Power distribution network fault detection method and system based on Internet of Things

Abstract

The invention discloses a power distribution network fault detection method and system based on the Internet of Things, and relates to the technical field of power distribution network fault detection methods. And the fault area of the power distribution network is determined according to the abnormal current parameter combination, the abnormal current parameters and the working types of the working areas, so that the accuracy of the fault area of the power distribution network is improved. Determining a plurality of fault events of the fault area according to the function of the fault power distribution network component of the fault area and the current parameters of the fault area at different time; the maintenance sequence table of the fault area is determined according to the plurality of fault events of the fault area and the current working mode of the power distribution network, and the autonomous maintenance of the fault area is triggered according to each maintenance event in the maintenance sequence table, the corresponding maintenance time and the fault maintenance process of the fault area, so that the autonomous maintenance effect of the fault area is ensured. And synchronous maintenance of the fault area when the power distribution network is in the working state is realized.

Description

Technical Field

[0001] This invention relates to the technical field of distribution network fault detection methods, and in particular to a distribution network fault detection method and system based on the Internet of Things. Background Technology

[0002] Distribution networks are gradually being applied to people's lives and serve as one of the main power supply facilities. When in operation, distribution networks guide the transmission of current and transmit it to the corresponding target power supply point. When in operation, distribution networks have multiple current parameters. In the current technology, the corresponding abnormal current parameters are determined by screening multiple current parameters, and the fault events of the distribution network are determined based on each abnormal current parameter. However, it is impossible to accurately detect the fault area of ​​the distribution network, and thus it is impossible to guarantee the autonomous maintenance effect of the fault area. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for detecting power distribution network faults based on the Internet of Things.

[0004] This invention provides an Internet of Things (IoT)-based method for detecting faults in a distribution network, comprising: acquiring a distribution map of the distribution network using IoT; determining multiple working areas based on the distribution map and the current operating mode of the distribution network; determining corresponding current parameter combinations based on current detection in each working area; determining corresponding abnormal current parameters based on current detection in each current parameter combination; determining abnormal current parameter combinations by comparing the current parameter combinations; determining faulty areas of the distribution network based on the abnormal current parameter combinations, abnormal current parameters, and the operating type of each working area; determining corresponding faulty distribution network components based on fault detection in the faulty areas; determining multiple fault events in the faulty areas based on the functions of the faulty distribution network components and the current parameters of the faulty areas at different times; determining a maintenance sequence list for the faulty areas based on the multiple fault events in the faulty areas and the current operating mode of the distribution network; and triggering autonomous maintenance of the faulty areas based on each maintenance event in the maintenance sequence list, the corresponding maintenance time, and the fault maintenance progress of the faulty areas.

[0005] This invention provides an Internet of Things (IoT)-based distribution network fault detection system, which is applied to the aforementioned IoT-based distribution network fault detection method. The IoT-based distribution network fault detection system includes:

[0006] The working area module is used to collect distribution maps of the power distribution network based on the Internet of Things, and to determine multiple working areas based on the distribution map and the current working mode of the power distribution network.

[0007] The abnormal current parameter module is used to determine the corresponding combination of current parameters based on the current detection of each working area, and to determine the corresponding abnormal current parameter based on the current detection of each combination of current parameters.

[0008] The fault area module is used to determine the abnormal current parameter combination based on the comparison of various current parameter combinations, and to determine the fault area of ​​the distribution network based on the abnormal current parameter combination, abnormal current parameters and the working type of each working area.

[0009] The fault event module is used to determine the corresponding faulty distribution network component based on the fault detection of the faulty area, and to determine multiple fault events of the faulty distribution network component according to the function of the faulty distribution network component and the current parameters of the faulty area at different times.

[0010] The maintenance module is used to determine the maintenance sequence list of the fault area based on multiple fault events in the fault area and the current working mode of the distribution network, and to trigger autonomous maintenance of the fault area based on each maintenance event, the corresponding maintenance time, and the fault maintenance progress of the fault area in the maintenance sequence list.

[0011] Compared with the prior art, the beneficial effects of the present invention are:

[0012] In this embodiment of the invention, the method of this embodiment determines the corresponding abnormal current parameter based on the current detection of various current parameter combinations; determines the abnormal current parameter combination based on the comparison of various current parameter combinations; and determines the fault area of ​​the distribution network based on the abnormal current parameter combination, abnormal current parameter, and the working type of each working area. This method takes into account the overall consideration of abnormal current parameter combinations, abnormal current parameters, and the working type of each working area, thereby improving the accuracy of the fault area of ​​the distribution network.

[0013] Therefore, based on fault detection in the fault area, the corresponding faulty distribution network components are identified. Multiple fault events in the fault area are determined according to the functions of the faulty distribution network components and the current parameters of the fault area at different times. Based on the multiple fault events in the fault area and the current working mode of the distribution network, a maintenance sequence table for the fault area is determined. Autonomous maintenance of the fault area is triggered according to each maintenance event, corresponding maintenance time, and fault maintenance process in the maintenance sequence table. The maintenance sequence table for the fault area is introduced, and each maintenance event, corresponding maintenance time, and fault maintenance process in the maintenance sequence table are considered as a whole to ensure the effectiveness of autonomous maintenance of the fault area and realize synchronous maintenance of the fault area when the distribution network is in working condition. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the Internet of Things-based power distribution network fault detection method in an embodiment of the present invention.

[0015] Figure 2 This is a flowchart illustrating step S11 of the Internet of Things-based power distribution network fault detection method in this embodiment of the invention.

[0016] Figure 3 This is a flowchart illustrating step S12 of the Internet of Things-based power distribution network fault detection method in this embodiment of the invention.

[0017] Figure 4 This is a flowchart illustrating step S13 of the Internet of Things-based power distribution network fault detection method in this embodiment of the invention.

[0018] Figure 5 This is a flowchart illustrating S14 of the Internet of Things-based power distribution network fault detection method in this embodiment of the invention.

[0019] Figure 6 This is a flowchart illustrating S15 of the Internet of Things-based power distribution network fault detection method in this embodiment of the invention.

[0020] Figure 7 This is a schematic diagram of the structural composition of an Internet of Things-based power distribution network fault detection system in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0022] Please see Figures 1 to 7 An IoT-based distribution network fault detection method is proposed and applied to IoT-based distribution network fault detection scenarios. The IoT-based distribution network fault detection method includes:

[0023] Step S11: Collect the distribution map of the power distribution network based on the Internet of Things, and determine multiple working areas according to the distribution map and the current working mode of the power distribution network;

[0024] Step S12: Determine the corresponding current parameter combination based on the current detection of each working area, and determine the corresponding abnormal current parameter based on the current detection of each current parameter combination.

[0025] Step S13: Determine the abnormal current parameter combination based on the comparison of various current parameter combinations, and determine the fault area of ​​the distribution network based on the abnormal current parameter combination, abnormal current parameters and the working type of each working area.

[0026] Step S14: Based on the fault detection of the fault area, determine the corresponding faulty distribution network component, and determine multiple fault events of the fault area according to the function of the faulty distribution network component and the current parameters of the fault area at different times.

[0027] Step S15: Determine the maintenance sequence table for the fault area based on multiple fault events in the fault area and the current operating mode of the distribution network. Trigger autonomous maintenance of the fault area based on each maintenance event in the maintenance sequence table, the corresponding maintenance time, and the fault maintenance process of the fault area.

[0028] Please see Figure 2 In step S11, the specific steps are as follows:

[0029] S111: Collect the location of the distribution network, determine the corresponding communication network space based on the location of the distribution network and the corresponding Internet of Things, and determine the distribution map of the distribution network based on the traversal of the communication network space.

[0030] S112: Multiple sub-regions are determined based on the distribution map of the power distribution network, and the corresponding operating status is determined based on the detection of multiple sub-regions; the operating status includes normal, overload, or maintenance.

[0031] S113: Collect the current working mode of the distribution network, determine multiple working areas based on the current working mode of the distribution network, the working status of multiple sub-regions, and the location of multiple sub-regions, and mark the corresponding working function for each working area; the current working mode includes peak hours, maintenance period, or routine maintenance.

[0032] In the embodiments of this application, the location of the power distribution network is collected, and GPS devices are used to locate key equipment in the power distribution network and record latitude and longitude information; or, if GIS data is available, the location information of key equipment is extracted from it. The communication paths and ranges between devices are determined based on the device location information and IoT technology; IoT devices (such as sensors, smart meters, etc.) typically have communication modules to exchange data with other devices; therefore, it is necessary to determine which devices communicate with each other, and the communication distance and path between them; at this time, the communication type and range (such as Wi-Fi, LoRa, NB-IoT, etc.) of each IoT device in the power distribution network are determined; based on the device location information and communication range, a communication path and range map is drawn in the GIS system; ensuring that each device can communicate with its adjacent devices or the central control system.

[0033] Traversing the communication network space involves connecting all devices in the distribution network according to their communication paths and ranges to form a complete distribution map. This map should clearly show the location, connection relationships, and communication paths of the devices. Simultaneously, within the GIS system, all devices in the distribution network are connected based on the communication path and range map. This ensures that the distribution map accurately reflects the physical connections and communication relationships between devices. The distribution map is then verified and adjusted to ensure its accuracy and readability.

[0034] Furthermore, based on the distribution network map and considering factors such as its geographical features, equipment density, and operation and maintenance requirements, the distribution network is divided into multiple sub-regions. The division of sub-regions should be reasonable and easy to manage to facilitate subsequent monitoring and maintenance work. When dividing, consider grouping equipment with similar functions or adjacent geographical locations into one sub-region. At this point, on the distribution network map, several potential sub-regions are initially divided based on geographical features (such as rivers, roads, etc.) and equipment density. The initially divided sub-regions are then adjusted and optimized to ensure that each sub-region contains the necessary equipment and that the connection relationships between the equipment are clear and unambiguous.

[0035] Each sub-region is monitored to determine its operating status. Monitoring includes parameters such as current, voltage, power factor, and temperature. By monitoring these parameters in real time, the operating status of the sub-region is understood, anomalies are detected promptly, and corresponding measures are taken. Sensors and monitoring equipment are deployed in each sub-region to collect data on key parameters in real time. A data analysis system is established to process and analyze the collected data to determine the operating status of the sub-region. Based on the analysis results, a status assessment is performed on the sub-region, such as normal operation, overload, undervoltage, or overheating. The status assessment results are promptly fed back to the operations and maintenance team so that they can take appropriate measures.

[0036] Therefore, the current operating mode of the distribution network is collected, and multiple working areas are determined based on the current operating mode of the distribution network, the working status of multiple sub-regions, and the regional location of multiple sub-regions. Each working area is marked with its corresponding working function, which takes into account the overall consideration of the current operating mode of the distribution network, the working status of multiple sub-regions, and the regional location of multiple sub-regions, thus ensuring the accuracy of multiple working areas.

[0037] At this point, it is crucial to obtain the current operating mode or status of the distribution network. This typically includes understanding whether the distribution network is operating normally, undergoing maintenance or repair, or facing special load demands (such as peak hours or emergencies). This information usually comes from the distribution network's dispatch center or control center, which determines the distribution network's operating mode based on real-time load data, equipment status, and operation and maintenance plans. Communication with the distribution network's dispatch center or control center is then established to obtain real-time information on the distribution network's current operating mode. It is essential to ensure that the obtained information is accurate, timely, and consistent with the actual state of the distribution network.

[0038] The work areas are determined by comprehensively considering the current operating mode of the distribution network, the working status of each sub-region, and their geographical locations. The division of work areas should be based on actual maintenance needs to ensure that the maintenance team can execute tasks efficiently. For example, during peak hours, the focus should be on sub-regions with heavier loads; during equipment maintenance, relevant sub-regions should be grouped into a single work area for centralized processing. At this point, the impact of the current operating mode of the distribution network on maintenance work, such as load demand and equipment status, should be analyzed. Based on the working status and geographical location of each sub-region, several potential work areas are initially identified. The initial work areas are adjusted and optimized, taking into account the actual capabilities and needs of the maintenance team (such as staffing, vehicle scheduling, and time windows). Each work area should contain the necessary equipment, and the connections between the equipment should be clear and unambiguous to facilitate efficient task execution by the maintenance team.

[0039] Labeling each work area with its corresponding job function helps the operations and maintenance team clarify their respective tasks and responsibilities, improving work efficiency. The labeling of job functions should be based on the actual needs and operations and maintenance plans of the distribution network, such as troubleshooting, equipment maintenance, and load management. Simultaneously, based on the distribution network's operations and maintenance plan and actual needs, specific job functions should be determined for each work area. These job functions should be marked on the work area distribution map or in the relevant operations and maintenance management system for easy viewing and execution by the operations and maintenance team. Ensuring that the operations and maintenance team understands and is familiar with the job functions of their respective work areas is crucial for efficient task execution.

[0040] Please see Figure 3 In step S12, the specific steps are as follows:

[0041] S121: In each working area, multiple current detection nodes are determined based on the markings of the working area, and the corresponding current parameters are determined based on the current detection of the multiple current detection nodes. The position of the current detection node is used as the corresponding detection position of the current parameter.

[0042] S122: Real-time monitoring of multiple current parameters in the same working area, and determination of the corresponding combination of current parameters based on the magnitude and location of the multiple current parameters, which marks the corresponding working area.

[0043] S123: In each combination of current parameters, the corresponding abnormal current parameters are determined based on the matching of the current parameter combination, the previous current parameters of the working area, and the working function corresponding to the working area.

[0044] In the embodiments of this application, the location of the current sensing node is determined based on specific markers or attributes of the work area. These markers include factors such as the work function, equipment type, load level, and fault history of the work area. The selection of the current sensing node should comprehensively reflect the current status within the work area, while also considering cost-effectiveness and operational feasibility. Specifically, the current sensing node is determined based on the following factors:

[0045] Job functions: For example, load management areas need to set up detection nodes at transformer inlets and major load connections; Equipment type: For example, areas containing a large number of motors need to set up detection nodes near motor starters; Load level: High-load areas require more dense current detection points to monitor load changes; Fault history: Areas with past current faults need to increase detection nodes for preventative monitoring; At the same time, analyze the markings and attributes of the work area to determine key locations and equipment; Based on the analysis results, select representative current detection nodes within the work area; Record the location information of each detection node for subsequent data collection and analysis.

[0046] Current sensors are installed at selected current sensing nodes, and current data is collected in real time or periodically. This data is then processed into current parameters, such as instantaneous current, average current, and peak current, which can reflect the current status in the working area. At this point, current sensors are installed at each selected current sensing node; a data acquisition system is configured to ensure that current data can be obtained from the sensors in real time or periodically; the collected current data is processed and analyzed to extract useful current parameters.

[0047] Associating each current parameter with its corresponding detection location helps to accurately identify the location and cause of current anomalies in subsequent analysis. At the same time, the location information of each current parameter and its corresponding detection node is recorded in the data acquisition or processing system to ensure that the correspondence between the location information and the current parameter is accurate.

[0048] Furthermore, a real-time monitoring system is established, capable of collecting and processing current data from multiple current detection nodes within the same working area. This current data is then converted into current parameters, such as instantaneous current value, average current value, and current fluctuation range, which comprehensively reflect the current status within the working area. At this point, current sensors are deployed at each current detection node to ensure that the sensors can accurately and in real-time measure the current. A data acquisition system is configured to receive current data from each sensor in real time and perform preliminary processing. A data monitoring interface is set up so that maintenance personnel can view the changes in each current parameter in real time.

[0049] Multiple real-time acquired current parameters are analyzed, and a combination of current parameters is determined based on their magnitude and location information. This combination should comprehensively and accurately reflect the current distribution and changes within the working area. Specifically, the combination of current parameters is determined based on the following factors: the magnitude of the current parameters, such as instantaneous current value and average current value, which reflect the current intensity and load level; the location of the current parameters, i.e., the location information of the current detection nodes, which helps to accurately identify the location of current anomalies in subsequent analysis; and the changing trend of the current parameters, such as the current fluctuation range and growth rate, which reflect the stability and changing trend of the current.

[0050] Meanwhile, the real-time collected current data is processed and analyzed to extract useful current parameters; based on the magnitude, location, and trend of the current parameters, a combination of current parameters that can comprehensively reflect the current status within the working area is determined.

[0051] Associating the identified current parameter combinations with their corresponding working areas helps to quickly identify the current status of different working areas in subsequent analysis and management, thereby enabling the implementation of appropriate maintenance measures. At this point, in the data management system or monitoring interface, each current parameter combination is labeled with its corresponding working area. It is essential to ensure that the labeling information is accurate so that maintenance personnel can quickly find and analyze the current data of different working areas.

[0052] Therefore, in each combination of current parameters, the corresponding abnormal current parameters are determined based on the matching of the current parameter combination, the previous current parameters of the working area, and the work functions corresponding to the working area. This takes into account the overall consideration of the matching of the current parameter combination, the previous current parameters of the working area, and the work functions corresponding to the working area, and ensures the accuracy of the corresponding abnormal current parameters.

[0053] At this point, real-time current parameter combinations for each working area are collected and analyzed. These parameter combinations contain current data from multiple current detection nodes, which can comprehensively reflect the current status within the working area. The purpose of analyzing these parameter combinations is to identify any current parameters that do not conform to the normal operating conditions, i.e., abnormal current parameters. At this point, real-time current parameter combinations for each working area are extracted from the monitoring system. Each current parameter combination is analyzed in detail, including the magnitude of the current value, the trend of change, and the comparison with preset thresholds.

[0054] Historical current data for the work area, i.e., past current parameters, are acquired and organized. This data is used to compare with real-time current parameters to identify abnormal changes. Past current parameters include statistical data such as average current value, maximum current value, and current fluctuation range over a period of time. At the same time, past current parameters for the work area are extracted from the data management system or historical database. The past current parameters are organized and analyzed to understand the normal current range and trend of the work area.

[0055] The system performs a matching analysis of real-time current parameter combinations, historical current parameters, and the work functions of the work area. These functions include load management, fault warning, and equipment protection, which affect the normal range and abnormal judgment criteria of current parameters. Through matching analysis, abnormal current parameters that do not conform to the work functions can be identified more accurately. At this point, the normal range and abnormal judgment criteria of current parameters are determined based on the work functions of the work area. By comparing real-time current parameter combinations with historical current parameters and considering the influence of work functions, abnormal current parameters are identified.

[0056] Based on the preceding analysis, abnormal current parameters are identified and marked. These parameters exceed the preset threshold range or show a significant trend of change compared to previous current parameters. Marking abnormal current parameters helps maintenance personnel quickly locate problems and take corresponding measures. At the same time, abnormal current parameters are marked in the monitoring system, including their location, magnitude, and trend of change. An alarm or notification mechanism is triggered to promptly convey the information of abnormal current parameters to maintenance personnel.

[0057] Please see Figure 4 In step S13, the specific steps are as follows:

[0058] S131: Collect various current parameter combinations, determine the corresponding matching coefficient based on the comparison of various current parameter combinations, determine the preset matching coefficient threshold based on various current parameter combinations and the current working mode of the distribution network, and determine the abnormal current parameter combination based on the comparison of various matching coefficients with the preset matching coefficient threshold.

[0059] S132: Determine the first fault range based on the combination of abnormal current parameters and the abnormal current parameters, and determine the second fault range based on the combination of abnormal current parameters and the working type of each working area; the first fault range includes the fault point itself and adjacent equipment and lines affected by the fault; the second fault range includes equipment and lines indirectly affected, as well as areas where preventive measures need to be taken.

[0060] S133: In the distribution network, the fault area of ​​the distribution network is determined according to the mapping relationship between the first fault range, the second fault range and the fault area.

[0061] In the embodiments of this application, various current parameter combinations are collected, and corresponding matching coefficients are determined based on the comparison of various current parameter combinations. A preset matching coefficient threshold is determined based on various current parameter combinations and the current working mode of the distribution network. Abnormal current parameter combinations are determined based on the comparison of various matching coefficients with the preset matching coefficient threshold. This approach takes into account the overall consideration of comparing various matching coefficients with the preset matching coefficient threshold, ensuring the accuracy of abnormal current parameter combinations.

[0062] At this point, various current parameter combinations are collected. Simultaneously, a data acquisition system is configured to ensure that the current parameters of each node can be collected in real time and accurately. The collected data is preprocessed, such as by filtering and noise reduction, to improve data quality. The processed data is then grouped according to the working area to form various current parameter combinations.

[0063] By comparing various combinations of current parameters, the similarity between them is calculated to obtain the matching coefficient. The matching coefficient is a value between 0 and 1, used to quantify the degree of similarity between combinations of current parameters. The closer the value is to 1, the higher the degree of matching; the closer the value is to 0, the greater the difference. Optionally, the matching coefficient can be calculated based on various algorithms, such as Euclidean distance, cosine similarity, Pearson correlation coefficient, etc. The specific algorithm chosen depends on the characteristics of the current parameter combination and the actual situation of the distribution network.

[0064] A preset matching coefficient threshold is determined based on various current parameter combinations and the current operating mode of the distribution network. This threshold is used to determine whether the current parameter combinations are normal. If the matching coefficient of a certain current parameter combination with other combinations is lower than this threshold, the combination is considered abnormal. The method for determining the matching coefficient threshold is based on historical data. Specifically, the current parameter combination data of the distribution network under normal operating mode is analyzed, the matching coefficients between them are calculated, and a reasonable threshold is determined based on the distribution of these coefficients. At this time, the historical data of the distribution network under normal operating mode is analyzed; the matching coefficients between various current parameter combinations in the historical data are calculated; a reasonable threshold is determined based on the distribution of the matching coefficients; this threshold is stored for subsequent analysis.

[0065] Abnormal current parameter combinations are determined by comparing each matching coefficient with a preset matching coefficient threshold. Specifically, for each current parameter combination, its matching coefficient is compared with a preset threshold. If the matching coefficient is lower than the threshold, the combination is marked as an abnormal current parameter combination. At the same time, for each current parameter combination, its matching coefficient is compared with a preset threshold. If the matching coefficient is lower than the threshold, the combination is marked as an abnormal current parameter combination. The current parameter combinations marked as abnormal are stored or output for subsequent analysis and processing.

[0066] Specifically, suppose there is a distribution network working area with three nodes, and the following two combinations of current parameters are collected in real time: Combination A: Node 1 current 10A, Node 2 current 15A, Node 3 current 5A; Combination B: Node 1 current 12A, Node 2 current 14A, Node 3 current 4A.

[0067] Euclidean distance was chosen as the algorithm for calculating the matching coefficient, and a preset matching coefficient threshold of 0.8 was determined (this threshold was determined based on historical data and expert experience). First, the matching coefficient between combination A and combination B was calculated. Since Euclidean distance measures the straight-line distance between two points, the current parameters were treated as points in a multi-dimensional space, and the distances between them were calculated. To simplify the calculation, the current values ​​of each node were normalized to ensure they were of the same order of magnitude. Assuming the normalized current values ​​are: Combination A': Node 1 current 0.33, Node 2 current 0.50, Node 3 current 0.17; Combination B': Node 1 current 0.40, Node 2 current 0.47, Node 3 current 0.13; then the Euclidean distance between combination A' and combination B' is:

[0068]

[0069] Since a smaller Euclidean distance indicates greater similarity, it is converted into a matching coefficient (here, we simply use 1 minus the normalized Euclidean distance as the matching coefficient):

[0070]

[0071] Where, d max It is the maximum Euclidean distance (after normalization, the range of each current value is 0 to 1, so the maximum Euclidean distance is...). However, we don't need to calculate it precisely here, as we only care about the relative magnitude of the matching coefficients; for simplicity, we directly use a constant to approximate the upper limit of the matching coefficients (e.g., 1), and calculate:

[0072] Matching coefficient ≈ 1 - 0.10 = 0.90

[0073] Since the calculated matching coefficient of 0.90 is higher than the preset matching coefficient threshold of 0.8, combination A and combination B are considered to be similar and without abnormality. However, in practical applications, multiple combinations are usually compared and those combinations with matching coefficients lower than the threshold are marked as abnormal current parameter combinations.

[0074] Furthermore, the first fault range is determined based on the combination of abnormal current parameters and the abnormal current parameters themselves, and the second fault range is determined based on the combination of abnormal current parameters and the working type of each working area. This approach takes into account both the combination of abnormal current parameters and the working type of each working area, ensuring the accuracy of the second fault range.

[0075] At this point, the preliminary fault range, i.e., the first fault range, is determined based on the combination and specific abnormal current parameters. This usually involves in-depth analysis of the abnormal current parameters to identify which equipment or areas have experienced faults. Each parameter in the abnormal current parameter combination is carefully examined, including the magnitude, direction, and trend of the current. Changes in these parameters often directly reflect the location and nature of the fault. The abnormal current parameters are used to locate the fault point in conjunction with the distribution network topology and equipment parameters. For example, if the current at a node suddenly increases, the equipment or line directly connected to that node is likely the source of the fault. Based on locating the fault point, the extent of fault propagation needs to be further considered to determine the first fault range. This range typically includes the fault point itself and adjacent equipment and lines affected by the fault.

[0076] After determining the first fault scope, it is necessary to consider the potential impact of the fault on the entire distribution network to determine the second fault scope. This step typically involves an in-depth analysis of the types of work, load importance, and interdependencies of each work area. At this point, understanding the types of work, load types, and importance levels of each work area is crucial for assessing the impact of the fault on the entire distribution network. Combining the information from the first fault scope, it is necessary to assess the cascading effects and impacts of the fault on the entire distribution network. For example, if the fault occurs on critical equipment, it will lead to a power outage or load shift in the entire area. Based on the assessment of the fault's impact, it is necessary to determine the second fault scope, which is usually broader than the first fault scope, including indirectly affected equipment and lines, as well as areas where preventative measures are required.

[0077] Therefore, in the distribution network, the fault area is determined based on the mapping relationship between the first fault range, the second fault range, and the fault area. This takes into account the overall consideration of the mapping relationship between the first fault range, the second fault range, and the fault area, ensuring the accuracy of the fault area of ​​the distribution network. At the same time, it takes into account the overall consideration of abnormal current parameter combinations, abnormal current parameters, and the working types of each working area, thereby improving the accuracy of the fault area of ​​the distribution network.

[0078] At this point, it is crucial to clearly understand the meaning of the first fault range, the second fault range, and the fault area mapping relationship, as well as their roles in determining the fault area. The first fault range is the area where the fault occurred, initially determined based on combinations of abnormal current parameters and specific abnormal parameters; it is usually quite precise but has a small scope. The second fault range is a broader fault area determined after considering the potential impact of the fault on the entire distribution network, typically including directly and indirectly affected equipment and lines. The fault area mapping relationship is a pre-established database or model used to associate various devices, lines, and areas in the distribution network with potential fault types, locations, and ranges; it is usually constructed based on historical fault data, the distribution network topology, and equipment parameters.

[0079] At this point, ensure an accurate understanding of the first and second fault ranges and be able to clearly mark them on the power distribution network diagram; be familiar with the content and usage of the fault area mapping relationship to ensure that you can quickly find the corresponding potential fault area based on the fault range.

[0080] By utilizing the fault area mapping relationship and combining information from the first and second fault ranges, specific fault areas in the distribution network will be determined. Then, the information from the first and second fault ranges will be matched with the fault area mapping relationship to identify potential fault areas corresponding to these fault ranges. If multiple potential fault areas are matched, a comprehensive evaluation based on the actual situation of the distribution network, historical fault data, and expert experience is needed to determine the fault area. The determined fault area will be marked on the distribution network diagram for subsequent analysis and processing.

[0081] Although the fault area has been identified through the mapping relationship, in practice, this area still needs to be verified and adjusted to ensure its accuracy and reliability. At the same time, professional personnel should be dispatched to the site to conduct an investigation to verify whether the fault area matches the actual situation. If the on-site investigation results differ from the previously identified fault area, the fault area needs to be adjusted based on the investigation results. If there is a significant difference between the fault area and the information in the mapping relationship, it is also necessary to consider updating the mapping relationship to improve its accuracy in future fault location.

[0082] Specifically, suppose there is a power distribution network with multiple work areas, where work area A is mainly responsible for supplying power to residential areas, work area B supplies power to commercial areas, and work area C supplies power to industrial areas; at a certain point in time, abnormal current parameter combinations are collected, and the first fault range and the second fault range are determined; the first fault range is a certain feeder in work area A and its adjacent equipment; the second fault range is the entire residential area in work area A and a part of the adjacent commercial area (a part of work area B);

[0083] Now, the specific fault areas are determined using a fault area mapping relationship. Within this mapping relationship, potential fault areas corresponding to the feeder and its adjacent equipment in work area A are identified, taking into account the possibility of the fault spreading to adjacent commercial areas. Combining the actual situation of the distribution network, historical fault data, and expert experience, it is assessed that the fault most likely occurred on the feeder in work area A. However, considering the impact of the fault on a portion of the commercial area in work area B, this portion is also included in the fault area. On the distribution network diagram, the feeder and its adjacent equipment in work area A are marked as the primary fault area, and the affected commercial area in work area B is also marked as a fault area. Professional personnel are dispatched to the site for investigation to verify whether the fault area matches the actual situation. The investigation confirms that the fault did indeed occur on the feeder in work area A, but it did not spread to the commercial area in work area B. Therefore, the fault area is adjusted, retaining only the feeder and its adjacent equipment in work area A as the fault area. In some embodiments of this application, a fault area matching table is collected, as shown in Table 1.

[0084] Table 1 Fault Area Matching Table

[0085] Fault area source of failure Area A Feeder 1 fault, Transformer 2 fault Area B Feeder 3 fault, switch 4 fault Area C (partial) Feeder 1 fault (spreading effect)

[0086] Now, the abnormal current parameter combination has been collected, and the first fault range has been determined to be near feeder 1, and the second fault range is region A and part of region C. Based on the first fault range, it is preliminarily determined that the fault occurred on feeder 1 or transformer 2. In the fault region matching table, it is seen that the fault of feeder 1 corresponds to region A and part of region C. Combining the second fault range and the results of the matching table, the fault region is determined to be region A. Considering that the fault of feeder 1 has spread to part of region C, this part of the region is also included in the scope of consideration.

[0087] Please see Figure 5 In step S14, the specific steps are as follows:

[0088] S141: Collect the fault area and perform fault detection on the fault area. Based on the fault detection of the fault area, determine multiple fault markers. Based on the matching of the location of multiple fault markers and the distribution map of the distribution network, determine the corresponding fault path. Based on the detection of the fault path, determine the corresponding faulty distribution network component.

[0089] S142: Determine the function of the faulty distribution network component based on the detection of the faulty distribution network component, monitor the fault area in real time, and collect the current parameters of the fault area at different times. Determine the first fault combination according to the function of the faulty distribution network component and the fault area. The first fault combination includes the faulty component, the fault type, and the fault occurrence time.

[0090] S143: Determine the second fault combination based on the function of the faulty distribution network components and the current parameters of the fault area at different times. Determine multiple fault events in the fault area based on the mapping relationship between the first fault combination, the second fault combination and the fault events. The second fault combination contains fault information, such as the specific fault mode of the faulty component, the stage of fault development, and the impact of the fault on other components.

[0091] In the embodiments of this application, the fault area is collected and fault detection is performed on the fault area. Multiple fault markers are determined based on the fault detection of the fault area. The corresponding fault path is determined based on the matching of the location of the multiple fault markers and the distribution map of the power distribution network. The corresponding faulty power distribution network component is determined based on the detection of the fault path. This approach takes into account the overall consideration of matching the location of multiple fault markers with the distribution map of the power distribution network, ensuring the accuracy of the corresponding fault path.

[0092] At this point, upon arriving at the fault site, comprehensive data collection and fault detection are conducted in the fault area. This typically involves using specialized testing equipment and tools, such as infrared thermal imagers, partial discharge detectors, and current and voltage measuring instruments, to gather various information about the fault area. A professional testing team is then dispatched to the fault site. Infrared thermal imagers are used to detect the temperature distribution of various components in the fault area, identifying points of overheating or abnormal temperature rise. Partial discharge detectors are used to check for partial discharge phenomena in cables, transformers, and other equipment. Current and voltage measuring instruments are used to measure the current and voltage parameters of the fault area to determine if there are problems such as short circuits or overloads.

[0093] After collecting data from the fault area, this data needs to be analyzed to identify fault markers. Fault markers are suspicious or abnormal points discovered during the fault detection process, representing potential fault sources or areas affected by the fault. At this point, the thermal images captured by the infrared thermal imager are analyzed to identify points with abnormally high temperatures as fault markers. Data from the partial discharge detector is analyzed to identify equipment or components with partial discharge as fault markers. Current and voltage measurement data are analyzed to identify points or areas with abnormal fluctuations in current and voltage as fault markers.

[0094] After identifying the fault markers, their location information needs to be matched with the distribution network map. This typically involves converting the location coordinates of the fault markers to the coordinate system of the distribution network map to accurately mark their locations on the map. Simultaneously, the distribution network map is opened using GIS (Geographic Information System) or CAD (Computer-Aided Design) software; the location coordinates of the fault markers are input into the software, or their locations are manually marked on the map; ensuring that the locations of the fault markers accurately correspond to the equipment, lines, and other elements on the distribution network map.

[0095] After matching the fault marker locations with the distribution network map, it is necessary to infer the fault propagation path based on the distribution of fault markers and the topology of the distribution network. This usually involves understanding the connection relationships and electrical characteristics between various devices and lines in the distribution network. At the same time, observe the distribution of fault markers on the distribution network map to find the correlation and trend between them. Based on the topology and electrical characteristics of the distribution network, infer from which point the fault starts and along which path it propagates. Mark the inferred fault path on the map for subsequent analysis and processing.

[0096] After identifying the fault path, it is necessary to inspect each component in the distribution network along this path to find the source of the fault. This usually involves a detailed physical inspection and electrical test of the equipment and lines along the fault path. At the same time, the equipment, lines and other components are inspected one by one along the fault path. Suspicious components are physically inspected, such as observing whether there is any damage or deformation. Suspicious components are also subjected to electrical tests, such as measuring insulation resistance and dielectric loss. Based on the inspection results, the specific faulty distribution network component is determined.

[0097] Specifically, suppose a residential area experiences a sudden power outage in a power distribution network. Upon arrival, the inspection team first uses an infrared thermal imager to examine the fault area, identifying a transformer with excessively high temperature as the first fault marker. Next, they use a partial discharge detector to inspect nearby cables, finding a cable exhibiting partial discharge, marking it as the second fault marker. Then, they match the locations of these two fault markers with the power distribution network map to determine their exact positions on the map. By observing the distribution of the fault markers and the topology of the power distribution network, they deduce that the fault originated from the transformer and propagated along the cable to the distribution box in the residential area. Finally, they inspect the transformer, cable, and distribution box along this fault path, discovering a short circuit in the transformer's internal windings, causing overheating and partial discharge, ultimately identifying the transformer as the source of the fault.

[0098] Furthermore, the function of the faulty distribution network component is determined based on the detection of the faulty distribution network component, the fault area is monitored in real time, and the current parameters of the fault area at different times are collected. The first fault combination is determined according to the function of the faulty distribution network component and the fault area, which takes into account the overall consideration of the function of the faulty distribution network component and the fault area, and ensures the accuracy of the first fault combination.

[0099] At this point, it is crucial to clarify the specific function of the faulty distribution network component within the distribution network. This typically involves understanding the distribution network design, operating principles, and the role of each component. The functional information of the faulty component is essential for subsequent analysis of fault combinations and the development of repair plans. It is also necessary to consult distribution network design drawings and related documents to understand the type, specifications, and location of the faulty component within the distribution network. The function of the faulty component under normal operating conditions should be analyzed, including its electrical characteristics, mechanical performance, and interactions with other components. Finally, the type of fault occurring in the faulty component and its impact on the operation of the distribution network should be considered.

[0100] Real-time monitoring of the fault area is to obtain dynamic information about the faulty component and its surrounding environment in a timely manner. This helps to more accurately judge the development trend of the fault and the impact of the faulty component on other components and the entire power distribution network. At the same time, remote monitoring equipment, such as current and voltage sensors and temperature sensors, should be deployed at key locations in the fault area. Alarm thresholds for the monitoring system should be set to ensure a rapid response when a fault occurs. The operating status of the monitoring equipment should be checked regularly to ensure its accuracy and reliability.

[0101] Collecting current parameters of the fault area at different times is to analyze the changes in the electrical behavior of the faulty components over time, which helps to understand the fault mechanism and its impact range more deeply. At the same time, high-precision current measuring instruments are used to collect current parameters in real time at key locations in the fault area. Current parameters before and after the fault occurs and during the fault handling process are recorded, including current magnitude and waveform. The collected current parameters are sorted and analyzed to look for outliers and trends.

[0102] By combining the functional information of the faulty component, the real-time monitoring data of the faulty area, and the changes in current parameters, the first fault combination is determined. The first fault combination typically includes key information such as the faulty component, the fault type, and the time of fault occurrence. At the same time, the correlation between the function of the faulty component and the real-time monitoring data of the faulty area is analyzed to determine whether the faulty component is in an abnormal state. By combining the changes in current parameters, the type of fault occurring in the faulty component is inferred. Based on the above information, the first fault combination is determined, including the faulty component, the fault type, and the time of fault occurrence.

[0103] Therefore, the second fault combination is determined based on the function of the faulty distribution network components and the current parameters of the fault area at different times. Multiple fault events in the fault area are determined based on the mapping relationship between the first fault combination, the second fault combination and the fault event. This approach takes into account the overall consideration of the mapping relationship between the first fault combination, the second fault combination and the fault event, and ensures the accuracy of multiple fault events in the fault area.

[0104] At this point, further analysis of the functions of the faulty distribution network components and the current parameters of the faulty area at different times is conducted to determine the second fault combination. The second fault combination typically includes more detailed fault information, such as the specific fault mode of the faulty component, the stage of fault development, and the impact of the fault on other components. The electrical and mechanical characteristics of the faulty component during normal operation, as well as the impact of the fault on it, are considered. For example, a faulty transformer can cause current fluctuations, voltage drops, or localized overheating. The changes in current parameters collected before, during, and after the fault, including current magnitude, waveform, and phase, are analyzed. These parameter changes provide clues about the fault type, location, and severity. Based on the functional analysis of the faulty component and the changes in current parameters, the specific fault mode of the faulty component, the stage of fault development, and the potential impact of the fault on other components are inferred, thus forming the second fault combination.

[0105] By combining the first fault combination, the second fault combination, and the pre-established fault event mapping relationship, multiple fault events in the fault area are determined. The fault event mapping relationship is usually a database or knowledge base, which contains the mapping relationship between various fault combinations and corresponding fault events. At the same time, based on the information in the first and second fault combinations, matching fault events are searched in the fault event mapping relationship. The information in the fault combination is compared with the entries in the fault event mapping relationship to find the most matching fault event. Since one fault triggers multiple related events, it is necessary to determine multiple fault events based on different aspects and details in the fault combination.

[0106] Specifically, suppose a feeder switch in a substation suddenly trips, causing a power outage in some areas. After arriving at the site, the inspection team first identified the first fault combination: the faulty component was the feeder switch, and the fault type was overload tripping. The inspection team further analyzed the function of the feeder switch and the current parameters of the faulty area at different times. They found that before the fault occurred, the current on the feeder gradually increased until it exceeded the rated current of the feeder switch. At the same time, they also noticed that after the fault occurred, the temperature of the feeder switch rose rapidly, indicating that there was overheating.

[0107] Based on this information, the testing team identified the second fault combination: the feeder switch overheated due to overload, leading to a trip. They also deduced that the overload was caused by an abnormal increase in current due to a fault in a user's equipment or aging of the line. Finally, the testing team reviewed the fault event mapping relationship and, based on the information in the first and second fault combinations, identified multiple fault events: feeder switch overload tripping events, abnormal increase in current caused by user equipment faults or aging of the line, and power outage events caused by these events. These fault events provided important basis for subsequent development of repair plans, analysis of fault causes, and implementation of preventive measures.

[0108] Specifically, the fault event mapping relationship includes multiple fault events, such as feeder switch damage, line overload, and user equipment failure. The testing team assigned a weight to each fault event to reflect its occurrence and impact on the distribution network operation. Based on the first fault combination (feeder switch overload tripping), the second fault combination (feeder switch internal overheating due to overload), and the matching degree in the fault event mapping relationship, a comprehensive score was calculated for each fault event. Based on the comprehensive score and weight distribution, the testing team identified the following fault events: feeder switch overload tripping event (high weight, high score), line overload event (medium weight, medium score), and abnormal current increase event caused by user equipment failure (low weight, low score but not negligible). These fault events provide important basis for subsequent development of repair plans, analysis of fault causes, and implementation of preventive measures.

[0109] Please see Figure 6 In step S15, the specific steps are as follows:

[0110] S151: Collect multiple fault events in the fault area, determine the maintenance nodes of multiple fault events based on the multiple fault events in the fault area and the current working mode of the distribution network, and mark the maintenance time of the maintenance node; the maintenance node refers to the department, team or individual responsible for the maintenance work of a specific area or specific equipment;

[0111] S152: Construct a maintenance sequence table for the fault area based on the event content of multiple fault events, the maintenance nodes of multiple fault events, and the corresponding maintenance time. Mark each corresponding maintenance event in the maintenance sequence table for the fault area.

[0112] S153: Determine the first maintenance event based on each maintenance event in the maintenance sequence table and the corresponding maintenance time; determine the second maintenance event based on each maintenance event in the maintenance sequence table and the fault maintenance progress of the fault area; determine the autonomous maintenance logic of the distribution network based on the first and second maintenance events to trigger autonomous maintenance of the fault area; the first maintenance event is the maintenance event that needs to be prioritized and identified from the maintenance sequence table; the second maintenance event is the event that needs to be followed up immediately after the first maintenance event is completed, or the next important event determined based on the overall maintenance plan and resource allocation of the fault area.

[0113] In the embodiments of this application, multiple fault events in the fault area are collected, maintenance nodes for multiple fault events are determined based on the multiple fault events in the fault area and the current working mode of the distribution network, and the maintenance time of the maintenance node is marked. This approach takes into account the overall consideration of multiple fault events in the fault area and the current working mode of the distribution network, ensuring the accuracy of the maintenance nodes for multiple fault events.

[0114] At this time, multiple fault events in the fault area are collected. These fault events come from multiple sources, including but not limited to reports from on-site testing personnel, alarms from remote monitoring systems, and user complaints. When collecting fault events, it is necessary to ensure the accuracy and completeness of the information, including the specific location of the fault event, the scope of impact, the time of occurrence, and the cause of the fault.

[0115] The maintenance nodes responsible for handling these fault events are determined based on the collected fault events and the current operating mode of the distribution network. Maintenance nodes typically refer to departments, teams, or individuals responsible for the maintenance of specific areas or equipment. When determining maintenance nodes, factors such as the urgency of the fault event, its scope of impact, and resource allocation under the current operating mode need to be considered. Simultaneously, the urgency of the fault event is assessed based on its scope of impact, severity, and user complaints. Fault events with higher urgency require priority handling. Understanding the current operating mode of the distribution network, load conditions, and planned maintenance work arrangements helps in the more rational allocation of maintenance resources. Based on the urgency of the fault event and the resource allocation under the current operating mode, the maintenance node responsible for handling each fault event is determined. This includes on-site maintenance teams, remote monitoring centers, and specific equipment maintenance departments.

[0116] For each identified maintenance node, a scheduled maintenance time is assigned. This time typically includes a start and end time, which helps in better planning and managing maintenance work. Several factors need to be considered when assigning maintenance times, such as the availability of the maintenance node, the urgency of the fault, and user power demand. Furthermore, after identifying a maintenance node, communication with it is essential to understand its availability, which helps in rationally scheduling maintenance work and avoiding resource conflicts. Faults with high urgency require priority scheduling, necessitating adjustments to the original planned maintenance schedule to ensure timely handling of urgent faults. Based on communication with the maintenance node and the urgency of the fault, a scheduled maintenance time is assigned to each node. This information is recorded and managed using spreadsheets, calendars, or a dedicated maintenance management system.

[0117] Specifically, the following two fault events occurred: Fault Event A: A feeder switch in a commercial area tripped, causing a power outage for some businesses in the area; this event was reported by on-site inspection personnel and was of a high urgency level; Fault Event B: The transformer oil temperature in a residential area was too high, posing a risk of overheating; this event was triggered by an alarm from the remote monitoring system and was of a medium urgency level; Under the current operating mode, the distribution network is in normal operation, the load is stable, and there are few planned maintenance tasks.

[0118] Based on the above information, the following operations were performed: Detailed information on fault events A and B was recorded, including location, scope of impact, time of occurrence, and cause of the fault; based on the urgency of the fault events and the resource allocation under the current work mode, it was determined that the on-site maintenance team would handle fault event A (high urgency), while the transformer maintenance department would handle fault event B (medium urgency); after communicating with the on-site maintenance team and the transformer maintenance department, the maintenance time for fault event A was determined to be from 2 PM to 4 PM (to restore power to the commercial area as quickly as possible), and the maintenance time for fault event B was determined to be from 8 PM to 10 PM (avoiding peak residential electricity consumption periods). This information was recorded in a dedicated maintenance management system for subsequent tracking and management.

[0119] Furthermore, a maintenance sequence table for the fault area is constructed based on the event content of multiple fault events, the maintenance nodes of multiple fault events, and the corresponding maintenance time. In the maintenance sequence table for the fault area, each corresponding maintenance event is marked, and each marked maintenance event is introduced.

[0120] At this point, a maintenance sequence table is constructed based on the information collected from multiple fault events (including event content, maintenance nodes, and corresponding maintenance times). This sequence table will serve as the primary tool for managing and tracking maintenance tasks in the fault area. The format of the maintenance sequence table is then determined, typically including key information columns such as fault event number, event content, maintenance node, and maintenance time. All relevant information for each fault event is collected from previous steps, ensuring that the content of each event, the node responsible for maintenance, and the planned maintenance time are accurately recorded. The collected information is then populated into the maintenance sequence table according to the determined format. Each fault event should have a corresponding row in the sequence table, detailing all its key information.

[0121] In the established maintenance sequence list, each maintenance event is clearly marked. This helps to clearly identify each maintenance task, as well as its position and status throughout the maintenance process. At this point, one or more marker columns are added to the maintenance sequence list to record information such as the status or priority of each maintenance event. Marking rules are set according to the organization's maintenance processes and management needs. For example, different colors or symbols are used to represent different statuses of maintenance events (such as pending, in progress, completed, etc.). Each maintenance event is marked according to the set rules, which is done manually (such as using colored pens or stickers) or by using conditional formatting in a spreadsheet.

[0122] Specifically, suppose the following two fault events were previously collected: Fault Event A: A feeder switch in a commercial area tripped, causing a power outage for some businesses; the responsible maintenance node is the on-site maintenance team, and the planned maintenance time is from 2 PM to 4 PM; Fault Event B: The transformer oil temperature in a residential area is too high, posing a risk of overheating; the responsible maintenance node is the transformer maintenance department, and the planned maintenance time is from 8 PM to 10 PM; a maintenance node matching table is collected, as shown in Table 2:

[0123] Table 2 Maintenance Node Matching Table

[0124]

[0125]

[0126] As maintenance work progresses, the status flags in the maintenance node matching table are updated. For example, when the field maintenance team completes the maintenance of fault event A, the status flag is changed to "Completed". Similarly, when the transformer maintenance department starts to handle fault event B, the status flag is changed to "In Progress". In this way, the progress and status of each maintenance task can be clearly tracked through the maintenance sequence table.

[0127] Therefore, the first maintenance event is determined based on each maintenance event and its corresponding maintenance time in the maintenance sequence table, and the second maintenance event is determined based on each maintenance event in the maintenance sequence table and the fault maintenance process of the fault area. The autonomous maintenance logic of the distribution network is determined based on the first and second maintenance events to trigger the autonomous maintenance of the fault area. This approach takes into account both the first and second maintenance events, ensuring the accuracy of the autonomous maintenance logic of the distribution network. At the same time, a maintenance sequence table for the fault area is introduced, and each maintenance event, its corresponding maintenance time, and the fault maintenance process of the fault area are considered holistically to ensure the effectiveness of the autonomous maintenance of the fault area. This achieves synchronous maintenance of the fault area while the distribution network is in operation.

[0128] At this point, the maintenance events requiring priority processing, i.e., the first maintenance events, are identified from the maintenance sequence list. This is typically determined based on the importance, urgency, and corresponding maintenance time of the maintenance events. Importance involves factors such as the scope of the fault event's impact on users and its impact on the stability of the distribution network. Urgency depends on the immediate danger of the fault event and the urgency of user complaints. Maintenance time helps identify which event is about to reach its scheduled maintenance window. Based on the content of the fault event, its impact on user power supply and the safe and stable operation of the distribution network are assessed to determine its importance. The immediate danger of the fault event and the urgency of user complaints are considered to determine its urgency. The maintenance time of each event in the maintenance sequence list is reviewed, especially those that are about to arrive or have already exceeded their timeout. Combining all this information, the maintenance events requiring priority processing are determined as the first maintenance events.

[0129] The second maintenance event is determined based on the remaining maintenance events in the maintenance sequence list and the maintenance progress of the faulty area. The second maintenance event is either an event that needs to be followed up immediately after the first maintenance event is completed, or the next important event determined according to the overall maintenance plan and resource allocation of the faulty area. At this time, the maintenance progress of the current faulty area is understood, including completed tasks, ongoing tasks, and tasks that have not yet started. The remaining events in the maintenance sequence list are evaluated, considering their importance, urgency, and matching with maintenance resources. The current status and availability of maintenance team, equipment, materials, and other resources are understood to ensure that the second maintenance event can be handled in a timely manner. Based on the above information, the maintenance event that needs to be handled in the second phase is determined as the second maintenance event.

[0130] The autonomous maintenance logic for the distribution network is formulated based on the first and second maintenance events. This includes determining the priority order of maintenance tasks, resource allocation strategies, task execution processes, and monitoring and feedback mechanisms. Once the autonomous maintenance logic is determined, the autonomous maintenance process for the fault area is triggered to ensure that maintenance work can be carried out according to the predetermined plan and logic. Simultaneously, based on the characteristics of the first and second maintenance events and the actual situation of the fault area, detailed autonomous maintenance logic is formulated, including the priority order of maintenance tasks, resource scheduling schemes, specific steps for task execution, and timetables. An automatic triggering mechanism is configured in the maintenance management system to automatically initiate the processing flow of the second maintenance event after the completion of the first maintenance event. This involves the automation of workflow, task allocation, and monitoring and feedback mechanisms. During the execution of the autonomous maintenance process, the maintenance progress is continuously monitored to ensure that all tasks are carried out according to the predetermined logic and timetable. At the same time, feedback information from the field is collected and processed in a timely manner to make necessary adjustments and optimizations to the autonomous maintenance logic. A maintenance sequence table is collected, as shown in Table 3.

[0131] Table 3 Maintenance Sequence List

[0132]

[0133] Now, we need to determine the first and second maintenance events and formulate the autonomous maintenance logic: First maintenance event: Considering that event 001 (commercial area feeder switch tripping) has a wide impact on user power supply and is highly urgent, and its maintenance time is imminent, it is determined as the first maintenance event. Second maintenance event: After handling event 001, we need to consider the next important and urgent maintenance event; although event 002 (residential area transformer oil temperature too high) is also highly urgent, its maintenance time is at night, which does not conflict with event 001; however, considering that event 003 (industrial area line aging), although relatively less urgent, has a wide impact and preventative replacement work is crucial for the long-term stable operation of the distribution network, and its maintenance time is scheduled for tomorrow morning, it does not conflict with the handling of events 001 and 002; therefore, if resources permit, event 003 will be considered as the second maintenance event for preparation and planning (Note: In actual operation, the determination of the second maintenance event also needs to consider more factors, such as the availability of the maintenance team, the preparedness of materials, etc.).

[0134] Based on the above analysis, the following autonomous maintenance logic was established: First, the on-site maintenance team handles event 001; after handling, if resources and time constraints permit (i.e., if the urgency of event 002 is very high and the transformer oil temperature continues to rise), the transformer maintenance department will immediately initiate the handling of event 002; otherwise, priority will be given to preparing and planning the handling of event 003 (such as allocating materials and arranging personnel in advance), and handling will be carried out according to the predetermined schedule the next morning; at the same time, an automatic triggering mechanism is configured to monitor the completion status of event 001, and automatically initiate the handling process of event 002 or 003 upon its completion (depending on the actual situation at the time and adjustments to the autonomous maintenance logic).

[0135] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of an IoT-based power distribution network fault detection system according to an embodiment of the present invention; the IoT-based power distribution network fault detection system includes:

[0136] The working area module 21 is used to collect the distribution map of the distribution network based on the Internet of Things, and to determine multiple working areas according to the distribution map of the distribution network and the current working mode of the distribution network.

[0137] The abnormal current parameter module 22 is used to determine the corresponding current parameter combination based on the current detection of each working area, and to determine the corresponding abnormal current parameter based on the current detection of each current parameter combination.

[0138] The fault area module 23 is used to determine the abnormal current parameter combination based on the comparison of various current parameter combinations, and to determine the fault area of ​​the distribution network based on the abnormal current parameter combination, abnormal current parameters and the working type of each working area.

[0139] The fault event module 24 is used to determine the corresponding faulty distribution network component based on the fault detection of the faulty area, and to determine multiple fault events of the faulty area according to the function of the faulty distribution network component and the current parameters of the faulty area at different times.

[0140] The maintenance module 25 is used to determine the maintenance sequence list of the fault area based on multiple fault events in the fault area and the current working mode of the distribution network, and to trigger autonomous maintenance of the fault area based on each maintenance event, the corresponding maintenance time, and the fault maintenance process of the fault area in the maintenance sequence list.

[0141] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for detecting faults in a power distribution network based on the Internet of Things, characterized in that, include: Based on the Internet of Things, the distribution map of the power distribution network is collected, and multiple working areas are determined according to the distribution map and the current working mode of the power distribution network. The corresponding combination of current parameters is determined based on the current detection of each working area, and the corresponding abnormal current parameters are determined based on the current detection of each combination of current parameters. The abnormal current parameter combination is determined by comparing the various current parameter combinations, and the fault area of ​​the distribution network is determined by the abnormal current parameter combination, the abnormal current parameters, and the working type of each working area. Based on the fault detection of the fault area, the corresponding faulty distribution network component is identified, and multiple fault events of the fault area are determined according to the function of the faulty distribution network component and the current parameters of the fault area at different times. The maintenance sequence table for the fault area is determined based on multiple fault events in the fault area and the current operating mode of the distribution network. Autonomous maintenance of the fault area is triggered based on each maintenance event in the maintenance sequence table, the corresponding maintenance time, and the fault maintenance process of the fault area.

2. The method for detecting distribution network faults based on the Internet of Things according to claim 1, characterized in that, The distribution map of the power distribution network, collected based on the Internet of Things, is used to determine multiple working areas according to the distribution map and the current operating mode of the power distribution network, including: The location of the distribution network is collected, and the corresponding communication network space is determined based on the location of the distribution network and the corresponding Internet of Things. The distribution map of the distribution network is determined by traversing the communication network space. Multiple sub-regions are determined based on the distribution map of the power distribution network, and the corresponding operating status is determined based on the detection of multiple sub-regions; the operating status includes normal, overload, or maintenance. The current operating mode of the distribution network is collected. Based on the current operating mode of the distribution network, the operating status of multiple sub-regions, and the location of multiple sub-regions, multiple working areas are determined. Each working area is marked with its corresponding working function. The current operating mode includes peak hours, maintenance periods, or routine maintenance.

3. The method for detecting distribution network faults based on the Internet of Things according to claim 1, characterized in that, The process of determining the corresponding current parameter combination based on current detection in each working area, and determining the corresponding abnormal current parameter based on current detection of each current parameter combination, includes: In each working area, multiple current detection nodes are determined based on the markings of the working area, and the corresponding current parameters are determined based on the current detection of the multiple current detection nodes. The position of the current detection node is used as the corresponding detection position of the current parameter. The system monitors multiple current parameters in the same working area in real time and determines the corresponding combination of current parameters based on the magnitude and location of the multiple current parameters. This combination of current parameters marks the corresponding working area. In each combination of current parameters, the corresponding abnormal current parameters are determined based on the combination of current parameters, the previous current parameters of the working area, and the matching of the working functions corresponding to the working area.

4. The Internet of Things-based power distribution network fault detection method according to claim 1, characterized in that, The process of determining abnormal current parameter combinations based on comparisons of various current parameter combinations, and determining the fault areas of the distribution network based on abnormal current parameter combinations, abnormal current parameters, and the operating types of each working area, includes: Collect various current parameter combinations, determine the corresponding matching coefficients based on the comparison of each current parameter combination, determine the preset matching coefficient thresholds based on each current parameter combination and the current working mode of the distribution network, and determine the abnormal current parameter combinations based on the comparison of each matching coefficient with the preset matching coefficient thresholds.

5. The Internet of Things-based power distribution network fault detection method according to claim 4, characterized in that, The process of determining abnormal current parameter combinations based on comparisons of various current parameter combinations, and determining the fault area of ​​the distribution network based on abnormal current parameter combinations, abnormal current parameters, and the operating type of each working area, also includes: The first fault range is determined based on the combination of abnormal current parameters and the abnormal current parameters themselves. The second fault range is determined based on the combination of abnormal current parameters and the type of work in each working area. The first fault range includes the fault point itself and adjacent equipment and lines affected by the fault. The second fault range includes equipment and lines indirectly affected, as well as areas where preventive measures need to be taken. In a power distribution network, the fault area is determined based on the mapping relationship between the first fault range, the second fault range, and the fault area.

6. The method for detecting distribution network faults based on the Internet of Things according to claim 1, characterized in that, The fault detection based on the fault region determines the corresponding faulty distribution network component, and determines multiple fault events in the fault region based on the function of the faulty distribution network component and the current parameters of the fault region at different times, including: The fault area is collected and fault detection is performed. Multiple fault markers are determined based on the fault detection of the fault area. The corresponding fault path is determined based on the matching of the location of the multiple fault markers with the distribution network distribution map. The corresponding faulty distribution network component is determined based on the detection of the fault path.

7. The Internet of Things-based power distribution network fault detection method according to claim 6, characterized in that, The method of determining the corresponding faulty distribution network component based on fault detection of the faulty region, and determining multiple fault events of the faulty distribution network component based on the function of the faulty distribution network component and the current parameters of the faulty region at different times, further includes: The function of the faulty distribution network component is determined by detecting the faulty distribution network component, the fault area is monitored in real time, and the current parameters of the fault area at different times are collected. A first fault combination is determined according to the function of the faulty distribution network component and the fault area. The first fault combination includes the faulty component, the fault type, and the fault occurrence time. The second fault combination is determined based on the function of the faulty distribution network components and the current parameters of the fault area at different times. Multiple fault events in the fault area are determined based on the mapping relationship between the first fault combination, the second fault combination, and the fault events. The second fault combination contains fault information, such as the specific fault mode of the faulty component, the stage of fault development, and the impact of the fault on other components.

8. The method for detecting distribution network faults based on the Internet of Things according to claim 1, characterized in that, The process of determining a maintenance sequence list for the fault area based on multiple fault events and the current operating mode of the distribution network, and triggering autonomous maintenance of the fault area based on each maintenance event, corresponding maintenance time, and fault maintenance progress in the fault area according to the maintenance sequence list, includes: Collect multiple fault events in the fault area, determine the maintenance nodes of multiple fault events based on the multiple fault events in the fault area and the current working mode of the distribution network, and mark the maintenance time of the maintenance node; the maintenance node refers to the department, team or individual responsible for the maintenance work of a specific area or specific equipment.

9. The method for detecting distribution network faults based on the Internet of Things according to claim 8, characterized in that, The step of determining a maintenance sequence list for the fault area based on multiple fault events in the fault area and the current operating mode of the distribution network, and triggering autonomous maintenance of the fault area based on each maintenance event, corresponding maintenance time, and fault maintenance progress in the fault area according to the maintenance sequence list, further includes: A maintenance sequence table for the fault area is constructed based on the event content of multiple fault events, the maintenance nodes of multiple fault events, and the corresponding maintenance time. Each maintenance event is marked in the maintenance sequence table for the fault area. The first maintenance event is determined based on the maintenance events in the maintenance sequence table and their corresponding maintenance times. The second maintenance event is determined based on the maintenance events in the maintenance sequence table and the fault maintenance progress of the fault area. The autonomous maintenance logic of the distribution network is determined based on the first and second maintenance events to trigger autonomous maintenance of the fault area. The first maintenance event is the maintenance event that needs to be prioritized and identified from the maintenance sequence table. The second maintenance event is the event that needs to be followed up immediately after the first maintenance event is completed, or the next important event determined based on the overall maintenance plan and resource allocation of the fault area.

10. A power distribution network fault detection system based on the Internet of Things, characterized in that, The IoT-based distribution network fault detection system is applied to the IoT-based distribution network fault detection method as described in any one of claims 1-9, wherein the IoT-based distribution network fault detection system comprises: The working area module is used to collect distribution maps of the power distribution network based on the Internet of Things, and to determine multiple working areas based on the distribution map and the current working mode of the power distribution network. The abnormal current parameter module is used to determine the corresponding combination of current parameters based on the current detection of each working area, and to determine the corresponding abnormal current parameter based on the current detection of each combination of current parameters. The fault area module is used to determine the abnormal current parameter combination based on the comparison of various current parameter combinations, and to determine the fault area of ​​the distribution network based on the abnormal current parameter combination, abnormal current parameters and the working type of each working area. The fault event module is used to determine the corresponding faulty distribution network component based on the fault detection of the faulty area, and to determine multiple fault events of the faulty distribution network component according to the function of the faulty distribution network component and the current parameters of the faulty area at different times. The maintenance module is used to determine the maintenance sequence list of the fault area based on multiple fault events in the fault area and the current working mode of the distribution network, and to trigger autonomous maintenance of the fault area based on each maintenance event, the corresponding maintenance time, and the fault maintenance progress of the fault area in the maintenance sequence list.

Images