnosis results, and performing fault processing based on the strategy. First, based on the specific needs of the distribution network and the analysis of historical fault data, the fault thresholds are pre-set for different types of faults; fault classification information and its corresponding fault characteristic parameters, such as abnormal values ​​of magnetic field intensity, magnetic field distribution and magnetic field frequency, are extracted from the fault diagnosis results; the fault characteristic parameters are compared with the preset fault thresholds. If the parameters exceed the threshold, the system automatically issues an early warning message to indicate possible fault risks; according to the early warning information, the corresponding early warning control strategy is determined and implemented, including but not limited to adjusting the grid operation parameters, automatically isolating the fault area, notifying maintenance personnel, etc.; after the control strategy is implemented, the system will continuously monitor the status of the fault area, and optimize the early warning control strategy based on the feedback results to improve the response efficiency and accuracy.

[0082] Through real-time monitoring and rapid fault assessment, the system of this embodiment can respond to fault conditions quickly and reduce the impact of faults on power grid operation; timely fault warning and control strategy implementation help minimize the power outage time caused by faults and ensure the continuity and stability of power supply; accurate fault location and warning information make maintenance work more targeted, improve maintenance efficiency, and reduce maintenance costs; through automated fault handling and warning mechanisms, the power grid can better resist various faults and abnormal situations and enhance the overall resilience of the power grid; systematic fault handling strategies help optimize the allocation of human resources and other maintenance resources to ensure that problems can be handled quickly and effectively at critical moments.

[0083] Furthermore, in some embodiments, step S1 "building a fault type library for distribution network lines" may specifically include:

[0084] S11. Obtaining magnetic field data of the distribution network line, the magnetic field data includes simulated magnetic field data and historical magnetic field data;

[0085] S12. Extracting fault characteristic parameters corresponding to the magnetic field data, the fault characteristic parameters include magnetic field intensity, magnetic field distribution and magnetic field frequency;

[0086] S13. Analyze and filter the fault characteristic parameters to obtain the fault characteristic quantity;

[0087] S14. Constructing an initial fault type library corresponding to the distribution network line based on the fault characteristic quantity;

[0088] S15. Perform model training on the fault classification model in the initial fault type library based on the magnetic field data to obtain a trained fault type library.

[0089] Specifically, for step S1, the main task is to study the classification of distribution network line faults and establish a fault model library. Different fault types correspond to different types of magnetic field characteristics. The normal magnetic field and the fault feature magnetic field are processed and packaged to establish a feature library. The faults and defects of distribution network line facilities are analyzed and studied from different angles. Common fault types in the normal operation of distribution network lines are as follows: Figure 3 As shown, the fault types classified by nature include multi-phase grounding, single-phase grounding, phase-to-phase short circuit and grounded phase-to-phase short circuit. Among them, single-phase grounding refers to faults such as high vegetation in the line span, accidental line prolapse and accidental grounding. Phase-to-phase short circuit refers to faults caused by lightning strike, phase-to-phase short circuit of external conductors or semiconductors, reduced equipment insulation and bird damage. In terms of time, it includes transient faults and permanent faults. Among them, transient faults refer to faults such as the conditions for phase-to-phase short circuit are destroyed by arc after discharge and cannot constitute short circuit conditions. Permanent faults refer to faults that cannot be destroyed by arc short circuit conditions or require human intervention (maintenance). In terms of nature, it includes line faults and equipment faults. Line faults refer to faults such as broken wires, short circuits, grounding, overloads, etc., which may be caused by line aging, insulation damage, weather, etc. Equipment faults refer to equipment faults such as poles, insulator strings, transformers, reactors, capacitors, circuit breakers, grounding switches, lightning arresters, voltage transformers, current transformers, etc.

[0090] The premise of establishing a fault model library is to collect magnetic field data, which includes simulated magnetic field data and historical magnetic field data, such as Figure 4 As shown, this embodiment performs fault simulation and data collection through circuit simulation software, extracts useful information reflecting fault characteristics of model establishment and simulation through historical data analysis, analyzes and filters the extracted magnetic field strength, distribution, frequency and other parameters, constructs a mathematical model or database using characteristic quantities, forms the basis of a fault model library, and then verifies and optimizes the model to improve the accuracy and reliability of the fault model library.

[0091] In a specific embodiment, magnetic field data of distribution network lines are collected, and these data include simulated magnetic field data and historical magnetic field data to ensure the comprehensiveness and diversity of the model library. Fault feature parameters are extracted from the collected magnetic field data, and these parameters include at least magnetic field strength, magnetic field distribution and magnetic field frequency, which can reflect the operating status and potential faults of the distribution network lines. The extracted fault feature parameters are deeply analyzed and filtered to identify and distinguish the magnetic field characteristics under normal operation and fault conditions. Based on the fault feature quantity obtained by analysis and filtration, an initial fault type library is constructed, which contains the magnetic field characteristics of various fault types that the distribution network lines may encounter. The fault classification model in the initial fault type library is trained using magnetic field data to improve the accuracy and reliability of the model. The trained model is verified by comparison with the actual fault data, and optimized according to the results to ensure the practicality and effectiveness of the fault model library.

[0092] like Figure 5 As shown, the construction and application of the model are divided into two stages. One is the model training stage, which makes the model more reliable. The trained reliable model is compared with the collected data to improve the accuracy of fault identification.

[0093] This embodiment accurately extracts and analyzes magnetic field characteristic parameters, and the fault model library constructed can more accurately identify and classify different fault types; the model training and optimization process ensures that the fault model library can adapt to different operating conditions and fault scenarios; the trained fault model library can be quickly compared with the magnetic field data collected in real time to achieve rapid fault diagnosis; the automated fault model library construction process reduces the reliance on manual experience and improves the objectivity and consistency of fault detection; through model verification and optimization, the high reliability of the fault detection system is ensured, and the possibility of false alarms and missed alarms is reduced; the accurate fault model library provides strong data support for power grid operators, which helps to make more scientific maintenance and repair decisions; it promotes the innovation of power grid detection technology and promotes the development of power grid management and maintenance towards automation and intelligence.

[0094] Furthermore, in some embodiments, step S2 of "obtaining real-time magnetic field variable data of the distribution network line and performing data preprocessing" may specifically include:

[0095] S21. Obtain real-time magnetic field variable data of the distribution network line through a non-contact electromagnetic induction module;

[0096] S22. After preprocessing the real-time magnetic field variable data, magnetic field characteristic data is obtained; data preprocessing includes data cleaning, feature extraction and data standardization.

[0097] Specifically, for step S2, this embodiment adopts technical means such as non-contact electromagnetic induction module to safely and efficiently obtain real-time magnetic field variable data of distribution network lines, avoiding physical interference with the lines; preprocessing the collected original magnetic field data to remove noise and outliers, improve data quality, and ensure the accuracy of subsequent analysis; extract key magnetic field features from the cleaned data, such as local changes in magnetic field intensity, uneven magnetic field distribution, or changes in specific frequency components; standardize the extracted features to eliminate dimensional and magnitude differences between data collected from different lines or at different times, so as to facilitate unified model comparison and analysis; after the above preprocessing steps, magnetic field feature data are obtained, which can accurately reflect the real-time operating status of the distribution network lines.

[0098] In a specific embodiment, a non-contact Hall electromagnetic induction module is used to collect data on the magnetic field during line operation, and a characteristic magnetic field is obtained through data preprocessing. The underlying principle of this module, the Hall effect, is used to sense the magnetic field changes around the distribution network line facilities. The underlying principle is as follows:

[0099] Hall effect Figure 6 As shown, the current I flows in the line, the magnitude of the magnetic induction is Bz, the direction is perpendicular to the current, the diameter of the line is r, and the cross-sectional area is S = πr 2 , the number of free electrons per unit volume of the conductor is n, the charge of the electrons is e, and the average speed of the directional movement of the electrons is v. According to the microscopic definition of electric current:

[0100] I=nqSv=n·e·πr 2 ·v

[0101] Due to the Lorentz force, free electrons begin to drift laterally and gather on the side of the conductor, making the left side negatively charged and the right side positively charged, thus forming a potential difference. This potential difference will generate a uniform electric field in the conductor from positive charge to negative charge, with a direction perpendicular to the direction of the current and the magnetic field, and exert a force on the electrons with a magnitude of:

[0102]

[0103] Electric force F 电 and the Lorentz force F 洛 The directions are opposite, and because the free electrons gradually gather on the side, the potential difference on both sides of the conductor gradually increases (the electric field force gradually increases), resulting in the balance of the two forces. At this time, the lateral drift motion of the electrons stops and will not deviate. The last term of the formula is V H is called the Hall voltage. Finally, we can get

[0104]

[0105] After collecting data such as magnetic field through this module, useful data information is extracted through data communication and data processing, and compared with the data in the model library. Finally, the perception results are output after diagnosis. The specific implementation logic is as follows: Figure 7 shown.

[0106] This embodiment removes noise and outliers through data cleaning, improves the quality of magnetic field data, and provides a reliable data basis for subsequent analysis; feature extraction and data standardization make fault characteristics more obvious, which is convenient for accurate comparison and analysis of the fault model library; non-contact data acquisition and efficient data processing flow shorten the response time from data acquisition to fault detection; non-contact data acquisition methods reduce the need for manual inspections, reduce maintenance costs and safety risks; standardized processing ensures the adaptability of the system to different lines and environmental conditions, and improves the versatility of fault detection methods; real-time acquired magnetic field feature data supports real-time monitoring by power grid operators and triggers early warning mechanisms when necessary.

[0107] Further, in some embodiments, step S3 of "performing a comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain a comparison result" may specifically include:

[0108] S31. Obtaining real-time magnetic field characteristic data corresponding to the real-time magnetic field variable data after data preprocessing;

[0109] S32. Perform a comparison analysis based on the real-time magnetic field characteristic data and the magnetic field characteristic standard data in the fault model library to obtain a comparison result.

[0110] Specifically, for step S3, first, extract key magnetic field characteristic data from the real-time magnetic field variable data after data preprocessing, and these data will be used for comparison with the fault model library; then, perform detailed comparison and analysis on the extracted real-time magnetic field characteristic data and the magnetic field characteristic standard data in the fault model library. This step and pattern recognition, machine learning algorithms or other advanced data analysis technologies are used to identify deviations in magnetic field characteristics; in the preliminary comparison stage, the system will determine whether the real-time magnetic field characteristic data is within the normal operating range. If the magnetic field characteristic data is consistent with the standard data or within the acceptable deviation range, it is considered that the distribution network line is operating normally; if the preliminary comparison results indicate an abnormality, the system will enter the secondary verification analysis stage. At this stage, the system will conduct a more in-depth analysis to determine the exact type and nature of the fault; based on the results of the secondary verification analysis, the system will determine the specific type of fault and provide accurate information for subsequent fault handling and maintenance.

[0111] In a specific embodiment, Figure 8As shown, the obtained characteristic magnetic field is compared and analyzed with the data in the characteristic magnetic field library. The first comparison confirms whether there is a fault or whether it is running normally; the second verification analysis confirms the type of fault. In the first comparison stage, the operating status of the equipment is preliminarily judged by comparing the obtained characteristic magnetic field data with the standard data in the characteristic magnetic field library. This step is mainly to confirm whether there is an abnormality in the equipment, that is, to determine whether the equipment is in normal operation.

[0112] Through detailed comparison and analysis with the fault model library, the system of this embodiment can more accurately identify faults and abnormal conditions of distribution network lines; through real-time monitoring and rapid comparison, the system can promptly detect problems at the early stage of fault occurrence, providing opportunities for early intervention and repair; through the dual verification mechanism of preliminary comparison and secondary verification analysis, the system can reduce false alarms and missed alarms and improve the reliability of fault detection; accurate fault type identification helps to formulate more effective maintenance and repair strategies and reduce unnecessary maintenance work and costs; by timely detecting and handling faults, the system helps to improve the overall safety and stability of the power grid and reduce the risk of large-scale service interruptions; the comparison and analysis process supports the automation and intelligence of power grid management and provides technical support for the development of smart grids.

[0113] Further, in some embodiments, step S4 of "performing fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result" may specifically include:

[0114] S41. If the comparison result is detected to be that there is no fault, then the fault diagnosis result is determined to be that there is no fault in the distribution network line;

[0115] S42. If the comparison result is detected to be a fault, the real-time magnetic field characteristic data is fault classified by the fault classification model in the fault model library to obtain a fault classification result, and the fault diagnosis result is determined based on the fault classification result.

[0116] Specifically, for step S4, first, the comparison results of the fault model library and the real-time magnetic field characteristic data are analyzed in detail to determine whether there is a deviation or abnormality. If the comparison result shows that the magnetic field characteristic data is consistent with the normal operating standard in the fault model library, or the deviation is within an acceptable range, the diagnosis result is that there is no fault in the distribution network line at present. If the comparison result exceeds the threshold of normal operation, it indicates that there is an abnormality, and the system will enter the fault classification stage. Using the fault classification model in the fault model library, the abnormal real-time magnetic field characteristic data is further analyzed to identify the specific fault type. Based on the fault classification results, the system will determine the severity, location and possible causes of the fault, and generate a detailed fault diagnosis report. If the diagnostic result shows that a fault exists, the system will generate early warning information to provide a basis for taking preventive or repair measures.

[0117] In a specific embodiment, Figure 8 As shown, if the first comparison result indicates an abnormality, it is necessary to enter the secondary verification analysis stage. At this time, the fault model in the fault model library is used to further analyze and compare the magnetic field data in detail to determine the exact type of fault, whether it is a distribution network line fault or a distribution network line facility fault, so as to output the diagnosis result and subsequently trigger the early warning mechanism. This step is to diagnose the nature and cause of the fault more accurately and provide a basis for subsequent maintenance and repair.

[0118] Through precise comparison analysis and fault classification, the system of this embodiment can accurately identify the fault type and status of the distribution network line; the rapid output of fault diagnosis results enables the power grid operator to respond quickly and take timely measures to prevent the fault from expanding; through early diagnosis and early warning, the power outage time and economic losses caused by the fault can be reduced; accurate fault diagnosis results provide data support for power grid operators, helping them to formulate more reasonable maintenance and repair plans; reduce false alarms and missed alarms, improve the reliability of the fault detection system, and enhance the overall stability of the power grid; the fault diagnosis results can be used to analyze the cause of the fault and promote the continuous improvement of power grid facilities and detection technologies.

[0119] Further, in some embodiments, step S5 "determining the early warning control strategy corresponding to the distribution network line based on the fault diagnosis result" may specifically include:

[0120] S51. Pre-set fault thresholds corresponding to various types of faults based on the power distribution network requirements;

[0121] S52. Obtaining fault classification information and its corresponding fault characteristic parameters in the fault diagnosis result;

[0122] S53. If the fault characteristic parameter detected is greater than the fault threshold corresponding to the fault category, a warning message is issued;

[0123] S54. Based on the warning information, determine the warning control strategy corresponding to the distribution network line.

[0124] Specifically, for step S5, based on the demand of the distribution network and historical fault data, fault thresholds are pre-set for different types of faults, and these thresholds are used to evaluate the severity of the faults; fault classification information and its corresponding fault characteristic parameters are obtained from the fault diagnosis results, and these parameters indicate the nature and severity of the fault; the fault characteristic parameters are compared with the preset fault thresholds, and if the parameters exceed the thresholds, the system automatically triggers the early warning mechanism; the system generates early warning information, clearly indicating the fault type, location, possible impact and recommended emergency measures; based on the early warning information and fault diagnosis results, appropriate early warning control strategies are determined, and these strategies may include adjusting the grid operation parameters, automatically isolating the fault area, or notifying maintenance personnel; the determined early warning control strategy is implemented, and the implementation effect of the strategy is monitored to ensure that the fault is effectively managed and controlled; feedback information on fault handling and strategy implementation is collected, and the early warning control strategy is evaluated and optimized to improve the efficiency and effectiveness of future fault response.

[0125] In a specific embodiment, Fig. 9 As shown, a fault threshold is set for each type of fault in the feature model library. After the comparison is completed, the type of fault is diagnosed, and then the fault threshold of this type is compared to trigger the early warning mechanism and issue an early warning control strategy, such as notifying the operation and maintenance personnel to perform on-site repairs and restore power.

[0126] Through the automated fault assessment and early warning triggering mechanism, the system of this embodiment can respond to fault conditions quickly and reduce processing delays; timely early warning and control strategy implementation helps to limit the impact of the fault, prevent the fault from spreading, and ensure stable operation of the power grid; accurate early warning information and control strategies help power grid operators to allocate maintenance and emergency resources more effectively; through automatic isolation of fault areas and other safety measures, the system improves the overall security of the power grid; provides data-based decision support for power grid operators, enhances the scientificity and rationality of fault handling; by reducing the impact of faults on user power supply, improves user satisfaction with power grid services; feedback and optimization mechanisms help to continuously improve early warning control strategies and improve system performance and reliability; automated and intelligent early warning control strategies support the development of smart grid technology and provide a technical basis for achieving higher levels of power grid automation management.

[0127] In summary, the distribution network line fault detection method provided in this embodiment can effectively determine the distribution network line fault problem and fault type by using the model library comparison diagnosis method and secondary verification analysis. Efficient data processing and analysis can be achieved. An early warning mechanism is set in the method. A fault threshold is set for each type of fault in the feature model library. After the comparison is completed, the fault is diagnosed, the early warning mechanism is triggered, and the early warning control strategy is issued, and finally the detection, early warning and strategy issuance of the distribution network line facility defects of the distribution network operation line magnetic field variable characteristics are realized. It can be seen that this embodiment can significantly improve the operating efficiency and safety of the distribution network, and can identify facility defects early and prevent faults by analyzing the magnetic field distribution generated by the line, which helps to reduce unplanned power outage time, extend equipment life, reduce maintenance costs, and optimize maintenance plans. At the same time, the detection method provides data support for operation and maintenance decisions, improves power supply reliability, promotes the development of smart grids, and ensures the environmental friendliness and customer satisfaction of the power system.

[0128] In order to better implement the distribution network line fault detection method of the embodiment of the present application, the embodiment of the present application also provides a distribution network line fault detection system. The meanings of the terms are the same as those in the above distribution network line fault detection method, and the specific implementation details can refer to the description in the method embodiment.

[0129] See also Fig.10 , Fig.10 A schematic diagram of the structure of a distribution network line fault detection system provided in an embodiment of the present application, wherein the distribution network line fault detection system may specifically include a construction module 201, an acquisition module 202, a comparison module 203 and a diagnosis module 204, which may be specifically as follows:

[0130] Construction module 201, used to construct a fault model library for distribution network lines;

[0131] An acquisition module 202 is used to acquire real-time magnetic field variable data of the distribution network line and perform data preprocessing;

[0132] Comparison module 203, used to perform comparison analysis based on real-time magnetic field variable data after data preprocessing through a fault model library to obtain a comparison result;

[0133] The diagnosis module 204 is used to perform fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result.

[0134] Furthermore, if Fig.11 As shown, in some embodiments, the distribution network line fault detection system may further include:

[0135] The early warning module 205 is used to determine the early warning control strategy corresponding to the distribution network line based on the fault diagnosis result;

[0136] The processing module 206 is used to perform fault processing on the distribution network line based on the early warning control strategy.

[0137] The distribution network line fault detection system provided in this embodiment builds a fault model library for the distribution network line through the construction module 201; obtains the real-time magnetic field variable data of the distribution network line and performs data preprocessing through the acquisition module 202; uses the fault model library to perform comparison analysis based on the real-time magnetic field variable data after data preprocessing through the comparison module 203 to obtain the comparison result; and performs fault diagnosis on the distribution network line based on the comparison result through the diagnosis module 204 to obtain the fault diagnosis result. A distribution network line fault detection system provided in this embodiment can quickly identify potential defects and faults by real-time detection of magnetic field changes of the distribution network line, improve the timeliness and accuracy of fault detection, and effectively determine the fault problem and fault type of the distribution network line by using the fault model library comparison diagnosis method and secondary verification analysis, so as to improve the efficiency and intelligence of fault detection. It can be seen that this embodiment can significantly improve the operating efficiency and safety of the distribution network, reduce unplanned power outage time, extend equipment life, reduce maintenance costs, optimize maintenance plans, improve power supply reliability, and promote the development of smart grids.

[0138] In addition, the present application also provides an electronic device, such as Fig.12 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application. Specifically, the electronic device may include a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, an input unit 304 and other components. Those skilled in the art can understand that Fig.12 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0139] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0140] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and distribution network line fault detection methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0141] The electronic device also includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0142] The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0143] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0144] Construct a fault model library for distribution network lines; obtain real-time magnetic field variable data of distribution network lines and perform data preprocessing; perform comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain comparison results; perform fault diagnosis on the distribution network lines based on the comparison results to obtain fault diagnosis results.

[0145] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0146] The embodiments of the present application can quickly identify potential defects and faults by real-time detection of magnetic field changes in distribution network lines, improve the timeliness and accuracy of fault detection, and use fault model library comparison diagnosis methods and secondary verification analysis to effectively determine distribution network line fault problems and fault types, thereby improving the efficiency and intelligence of fault detection.

[0147] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0148] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any distribution network line fault detection method provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0149] Construct a fault model library for distribution network lines; obtain real-time magnetic field variable data of distribution network lines and perform data preprocessing; perform comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain comparison results; perform fault diagnosis on the distribution network lines based on the comparison results to obtain fault diagnosis results.

[0150] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0151] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. Since the instructions stored in the storage medium can execute the steps in any of the distribution network line fault detection methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the distribution network line fault detection methods provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0152] The above is a detailed introduction to a distribution network line fault detection method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A distribution network line fault detection method, It is characterized in that The steps include: Build a fault model library for distribution network lines; Acquiring real-time magnetic field variable data of the distribution network line and performing data preprocessing; Comparison analysis is performed based on the real-time magnetic field variable data after data preprocessing by the fault model library to obtain a comparison result; Based on the comparison result, fault diagnosis is performed on the distribution network line to obtain a fault diagnosis result.

2. The distribution network line fault detection method according to claim 1, It is characterized in that The method further comprises: Based on the fault diagnosis result, determining the early warning control strategy corresponding to the distribution network line; Fault processing is performed on the distribution network line based on the early warning control strategy.

3. The distribution network line fault detection method according to claim 1, It is characterized in that The construction of the fault type library for the distribution network line includes: Acquire magnetic field data of the distribution network line, wherein the magnetic field data includes simulated magnetic field data and historical magnetic field data; Extracting fault characteristic parameters corresponding to the magnetic field data, wherein the fault characteristic parameters include magnetic field intensity, magnetic field distribution and magnetic field frequency; Analyzing and filtering the fault characteristic parameters to obtain a fault characteristic quantity; Constructing an initial fault type library corresponding to the distribution network line based on the fault characteristic quantity; Model training is performed on the fault classification model in the initial fault type library based on the magnetic field data to obtain a trained fault type library.

4. The distribution network line fault detection method according to claim 1, It is characterized in that The obtaining of real-time magnetic field variable data of the distribution network line and performing data preprocessing includes: Acquiring real-time magnetic field variable data of the distribution network line through a non-contact electromagnetic induction module; After data preprocessing is performed on the real-time magnetic field variable data, magnetic field characteristic data is obtained; the data preprocessing includes data cleaning, feature extraction and data standardization.

5. The distribution network line fault detection method according to claim 1, It is characterized in that The comparison and analysis is performed based on the real-time magnetic field variable data after data preprocessing by the fault model library to obtain a comparison result, including: Acquire real-time magnetic field characteristic data corresponding to the real-time magnetic field variable data after the data preprocessing; A comparison result is obtained based on a comparison analysis between the real-time magnetic field characteristic data and the magnetic field characteristic standard data in the fault model library.

6. The distribution network line fault detection method according to claim 5, It is characterized in that The performing fault diagnosis on the distribution network line based on the comparison result to obtain the fault diagnosis result includes: If it is detected that the comparison result is that there is no fault, determining the fault diagnosis result is that there is no fault in the distribution network line at present; If it is detected that the comparison result is a fault, the real-time magnetic field characteristic data is fault classified by the fault classification model in the fault model library to obtain a fault classification result, and a fault diagnosis result is determined based on the fault classification result.

7. The distribution network line fault detection method according to claim 2, It is characterized in that The determining, based on the fault diagnosis result, a warning control strategy corresponding to the distribution network line comprises: Pre-set fault thresholds corresponding to various types of faults based on distribution network requirements; Obtaining fault classification information and corresponding fault characteristic parameters in the fault diagnosis result; If it is detected that the fault characteristic parameter is greater than the fault threshold corresponding to the fault category, a warning message is issued; Based on the warning information, a warning control strategy corresponding to the distribution network line is determined.

8. A distribution network line fault detection system, It is characterized in that include: A construction module is used to construct a fault model library for distribution network lines; An acquisition module, used to acquire real-time magnetic field variable data of the distribution network line and perform data preprocessing; A comparison module, used for performing comparison analysis based on the real-time magnetic field variable data after data preprocessing through the fault model library to obtain a comparison result; The diagnosis module is used to perform fault diagnosis on the distribution network line based on the comparison result to obtain a fault diagnosis result.

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

10. A storage medium, It is characterized in that A computer program is stored which can be loaded by a processor and executes the distribution network line fault detection method according to any one of claims 1 to 7.

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