Electrical fault detection system and electrical fault positioning method

Abstract

The invention provides an electrical fault detection system and an electrical fault positioning method. The electrical quantity of a corresponding node in a power supply network is monitored in real time through an intelligent monitoring sensor; determining a fault suspected area of the fault indication node in the power supply network according to the fault indication node and the power supply network weighted graph; calculating a waveform similarity index and a signal intensity attenuation index between each potential fault point and the fault indication node; determining a fault propagation feature of each potential fault point based on an edge weight of the power supply network weighted graph; and determining a comprehensive fault value of each potential fault point according to the fault propagation characteristics of each potential fault point, the corresponding waveform similarity index and the corresponding signal strength attenuation index, and further positioning a final fault area of the target underground power supply area according to all the comprehensive fault values. By adopting the scheme of the invention, multiple potential fault points can be distinguished based on the transient characteristics of the fault current and the loss of the fault signal.

Description

Technical Field

[0001] This application relates to the field of fault detection technology, and in particular to an electrical fault detection system and an electrical fault location method. Background Technology

[0002] Fault detection is the process of identifying whether a system, device, or component deviates from its normal operating state through technical means, thereby determining whether a fault exists. It does not focus on the specific location or cause of the fault. Its core objective is to establish a "normal operating baseline" based on the system's design parameters, historical operating data, or standard thresholds. By comparing the actual operating state, it determines whether there are any abnormalities, thus providing a basis for subsequent fault location, diagnosis, and repair.

[0003] Electrical fault location is the process of determining the specific physical location of a detected fault in an electrical system using technical means. It is a follow-up action to fault detection, initiated only after the existence of a fault is confirmed. Its core objective is to accurately locate the fault point and shorten repair time. Currently, there are often multiple potential fault points in underground electrical faults (e.g., a certain area may simultaneously have "cable insulation aging" and "minor short circuit in motor windings"). Traditional fault location methods rely on a single indicator. If only "insulation resistance value" is used, it may be impossible to distinguish between different fault points because their insulation resistances are similar (e.g., the insulation resistance of aging cables and slightly short-circuited motors are both low). Alternatively, if only "current value" is used, current fluctuations caused by underground electromagnetic interference can easily lead to misinterpreting "normal fluctuations" as "fault signals," or it may be impossible to distinguish "abnormal current characteristics of different fault points" (e.g., the difference in current waveforms between short circuits and overloads is ignored). Therefore, how to distinguish multiple potential fault points based on the transient characteristics of fault current and the loss of fault signals, thereby achieving accurate electrical fault location, has become a challenge for the industry. Summary of the Invention

[0004] Based on this, this application provides an electrical fault detection system and an electrical fault location method for distinguishing multiple potential fault points based on the transient characteristics of fault current and the loss of fault signal.

[0005] In a first aspect, this application provides an electrical fault location method based on regional collaboration, wherein a weighted power supply network map is pre-constructed according to the network topology of the target underground power supply area, and the method includes the following steps: Intelligent monitoring sensors are deployed at each node of the target underground power supply network, and the electrical quantities of the corresponding nodes in the power supply network are monitored in real time through each intelligent monitoring sensor. When any node detects an electrical quantity that meets a predetermined fault triggering condition, the node is marked as a fault indication node. Then, based on the fault indication node and the power supply network weighted graph, the suspected fault area of ​​the fault indication node in the power supply network is determined. Retrieve transient waveform data of fault current at all potential fault points within the suspected fault area, and calculate waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node; The fault propagation characteristics of each potential fault point are determined based on the edge weights of the weighted graph of the power supply network. Based on the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of each potential fault point, the comprehensive fault value of each potential fault point is determined, and then the final fault zone of the target underground power supply area is located based on all the comprehensive fault values.

[0006] In some embodiments, determining the suspected fault area of ​​the fault indicator node in the power supply network based on the fault indicator node and the power supply network weighted graph specifically includes: Based on the real-time current flow direction of the fault indicator node, determine multiple downstream associated nodes of the fault indicator node in the power supply network; The electrical distance between the fault indication node and each downstream associated node is determined based on the weighted graph of the power supply network. Obtain the preset electrical distance threshold; The electrical distance threshold is compared with the electrical distance of each downstream associated node to generate the suspected fault area of ​​the fault indication node in the power supply network.

[0007] In some embodiments, retrieving transient fault current waveform data of all potential fault points within the suspected fault area specifically includes: Based on the suspected fault area and the power supply network weighted map, multiple potential fault points within the suspected fault area in the power supply network weighted map are screened. Acquire transient waveform data of fault current at each potential fault point.

[0008] In some embodiments, calculating the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node specifically includes: The fault current transient waveform data of the fault indication node and each potential fault point are aligned with the time axis. Based on the time-axis aligned transient waveform data of the fault current, calculate the waveform similarity index between each potential fault point and the fault indication node. Extract the peak fault current of each potential fault point and the fault indication node from the time-axis aligned transient fault current waveform data; The signal strength attenuation index between each potential fault point and the fault indication node is determined based on all fault current peak values.

[0009] In some embodiments, determining the fault propagation characteristics of each potential fault point based on the edge weights of the power supply network weighted graph specifically includes: Obtain the topology and edge weights of the weighted graph of the power supply network; For each potential fault point, taking the fault indication node as the signal receiving point and the potential fault point as the fault signal source point, the theoretical propagation path of the signal from the fault signal source point to the signal receiving point is calculated based on the topology and edge weights. The theoretical attenuation coefficient of the fault characteristic signal propagating along the theoretical propagation path is calculated based on the edge weights on the theoretical propagation path. The theoretical attenuation coefficient is then used as the fault propagation characteristic of the potential fault point, thereby obtaining the fault propagation characteristic of each potential fault point.

[0010] In some embodiments, determining the comprehensive fault value of each potential fault point based on its fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index specifically includes: For each potential fault point, a corresponding weight coefficient is assigned to the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault point based on a preset feature weight allocation strategy. Based on the corresponding weight coefficients, the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault points are weighted and fused to obtain the comprehensive fault value of the potential fault points, and then the comprehensive fault value of each potential fault point is obtained.

[0011] In some embodiments, locating the final fault zone of the target downhole power supply area based on all comprehensive fault values ​​specifically includes: Sort the comprehensive fault values ​​of all potential fault points in descending order to generate a sequence of candidate fault points; Calculate the relative difference between the highest and second-highest comprehensive failure values; Based on the relative difference and the preset confidence threshold, the final fault area is determined from the candidate fault point sequence; Output the final fault location information and confidence level.

[0012] Secondly, this application provides an electrical fault detection system, which includes an electrical fault location unit, the electrical fault location unit comprising: The data acquisition module is used to deploy intelligent monitoring sensors at each node of the target downhole power supply network, and then monitor the electrical quantities of the corresponding nodes in the power supply network in real time through each intelligent monitoring sensor. The processing module is used to mark any node as a fault indication node when the electrical quantity detected by any node meets the predetermined fault triggering conditions, and then determine the suspected fault area of ​​the fault indication node in the power supply network based on the fault indication node and the power supply network weighted graph. The processing module is also used to retrieve transient waveform data of fault current at all potential fault points in the suspected fault area, and to calculate the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node. The processing module is also used to determine the fault propagation characteristics of each potential fault point based on the edge weights of the weighted graph of the power supply network. The execution module is used to determine the comprehensive fault value of each potential fault point based on the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of each potential fault point, and then locate the final fault area of ​​the target downhole power supply area based on all the comprehensive fault values.

[0013] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described regional collaborative electrical fault location method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described regional collaborative electrical fault location method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The electrical fault detection system and electrical fault location method provided in this application firstly deploy intelligent monitoring sensors at each node of the target downhole power supply network, and then monitor the electrical quantities of the corresponding nodes in the power supply network in real time through each intelligent monitoring sensor; when the electrical quantity monitored by any node meets the predetermined fault triggering conditions, the node is marked as a fault indication node, and then the suspected fault area of ​​the fault indication node in the power supply network is determined based on the fault indication node and the weighted graph of the power supply network; the transient waveform recording data of the fault current of all potential fault points in the suspected fault area are retrieved, and the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node are calculated; the fault propagation characteristics of each potential fault point are determined based on the edge weights of the weighted graph of the power supply network; the comprehensive fault value of each potential fault point is determined based on the fault propagation characteristics, the corresponding waveform similarity index and signal strength attenuation index of each potential fault point, and then the final fault area of ​​the target downhole power supply area is located based on all the comprehensive fault values.

[0016] Therefore, when any node detects an electrical quantity that meets a predetermined fault triggering condition, this application marks that node as a fault indication node. Then, based on the fault indication node and the weighted graph of the power supply network, the suspected fault area of ​​the fault indication node in the power supply network is determined. This is a network sub-topology starting from the fault indication node, extending downstream along the current direction, and with a total electrical distance not exceeding a preset threshold, representing the range where the fault point is most likely to exist. This step enables rapid and accurate preliminary fault location from monitoring the entire network. By marking nodes with electrical quantities exceeding limits as fault indication nodes and intelligently delineating suspected fault areas based on their downstream electrical distance thresholds, the scope of fault investigation can be significantly reduced, improving the efficiency of preliminary fault location. The high efficiency and accuracy lay a solid foundation for subsequent precise location. Secondly, transient waveform data of the fault current at all potential fault points within the suspected fault area are retrieved, and waveform similarity and signal strength attenuation indices are calculated between each potential fault point and the fault indication node. The waveform similarity index effectively reflects the morphological changes of the fault current waveform during propagation. The signal strength attenuation index is a quantitative parameter used to quantify the degree of energy attenuation of the fault current signal as it propagates from the potential fault point to the fault indication node. Its core function is to reflect the relative magnitude of the electrical distance or signal transmission loss between the potential fault point and the fault indication node by measuring the signal strength attenuation. This step allows for... This method extracts high-precision transient waveform data from suspected fault areas and performs multi-dimensional feature analysis. By calculating waveform similarity to determine the source of fault currents and calculating signal attenuation to assess electrical distance, it can fully utilize transient fault information and improve the reliability and objectivity of preliminary screening and ranking of potential fault points. Then, based on the edge weights of the power supply network weighted graph, the fault propagation characteristics of each potential fault point are determined. Based on the fault propagation characteristics, corresponding waveform similarity indices, and signal strength attenuation indices of each potential fault point, a comprehensive fault value is determined. This comprehensive fault value is a comprehensive evaluation value calculated by fusing multi-source, heterogeneous fault feature indices, and its core function is... To eliminate the randomness or one-sidedness that may exist in a single indicator, a unified quantitative standard that can comprehensively and evenly reflect the probability of whether each potential fault point is a real fault point is formed. Through this step, the theoretical propagation characteristics reflecting the physical structure of the power grid can be deeply integrated with the waveform and attenuation indicators reflecting actual monitoring data. A comprehensive fault probability evaluation value is obtained through weighted calculation, thereby comprehensively utilizing prior knowledge and real-time data to improve the overall accuracy, anti-interference ability and fault tolerance of the final fault point location decision. Finally, the final fault area of ​​the target underground power supply area is located based on all comprehensive fault values. In summary, the scheme of this application can distinguish multiple potential fault points based on the transient characteristics of the fault current and the loss of the fault signal. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of an electrical fault location method based on regional cooperation, according to some embodiments of this application; Figure 2 This is a schematic diagram illustrating an application scenario of an electrical fault location unit according to some embodiments of this application; Figure 3 This is a flowchart illustrating the process of determining a suspected fault area according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electrical fault location unit according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device that implements a regional collaborative electrical fault location method according to some embodiments of this application. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 The figure is an exemplary flowchart of an electrical fault location method based on regional cooperation according to some embodiments of this application. The electrical fault location method based on regional cooperation mainly includes the following steps: It should be noted that, as a preferred embodiment, the power supply network weighted graph is pre-constructed based on the network topology of the target underground power supply area. Specifically, the construction of the power supply network weighted graph based on the network topology of the target underground power supply area can be achieved in the following way: First, obtain the complete primary system wiring diagram of the target underground power supply area, and extract all switches, transformers, distribution points, and line connections from the diagram as the topological basis for network nodes and edges; then, use each electrical device (e.g., switch, transformer winding) as a node, and the cables or overhead lines connecting the devices as edges connecting two nodes in the diagram; finally, based on the technical parameters of the lines (including line type, cross-sectional area, length...),... The impedance per unit length of the line is calculated based on its length and material properties, and the product of the actual length of the line and the impedance per unit length is used as the weight value of the corresponding edge. For branches containing transformers, the equivalent impedance value referred to the same voltage side is also used as part of the edge weight. Finally, a weighted graph of the power supply network containing all nodes, edges and their weight values ​​is generated and stored in the database of the monitoring master station for subsequent fault location process calls. The edge weights of the power supply network weighted graph directly reflect the impedance characteristics of the line and are used to characterize the attenuation relationship of the fault current when it propagates in the network. In other embodiments, other methods can be used to construct the power supply network weighted graph, which is not limited here.

[0020] In step 101, intelligent monitoring sensors are deployed at each node of the target downhole power supply network, and the electrical quantities of the corresponding nodes in the power supply network are monitored in real time through each intelligent monitoring sensor.

[0021] In practice, intelligent monitoring sensors are deployed at each node of the target underground power supply network. Real-time monitoring of the electrical quantities at corresponding nodes in the power supply network is achieved through these sensors. This can be accomplished in the following way: Multifunctional monitoring terminals are installed at key nodes in the target underground power supply network, such as the power input terminals, the incoming and outgoing circuits of each horizontal mid-section substation, important branch line connection points, and line ends. These terminals are equipped with zero-sequence current transformers to collect zero-sequence current in real time, and built-in three-phase voltage sensors to collect three-phase voltage signals. The collected three-phase voltage signals are then used to calculate and generate a negative-sequence voltage in real time. For example: the embedded processor calculates and generates the negative sequence voltage in real time based on the acquired three-phase voltage signal using the symmetrical component method; all electrical quantity data (including the directly acquired zero-sequence current and the real-time calculated negative sequence voltage) are synchronously acquired at a sampling frequency of 4kHz, and after conversion and digital filtering, are uploaded to the ground monitoring center in real time; the monitoring terminal has a built-in fault recording function, which automatically starts recording when a sudden increase in zero-sequence current or an abnormality in negative sequence voltage is detected, and records complete transient waveform data including zero-sequence current and negative sequence voltage. Other methods can be used in other embodiments, which are not limited here.

[0022] It should be noted that all terminals in this application achieve microsecond-level time synchronization through an underground NTP time server to ensure the time sequence consistency of data at different nodes and provide an accurate data foundation for subsequent fault analysis.

[0023] In some embodiments, reference Figure 2 As shown in the figure, this figure is a schematic diagram of the application scenario of the electrical fault location unit shown in some embodiments of this application. The figure includes three main components: a data acquisition device, a server, and a data storage device. The data acquisition device is responsible for collecting the electrical quantities of the corresponding nodes in the power supply network and sending the collected electrical quantities to the server through the communication network. The electrical fault location unit runs in the server. The server stores the processing results in the data storage device and visualizes them.

[0024] In step 102, when the electrical quantity monitored by any node meets the predetermined fault triggering conditions, the node is marked as a fault indication node, and then the suspected fault area of ​​the fault indication node in the power supply network is determined based on the fault indication node and the power supply network weighted graph.

[0025] In specific implementation, when any node detects an electrical quantity that meets the predetermined fault triggering conditions, marking that node as a fault indication node can be achieved in the following way: when any node detects that the effective value of the zero-sequence current exceeds the 20A threshold for 20ms continuously, or the effective value of the negative-sequence voltage exceeds 15% of the rated voltage for 30ms continuously, the intelligent monitoring terminal of that node immediately sends a fault trigger signal with time stamp information to the ground monitoring master station; after receiving the signal, the monitoring master station first verifies its timing validity, and then marks the node as a fault indication node in a red flashing state in the real-time network topology diagram, while recording the node's number, fault type (zero-sequence or negative-sequence over-limit), over-limit amplitude, and accurate trigger timestamp; the fault indication node information will be stored in the current fault event record in the fault analysis database and used as the starting point for tracing the source of the suspected fault area; among them, for nodes that trigger both zero-sequence and negative-sequence over-limits simultaneously, the system will assign them a higher fault confidence level and give them priority in subsequent analysis. In other embodiments, the duration of the trigger threshold can also be adjusted, which is not limited here.

[0026] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining a suspected fault area in some embodiments of this application. In this embodiment, determining the suspected fault area of ​​the fault indicator node in the power supply network based on the fault indicator node and the weighted graph of the power supply network can be achieved by the following steps: In step 1031, multiple downstream associated nodes of the fault indication node in the power supply network are determined based on the real-time current flow direction of the fault indication node. In step 1032, the electrical distance between the fault indication node and each downstream associated node is determined based on the weighted graph of the power supply network; In step 1033, a preset electrical distance threshold is obtained; In step 1034, the electrical distance threshold is compared with the electrical distance of each downstream associated node to generate a suspected fault area of ​​the fault indication node in the power supply network.

[0027] Specifically, in implementation, determining multiple downstream associated nodes of the fault indication node in the power supply network based on the real-time current flow direction of the fault indication node can be achieved in the following way: First, obtain the instantaneous values ​​of the three-phase current collected by the intelligent monitoring terminal installed at the fault indication node within one power frequency cycle before and after the fault occurs; determine the mainstream current direction by analyzing the phase relationship and amplitude change of the three-phase current and combining it with the pre-configured power grid topology connection relationship; then, starting from the fault indication node, traverse all nodes directly or indirectly connected to it in the weighted diagram of the power supply network along the mainstream current direction; finally, take all load nodes, branch nodes, and terminal nodes identified through the above traversal process that are located downstream of the current of the fault indication node as the set of downstream associated nodes of the fault indication node; wherein, the determination of the current direction can be based on a comprehensive determination of the power flow direction or the propagation direction of the fault transient current, and other methods can also be used in other embodiments, which are not limited here.

[0028] In specific implementation, determining the electrical distance between the fault indicator node and each downstream associated node based on the weighted graph of the power supply network can be achieved in the following way: Based on the pre-constructed weighted graph of the power supply network (where the edge weights represent line impedances), the shortest path algorithm is used to calculate the shortest path from the fault indicator node to each downstream associated node; the weights (impedance values) of all edges traversed on the shortest path are summed, and the sum is taken as the electrical distance between the fault indicator node and the downstream associated node; wherein, the electrical distance is a dimensionless scalar value, the magnitude of which reflects the total impedance experienced by the fault current as it propagates from the indicator node to the downstream node. The larger the impedance, the farther the electrical distance and the more significant the current attenuation. Other methods can also be used in other embodiments, which are not limited here.

[0029] In specific implementation, comparing the electrical distance threshold with the electrical distance of each downstream associated node to generate the suspected fault area of ​​the fault indicator node in the power supply network can be achieved in the following way: traversing all downstream associated nodes, comparing the electrical distance of each node with the preset electrical distance threshold one by one; filtering out all downstream associated nodes whose electrical distance is less than or equal to the threshold; delineating a continuous, closed network area by these nodes and all intermediate nodes, lines, and devices on all paths connecting these nodes to the fault indicator node, as the suspected fault area corresponding to the fault indicator node; wherein, the suspected fault area is a network sub-topology that starts from the fault indicator node, extends along the downstream direction of the current, and the total electrical distance does not exceed the preset threshold, representing the range where the fault point is most likely to exist. Other methods can also be used in other embodiments, which are not limited here.

[0030] It should be noted that the electrical distance threshold in this application is an empirical value pre-set based on historical fault data statistical analysis, power grid simulation calculation, and protection coordination principles. Specifically, it is obtained by querying the system configuration parameter table in the monitoring master station database. This parameter table stores the recommended electrical distance thresholds for different voltage levels and operating modes. As a preferred embodiment, this threshold is usually set to 70% to 80% of the total electrical distance from the power source point to the farthest load point to ensure coverage of the vast majority of possible fault areas. When there are significant changes in the system operating mode, maintenance personnel can manually adjust this threshold through the human-machine interface to adapt to the new network structure. Other methods can also be used in other embodiments, which are not limited here.

[0031] It should be noted that the above steps can achieve rapid and accurate preliminary location from full network monitoring. By marking nodes that exceed electrical quantity limits as fault indication nodes and intelligently delineating suspected fault areas based on their downstream electrical distance thresholds, the scope of fault investigation can be significantly narrowed, improving the efficiency and accuracy of preliminary fault location and laying a solid foundation for subsequent precise location.

[0032] In step 103, fault current transient waveform data of all potential fault points in the suspected fault area are retrieved, and waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node are calculated.

[0033] In some embodiments, retrieving transient fault current waveform data of all potential fault points within the suspected fault area can be achieved using the following steps: Based on the suspected fault area and the power supply network weighted map, multiple potential fault points within the suspected fault area in the power supply network weighted map are screened. Acquire transient waveform data of fault current at each potential fault point.

[0034] In a specific implementation, based on the suspected fault area and the weighted graph of the power supply network, the screening of multiple potential fault points within the suspected fault area in the weighted graph of the power supply network can be achieved in the following way: First, the suspected fault area is defined as a connected subgraph in the weighted graph of the power supply network consisting of specific nodes and edges; then, all line segments (i.e., edges in the weighted graph) within this subgraph are traversed, and each line segment is treated as an independent potential fault point; next, a unique identifier is generated for each potential fault point (line segment), which is composed of the numbers of its two endpoints (e.g., "node A-node B"); finally, line segments that are close to the power supply side and theoretically cannot possibly fail (e.g., transformer outlet lines) are excluded, forming the final list of potential fault points. In a preferred embodiment, the system will pay special attention to lines in high-fault areas such as cable joints and line corners, and prioritize their inclusion in the list of potential fault points. Other methods can also be used in other embodiments, which are not limited here.

[0035] In specific implementation, the fault current transient waveform data of each potential fault point can be obtained in the following way: Based on the unique identifier of each line segment in the potential fault point list, determine its corresponding two-end nodes; send a data query request to the real-time database storing all data uploaded by intelligent monitoring terminals through the data management interface of the monitoring master station; this request includes the target node number, the fault occurrence time marker, and the data type to be obtained (zero-sequence current transient waveform data); after receiving the request, the database retrieves the current transient waveform data recorded by the corresponding node within a 200ms time window before and after the fault occurrence time marker; finally, organize and associate the retrieved waveform data according to the potential fault points to form the fault current transient waveform data corresponding to each potential fault point; wherein, the fault current transient waveform data contains a high sampling rate instantaneous value sequence, which can truly reflect the details of current changes before and after the fault occurs. Other methods can also be used in other embodiments, which are not limited here.

[0036] In some embodiments, calculating the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node can be achieved by the following steps: The fault current transient waveform data of the fault indication node and each potential fault point are aligned with the time axis. Based on the time-axis aligned transient waveform data of the fault current, calculate the waveform similarity index between each potential fault point and the fault indication node. Extract the peak fault current of each potential fault point and the fault indication node from the time-axis aligned transient fault current waveform data; The signal strength attenuation index between each potential fault point and the fault indication node is determined based on all fault current peak values.

[0037] In specific implementation, the time axis alignment of the fault current transient waveform data of the fault indication node and each potential fault point can be achieved in the following way: First, using the fault triggering time of the fault indication node as the reference time point, obtain the time series of its fault current transient waveform data; then, read the fault current transient waveform data recorded by the intelligent monitoring terminal of each node corresponding to each potential fault point; through time stamp comparison, calculate the inherent communication delay and sampling time deviation between each node and the fault indication node, and use a linear interpolation algorithm to correct the time offset of the waveform data of each potential fault point, so that the zero time of all data is aligned with the fault triggering time of the fault indication node; finally, resample the aligned data to ensure that the sampling point time and sampling interval of all waveform data are completely consistent, forming a time-synchronized transient waveform dataset. Other methods can also be used in other embodiments, which are not limited here.

[0038] In specific implementation, the waveform similarity index between each potential fault point and the fault indication node can be calculated based on the time-axis aligned transient waveform data of the fault current. This can be achieved as follows: Zero-sequence current transient waveform data within a 20ms time window during the initial stage of the fault after time-axis alignment is selected as the analysis object; the zero-sequence current waveform sequences of the potential fault point and the fault indication node are time-aligned to eliminate the time delay difference between them; then, the Pearson correlation coefficient between the aligned two waveform sequences is directly calculated to quantify the waveform similarity; this correlation coefficient is normalized to a value between 0 and 1, serving as the waveform similarity index; a value of 1 indicates that the waveforms are completely identical, and a value of 0 indicates that the waveforms are completely uncorrelated. This waveform similarity index can effectively reflect the morphological changes of the fault current waveform during propagation. Other methods can also be used in other embodiments, which are not limited here.

[0039] In specific implementation, the peak fault current of each potential fault point and the fault indication node can be extracted from the time-axis aligned transient fault current waveform data in the following manner: For the time-axis aligned transient fault current waveform data of each potential fault point and the fault indication node, a sliding window peak detection is used. For example, a sliding window with a length of 1 / 4 power frequency cycle is set, and the maximum absolute value of the current within the window is found in the first power frequency cycle after the fault occurs; the maximum value is interpolated at three points to obtain the accurate peak fault current; the peak fault current corresponding to each node is recorded, and its polarity direction is marked; for zero-sequence current, the absolute peak value is taken; for three-phase current, the peak value of each phase needs to be extracted separately and the maximum value is taken. Other methods can also be used in other embodiments, which are not limited here.

[0040] In specific implementation, the signal strength attenuation index between each potential fault point and the fault indication node can be determined based on all fault current peak values ​​in the following manner: using the fault current peak value of the fault indication node as the reference value, calculate the ratio of the fault current peak value of each potential fault point to the reference value; take the common logarithm of this ratio and multiply it by 20 to obtain the signal strength attenuation value in decibels; normalize this attenuation value to convert it into a value between 0 and 1 as the signal strength attenuation index; where the closer the attenuation index is to 0, the more severe the signal attenuation, and the closer it is to 1, the smaller the signal attenuation and the closer the electrical distance to the fault indication node. Other methods can also be used in other embodiments, which are not limited here.

[0041] It should be noted that the signal strength attenuation index in this application is a quantitative parameter used to quantify the degree of energy attenuation of a fault current signal during its propagation from a potential fault point to a fault indication node. Its core function is to reflect the relative magnitude of the electrical distance or signal transmission loss between the potential fault point and the fault indication node by measuring the attenuation of the signal strength. The closer the index value is to 1, the more slight the signal attenuation and the closer the electrical connection between the potential fault point and the indication node; conversely, the closer the value is to 0, the more severe the signal attenuation and the greater the electrical distance or path impedance between the two.

[0042] It should be noted that the above steps can extract high-precision transient waveform data from suspected fault areas and perform multi-dimensional feature analysis. By calculating waveform similarity to determine the origin of fault currents and calculating signal attenuation to assess electrical distance, the transient fault information can be fully utilized to improve the reliability and objectivity of preliminary screening and sorting of potential fault points.

[0043] In step 104, the fault propagation characteristics of each potential fault point are determined based on the edge weights of the weighted graph of the power supply network.

[0044] In some embodiments, determining the fault propagation characteristics of each potential fault point based on the edge weights of the power supply network weighted graph can be achieved using the following steps: Obtain the topology and edge weights of the weighted graph of the power supply network; For each potential fault point, taking the fault indication node as the signal receiving point and the potential fault point as the fault signal source point, the theoretical propagation path of the signal from the fault signal source point to the signal receiving point is calculated based on the topology and edge weights. The theoretical attenuation coefficient of the fault characteristic signal propagating along the theoretical propagation path is calculated based on the edge weights on the theoretical propagation path. The theoretical attenuation coefficient is then used as the fault propagation characteristic of the potential fault point, thereby obtaining the fault propagation characteristic of each potential fault point.

[0045] In a specific implementation, the topology of the power supply network weighted graph and the edge weights of each edge can be obtained in the following way: extract all nodes, all edges and the weight value corresponding to each edge contained in the power supply network weighted graph. The weight value directly reflects the impedance of the corresponding line. Other methods can also be used in other embodiments, which are not limited here.

[0046] In specific implementation, taking the fault indication node as the signal receiving point and the potential fault point as the fault signal source point, the theoretical propagation path of the signal from the fault signal source point to the signal receiving point can be calculated based on the topology and edge weights in the following way: calculate the minimum impedance path from each potential fault point to the fault indication node in the weighted graph of the power supply network, and take the minimum impedance path as the theoretical propagation path of the signal from the corresponding fault signal source point to the signal receiving point. The minimum impedance path consists of a series of continuous edges and nodes, and its total cost (i.e., total impedance) is the sum of the weights of all the edges passed through. As a preferred embodiment, the shortest path algorithm is used, with the edge weight (impedance) as the path cost, to calculate the minimum impedance path from each potential fault point to the fault indication node in the weighted graph. Other methods can also be used in other embodiments, which are not limited here.

[0047] In practice, the theoretical attenuation coefficient of the fault characteristic signal propagating along the theoretical propagation path can be calculated based on the edge weights on that path in the following way: For each theoretical propagation path, the total impedance value of the theoretical propagation path (i.e., the sum of all edge weights on the path) is substituted into the theoretical attenuation coefficient calculation formula to obtain the theoretical attenuation coefficient of the fault characteristic signal propagating along that path. The theoretical attenuation coefficient is calculated using the following formula: , The total impedance of the path. It is an attenuation factor related to the characteristic frequency of the power grid and the line parameters (usually determined to a certain value based on historical data or simulation). The calculated attenuation coefficient is a value between 0 and 1. The smaller the value, the more severe the attenuation of the signal along the path. Other methods can be used in other embodiments, which are not limited here.

[0048] It should be noted that the fault propagation characteristic in this application is a theoretical parameter calculated based on the power grid topology and line impedance parameters. It is used to characterize the theoretical attenuation characteristics of the fault characteristic signal as it propagates from a potential fault point along the power grid to the fault indication node. Its core function is to pre-evaluate and quantify the relative strength of the influence of fault points at different locations on the fault indication node from the theoretical model level, thereby providing a priori reference based on the physical structure of the power grid for judging the probability of a fault point. The higher the characteristic value, the easier (i.e., the smaller the attenuation) the fault signal generated by the potential fault point is to be detected by the fault indication node.

[0049] In step 105, the comprehensive fault value of each potential fault point is determined based on the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of each potential fault point, and then the final fault zone of the target downhole power supply area is located based on all the comprehensive fault values.

[0050] In some embodiments, determining the comprehensive fault value of each potential fault point based on its fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index can be achieved using the following steps: For each potential fault point, a corresponding weight coefficient is assigned to the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault point based on a preset feature weight allocation strategy. Based on the corresponding weight coefficients, the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault points are weighted and fused to obtain the comprehensive fault value of the potential fault points, and then the comprehensive fault value of each potential fault point is obtained.

[0051] In specific implementation, the assignment of corresponding weight coefficients to the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of potential fault points based on the preset feature weight allocation strategy can be achieved in the following ways. For example, the weight coefficients of each feature can be determined by the analytic hierarchy process (AHP). Specifically, this includes: constructing a feature importance judgment matrix, inviting relevant experts in the field to compare the pairwise importance of the three features—waveform similarity index, signal strength attenuation index, and fault propagation characteristics—and then calculating the eigenvector of the judgment matrix to obtain the initial weights of each feature. Finally, a consistency check is performed on the initial weights. If the check passes, the initial weights are used as the final weight coefficients. Among these, the waveform similarity index is usually assigned the highest weight (0.4-0.5), the signal strength attenuation index is assigned a medium weight (0.3-0.4), and the fault propagation characteristics are assigned a relatively low weight (0.1-0.2). Other methods can also be used in other embodiments, which are not limited here.

[0052] In specific implementation, the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault point are weighted and fused according to the corresponding weight coefficients to obtain the comprehensive fault value of the potential fault point. This can be achieved in the following way: the waveform similarity index value, signal strength attenuation index value, and fault propagation characteristic value of the potential fault point are multiplied by the corresponding weight coefficients, and then the three weighted values ​​are added together to obtain the comprehensive fault value of the potential fault point. The calculation formula is: Comprehensive fault value = (waveform similarity index × weight 1) + (signal strength attenuation index × weight 2) + (fault propagation characteristic × weight 3); where the comprehensive matching degree is a value between 0 and 1. The higher the value, the higher the feature matching degree between the point and the real fault point. Other methods can also be used in other embodiments, which are not limited here.

[0053] It should be noted that, in order to enhance the reliability of the results, this application may also introduce a confidence level verification mechanism: when the comprehensive failure value is greater than 0.7, it is considered that the point is very likely to be a failure point; when the comprehensive failure value is between 0.4 and 0.7, it is necessary to further analyze it in conjunction with other evidence; when the comprehensive failure value is less than 0.4, the possibility that the point is a failure point can be basically ruled out.

[0054] In addition, it should be noted that the comprehensive fault value in this application is a comprehensive evaluation value calculated by integrating multi-source and heterogeneous fault characteristic indicators. Its core function is to eliminate the randomness or one-sidedness that may exist in a single indicator, and form a unified quantitative standard that can comprehensively and evenly reflect the probability of whether each potential fault point is a real fault point. Through this comprehensive fault value, all candidate fault points can be prioritized, thereby ultimately serving the accurate location decision of the fault area.

[0055] It should be noted that determining the comprehensive fault value can achieve a deep integration of the theoretical propagation characteristics reflecting the physical structure of the power grid with the waveform and attenuation indicators reflecting actual monitoring data. Through weighted calculation, a comprehensive fault probability evaluation value is obtained. This allows for the comprehensive use of prior knowledge and real-time data, thereby improving the overall accuracy, anti-interference ability, and fault tolerance of the final fault location decision.

[0056] In some embodiments, locating the final fault zone of the target downhole power supply area based on all comprehensive fault values ​​can be achieved by the following steps: Sort the comprehensive fault values ​​of all potential fault points in descending order to generate a sequence of candidate fault points; Calculate the relative difference between the highest and second-highest comprehensive failure values; Based on the relative difference and the preset confidence threshold, the final fault area is determined from the candidate fault point sequence; Output the final fault location information and confidence level.

[0057] In specific implementation, the comprehensive fault values ​​of all potential fault points are sorted in descending order to generate a candidate fault point sequence. This can be achieved in the following way: the comprehensive fault values ​​of all potential fault points in the suspected fault area are sorted in descending order; the sorted potential fault points, their corresponding comprehensive fault values, and line identification information are combined to form a candidate fault point sequence; each element in the sequence contains three fields: potential fault point number, line segment identifier, and comprehensive fault value; the candidate point ranked first in the candidate fault point sequence is the most likely fault point. Other methods can also be used in other embodiments, which are not limited here.

[0058] In specific implementation, the relative difference between the highest and second-highest comprehensive fault values ​​can be calculated as follows: extract the comprehensive fault values ​​of the top two candidate fault points from the candidate fault point sequence, and denot them as the first comprehensive fault value and the second comprehensive fault value, respectively; calculate the relative difference between the two candidate fault points = (first comprehensive fault value - second comprehensive fault value) / first comprehensive fault value × 100%; the relative difference is a percentage value, and the larger the value, the more obvious the advantage of the first-ranked candidate point is over other candidate points, and the higher the certainty of fault location; at the same time, calculate the absolute difference between the first comprehensive fault value and the second comprehensive fault value as an auxiliary judgment basis. Other methods can also be used in other embodiments, which are not limited here.

[0059] In specific implementation, the final fault area can be determined from the candidate fault point sequence based on the relative difference and the preset confidence threshold in the following manner: A first confidence threshold (e.g., 40%) and a second confidence threshold (e.g., 20%) are set; when the relative difference is greater than or equal to the first confidence threshold, the line segment where the first-ranked candidate fault point is located is directly determined as the final fault area; when the second confidence threshold is less than or equal to the relative difference and less than the first confidence threshold, a verification mechanism is initiated: detailed fault waveform data of the top three candidate points are retrieved, waveform detail features are compared, and the line segment where the candidate point with the earliest arrival time of the traveling wave front and the most severe waveform distortion is selected as the final fault area; when the relative difference is less than the second confidence threshold, the fault location result is determined to be ambiguous, the information of the top three candidate points is output, and manual intervention is required for analysis. Other methods can also be used in other embodiments, which are not limited here.

[0060] In specific implementation, the location information and confidence level of the final fault area can be output in the following way: generate complete location information including the fault line number, the start and end points of the fault section, the location time, the comprehensive fault value, the relative difference degree, and the confidence level based on the determined final fault area; and mark the fault section with a prominent color through the human-machine interface of the monitoring system, while issuing an audible and visual alarm; wherein, the confidence level is divided into three levels: high, medium, and low, according to the magnitude of the relative difference degree. Other methods can also be used in other embodiments, which are not limited here.

[0061] In another aspect, in some embodiments, this application provides an electrical fault detection system, which includes an electrical fault location unit, with reference to... Figure 4 The figure is a schematic diagram of the structure of an electrical fault location unit according to some embodiments of this application. The electrical fault location unit includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to deploy intelligent monitoring sensors at each node of the target downhole power supply network, and then monitor the electrical quantities of the corresponding nodes in the power supply network in real time through each intelligent monitoring sensor. Processing module 402 in this application is mainly used to mark a node as a fault indication node when the electrical quantity monitored by any node meets the predetermined fault triggering conditions, and then determine the suspected fault area of ​​the fault indication node in the power supply network based on the fault indication node and the power supply network weighted diagram. The processing module 402 described in this application is also used to retrieve the transient waveform data of the fault current of all potential fault points in the suspected fault area, and to calculate the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node. The processing module 402 described in this application is further configured to determine the fault propagation characteristics of each potential fault point based on the edge weights of the weighted graph of the power supply network; The execution module 403 in this application is mainly used to determine the comprehensive fault value of each potential fault point based on the fault propagation characteristics, corresponding waveform similarity index and signal strength attenuation index of each potential fault point, and then locate the final fault area of ​​the target underground power supply area based on all the comprehensive fault values.

[0062] Each module in the aforementioned electrical fault location unit can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0063] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores regionally coordinated electrical fault location data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a regionally coordinated electrical fault location method.

[0064] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0065] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described embodiment of the regional collaborative electrical fault location method.

[0066] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps described in the embodiment of the regional collaborative electrical fault location method.

[0067] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the embodiment of the regionally coordinated electrical fault location method.

[0068] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0069] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible 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.

[0070] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for locating electrical faults based on regional cooperation, wherein, A weighted power supply network map is pre-constructed based on the network topology of the target underground power supply area. The method is characterized by the following steps: Intelligent monitoring sensors are deployed at each node of the target underground power supply network, and the electrical quantities of the corresponding nodes in the power supply network are monitored in real time through each intelligent monitoring sensor. When any node detects an electrical quantity that meets a predetermined fault triggering condition, the node is marked as a fault indication node. Then, based on the fault indication node and the power supply network weighted graph, the suspected fault area of ​​the fault indication node in the power supply network is determined. Retrieve transient waveform data of fault current at all potential fault points within the suspected fault area, and calculate waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node; The fault propagation characteristics of each potential fault point are determined based on the edge weights of the weighted graph of the power supply network. Based on the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of each potential fault point, the comprehensive fault value of each potential fault point is determined, and then the final fault zone of the target underground power supply area is located based on all the comprehensive fault values.

2. The method as described in claim 1, characterized in that, Determining the suspected fault area of ​​the fault indicator node in the power supply network based on the fault indicator node and the weighted graph of the power supply network specifically includes: Based on the real-time current flow direction of the fault indicator node, determine multiple downstream associated nodes of the fault indicator node in the power supply network; The electrical distance between the fault indication node and each downstream associated node is determined based on the weighted graph of the power supply network. Obtain the preset electrical distance threshold; The electrical distance threshold is compared with the electrical distance of each downstream associated node to generate the suspected fault area of ​​the fault indication node in the power supply network.

3. The method as described in claim 1, characterized in that, Retrieving transient fault current recording data from all potential fault points within the suspected fault area specifically includes: Based on the suspected fault area and the power supply network weighted map, multiple potential fault points within the suspected fault area in the power supply network weighted map are screened. Acquire transient waveform data of fault current at each potential fault point.

4. The method as described in claim 1, characterized in that, The calculation of waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node specifically includes: The fault current transient waveform data of the fault indication node and each potential fault point are aligned with the time axis. Based on the time-axis aligned transient waveform data of the fault current, calculate the waveform similarity index between each potential fault point and the fault indication node. Extract the peak fault current of each potential fault point and the fault indication node from the time-axis aligned transient fault current waveform data; The signal strength attenuation index between each potential fault point and the fault indication node is determined based on all fault current peak values.

5. The method as described in claim 1, characterized in that, Determining the fault propagation characteristics of each potential fault point based on the edge weights of the weighted graph of the power supply network specifically includes: Obtain the topology and edge weights of the weighted graph of the power supply network; For each potential fault point, taking the fault indication node as the signal receiving point and the potential fault point as the fault signal source point, the theoretical propagation path of the signal from the fault signal source point to the signal receiving point is calculated based on the topology and edge weights. The theoretical attenuation coefficient of the fault characteristic signal propagating along the theoretical propagation path is calculated based on the edge weights on the theoretical propagation path. The theoretical attenuation coefficient is then used as the fault propagation characteristic of the potential fault point, thereby obtaining the fault propagation characteristic of each potential fault point.

6. The method as described in claim 1, characterized in that, The comprehensive fault value for each potential fault point is determined based on its fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index. Specifically, this includes: For each potential fault point, a corresponding weight coefficient is assigned to the fault propagation characteristics, corresponding waveform similarity index and signal strength attenuation index of the potential fault point based on a preset feature weight allocation strategy. Based on the corresponding weight coefficients, the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of the potential fault points are weighted and fused to obtain the comprehensive fault value of the potential fault points, and then the comprehensive fault value of each potential fault point is obtained.

7. The method as described in claim 1, characterized in that, Based on all the comprehensive fault values, the final fault zone of the target downhole power supply area is located, specifically including: Sort the comprehensive fault values ​​of all potential fault points in descending order to generate a sequence of candidate fault points; Calculate the relative difference between the highest and second-highest comprehensive failure values; Based on the relative difference and the preset confidence threshold, the final fault area is determined from the candidate fault point sequence; Output the final fault location information and confidence level.

8. An electrical fault detection system, comprising an electrical fault location unit, characterized in that, The electrical fault location unit includes: The data acquisition module is used to deploy intelligent monitoring sensors at each node of the target downhole power supply network, and then monitor the electrical quantities of the corresponding nodes in the power supply network in real time through each intelligent monitoring sensor. The processing module is used to mark any node as a fault indication node when the electrical quantity detected by any node meets the predetermined fault triggering conditions, and then determine the suspected fault area of ​​the fault indication node in the power supply network based on the fault indication node and the power supply network weighted graph. The processing module is also used to retrieve transient waveform data of fault current at all potential fault points in the suspected fault area, and to calculate the waveform similarity index and signal strength attenuation index between each potential fault point and the fault indication node. The processing module is also used to determine the fault propagation characteristics of each potential fault point based on the edge weights of the weighted graph of the power supply network. The execution module is used to determine the comprehensive fault value of each potential fault point based on the fault propagation characteristics, corresponding waveform similarity index, and signal strength attenuation index of each potential fault point, and then locate the final fault area of ​​the target downhole power supply area based on all the comprehensive fault values.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the regional collaborative electrical fault location method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the regional collaborative electrical fault location method as described in any one of claims 1 to 7.

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