Traveling wave fault positioning method and device for multi-branch hybrid line, and medium

Abstract

The invention discloses a traveling wave fault positioning method and device for a multi-branch hybrid line, and a medium, and relates to the technical field of electrical variable measurement. The method comprises the following steps: acquiring a high-frequency transient signal generated in the hybrid line based on a traveling wave synchronous monitoring device deployed on a monitoring node in the hybrid line, and extracting a signal feature corresponding to the high-frequency transient signal; according to the signal characteristics and the signal energy of the high-frequency transient signal, performing fault pre-identification on the hybrid line to determine a pre-fault event generated in the hybrid line and a pre-fault section corresponding to the pre-fault event; when it is detected that the signal characteristics exceed a preset fault starting threshold value, it is determined that the hybrid line generates traveling waves, and wave head arrival time corresponding to traveling wave signals received by the monitoring nodes is obtained; according to the line topological relation of the mixed line and the sequence of the wave head arrival time corresponding to different monitoring nodes, fault branches generating traveling waves in the pre-fault section are determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of measuring electrical variables, and in particular to a traveling wave fault location method for a multi-branch hybrid line, a device and a medium. BACKGROUND

[0002] With the expansion of urban power grid scale and the improvement of power supply reliability, multi-branch and hybrid line structures are increasingly common in distribution networks. The topology of the multi-branch and hybrid line structure is complex, there are many T-junctions, and the power flow direction may change with the load, which brings great challenges to fault monitoring and positioning.

[0003] Existing fault location methods mainly rely on impedance methods or traditional traveling wave methods for fault location. The impedance method calculates the line impedance by measuring the voltage and current after the fault, and then calculates the fault distance. This method is susceptible to factors such as transition resistance, system operating mode, and inaccurate line parameters. For multi-branch lines, it is not possible to directly determine which specific branch the fault occurs in. The traveling wave method uses the traveling wave signal generated at the moment of fault to locate the fault. The time stamp of the traveling wave arriving at the measurement point is used for calculation. If a single-ended traveling wave method is used, the initial traveling wave from the fault point and the reflected traveling wave from the opposite side need to be identified. In a multi-branch line, due to the existence of a large number of T-junctions, extremely complex refraction and reflection waves will be generated, making it difficult to accurately distinguish the initial wave front and the reflected wave front, and the positioning reliability is poor. Although the double-ended traveling wave method has high accuracy, it requires the deployment of synchronous monitoring devices at both ends of the line, which is costly and has insufficient coverage for multi-branch structures. In addition, existing methods focus on post-fault positioning and lack early warning capabilities for early fault signs, making it impossible to transition from passive repair to active maintenance. SUMMARY

[0004] To solve the above problems, the present application proposes a traveling wave fault location method for a multi-branch hybrid line, comprising: Based on the traveling wave synchronous monitoring device deployed on the monitoring node in the hybrid line, the high-frequency transient signal generated in the hybrid line is collected, and the signal features corresponding to the high-frequency transient signal are extracted; According to the signal features and the signal energy of the high-frequency transient signal, the hybrid line is pre-identified for fault to determine the pre-fault event generated in the hybrid line and the pre-fault section corresponding to the pre-fault event; In the case where the signal features exceed the preset fault starting threshold, it is determined that the hybrid line generates a traveling wave, and the wave head arrival time corresponding to the traveling wave signal received by the monitoring node is obtained; According to the line topology relationship of the hybrid line and the sequence of the wave head arrival time corresponding to different monitoring nodes, the fault branch in the pre-fault section that generates the traveling wave is determined.

[0005] In an implementation manner of the present application, the fault branch in the pre-fault section generating the traveling wave is determined according to the line topological relationship of the mixed line and the sequence of the wave head arrival time corresponding to different monitoring nodes, and specifically includes: From the monitoring nodes receiving the traveling wave signals, a target monitoring node with the earliest wave arrival time and an adjacent monitoring node of the target monitoring node are selected; wherein the adjacent monitoring node includes at least one or more of the following: an upstream main line monitoring node, a downstream main line monitoring node and a branch monitoring node; According to the line topological relationship of the mixed line, the node distance between the adjacent monitoring node and the target monitoring node is determined; According to the sequence of the wave head arrival time in the target monitoring node and the adjacent monitoring node, the propagation path of the traveling wave signal is analyzed according to the node distance; The fault branch in the pre-fault section generating the traveling wave is determined through the propagation path.

[0006] In an implementation manner of the present application, the fault pre-identification of the mixed line is performed according to the signal feature and the signal energy of the high-frequency transient signal, to determine the pre-fault event generated in the mixed line and the pre-fault section corresponding to the pre-fault event, and specifically includes: The fault pre-identification of the mixed line is performed to match the signal feature with a pre-set early warning signal feature library, to determine the similarity between the signal feature and each early warning signal feature in the early warning signal feature library; wherein the early warning signal feature includes amplitude, polarity, steepness and energy distribution; In the case that the similarity exceeds a pre-set threshold and the signal energy of the high-frequency transient signal is less than a pre-set multiple of the real fault signal energy threshold, it is determined that a pre-fault event is generated in the mixed line; A specified monitoring node in the monitoring nodes acquiring the high-frequency transient signal is determined; According to the receiving time of the high-frequency transient signal received by the specified monitoring node, the pre-fault section in the mixed line generating the pre-fault event is obtained by inversion.

[0007] In an implementation manner of the present application, the pre-fault section in the mixed line generating the pre-fault event is obtained by inversion according to the receiving time of the high-frequency transient signal received by the specified monitoring node, and specifically includes: The mixed line is divided into a plurality of candidate hidden danger points according to a fixed distance; wherein each line section is provided with a candidate hidden danger point; calculating, based on the shortest propagation path of the candidate hidden point to each designated monitoring node, a theoretical arrival time of the high-frequency transient signal to each designated monitoring node when the candidate hidden point generates the high-frequency transient signal; selecting, according to an error value between a receiving time of the high-frequency transient signal and the theoretical arrival time, a line section in which the candidate hidden point with the error value less than a preset error is located as a pre-fault section in which the pre-fault event is generated in the mixed line.

[0008] In an implementation manner of the present application, after determining the fault branch in which the traveling wave is generated in the pre-fault section, the method further comprises: determining a branch line length of the fault branch and wave head arrival times corresponding to two ends of the fault branch respectively; obtaining a propagation speed of the traveling wave, and calculating a deviation distance between a midpoint and a fault point of the fault branch according to a difference between the wave head arrival times of the two ends and the propagation speed; determining distances between the fault point and the two ends of the fault branch according to the deviation distance and an average of the branch line length.

[0009] In an implementation manner of the present application, before collecting the high-frequency transient signal generated in the mixed line, the method further comprises: obtaining topology structure data of the mixed line; pre-selecting key nodes in the mixed line based on the topology structure data; wherein the key nodes at least include a line head end, a tail end and all T-branch points; performing dead zone analysis on the pre-selected key nodes to identify a dead zone section which cannot be located by all traveling wave synchronous monitoring devices; adding a supplementary node to the dead zone section, and determining monitoring nodes of the traveling wave synchronous monitoring devices to be deployed on the mixed line according to positions of the supplementary node and the key nodes.

[0010] In an implementation manner of the present application, after determining the monitoring nodes of the traveling wave synchronous monitoring devices to be deployed on the mixed line according to the positions of the supplementary node and the key nodes, the method further comprises: determining a coverage degree of the monitoring nodes on the mixed line; wherein the coverage degree refers to a proportion of a line which can be located by at least two traveling wave synchronous monitoring devices in the mixed line; in a case that the coverage degree is greater than a preset coverage threshold and a number of the monitoring nodes exceeds a preset upper limit value, the monitoring nodes are removed in ascending order of contribution degrees of the monitoring nodes to the coverage degree until the monitoring nodes after removal satisfy the preset upper limit value.

[0011] In one implementation of this application, the fault initiation threshold corresponding to the signal feature is lower than the fault feature threshold corresponding to the signal feature in the case of a fault in the hybrid line.

[0012] This application provides a traveling wave fault location device for a multi-branch hybrid line, the device comprising: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a traveling wave fault location method for a multi-branch hybrid line as described in any of the preceding claims.

[0013] This application provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows: A traveling wave fault location method for a multi-branch hybrid line as described in any of the preceding items.

[0014] The traveling wave fault location method for multi-branch hybrid lines proposed in this application can bring the following beneficial effects: By deploying traveling wave synchronous monitoring devices at multiple nodes, a monitoring network covering the entire line was formed, solving the problems of insufficient coverage and branch identification difficulties caused by insufficient devices in the double-ended traveling wave method. Secondly, by analyzing the signal characteristics and energy of high-frequency transient signals, it is possible to distinguish between weak pre-fault events and high-energy traveling wave signals. This allows for the identification of pre-fault events and preliminary location of pre-fault sections before permanent faults occur, achieving early warning of early line faults. When a fault occurs, logical reasoning based on the arrival time sequence of wavefronts at multiple nodes and the line topology is used to accurately locate the faulty branch, effectively improving the accuracy of fault location. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a traveling wave fault location method for a multi-branch hybrid line provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a traveling wave fault location device for a multi-branch hybrid line provided in an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0018] like Figure 1 As shown in the embodiment of this application, a traveling wave fault location method for a multi-branch hybrid line includes: S101: Based on the traveling wave synchronous monitoring device deployed on the monitoring node in the hybrid line, the high-frequency transient signals generated in the hybrid line are collected and the signal features corresponding to the high-frequency transient signals are extracted.

[0019] A hybrid line refers to a complex power distribution network composed of lines of different types (such as a mixture of overhead lines and cables) or lines of different voltage levels. In actual power systems, when a short circuit or ground fault occurs at a point on a line, a high-energy transient traveling wave is generated. To capture the abnormal signal, a traveling wave synchronous monitoring device is usually installed on the hybrid line to detect potential faults. Unlike traditional traveling wave fault detection methods, this embodiment does not install monitoring devices at a single or both ends of the line, but rather deploys traveling wave synchronous monitoring devices at multiple monitoring nodes on the hybrid line. These monitoring nodes can be the beginning or end of the line, T-junction points, major load points, etc. Setting up multiple monitoring nodes in the hybrid line allows the deployed traveling wave synchronous monitoring devices to cover multiple branch lines and multiple nodes. By collecting high-frequency transient signals from each monitoring node, it adapts to the complex hybrid power grid structure. Compared to the traditional two-end traveling wave method, which only collects signals from both ends of the line, the signal acquisition range is wider, enabling multi-point early warning and improving the ability to identify potential faults.

[0020] High-frequency transient signals refer to electromagnetic signals generated during the operation of power lines due to rapid and transient changes in the energy of the electric or magnetic fields caused by certain events. Identifying high-frequency transient signals can help determine whether a substantial fault has occurred in a mixed power line. Based on their source and energy level, high-frequency transient signals mainly include two types: one is a weak partial discharge signal caused by early insulation defects such as insulator deterioration or tree discharge; the other is a high-energy traveling wave signal generated when a permanent fault such as a short circuit or grounding occurs. After acquiring high-frequency transient signals using a traveling wave synchronous monitoring device, the signals need to be denoised, and the corresponding signal characteristics need to be extracted. These characteristics include amplitude, polarity, steepness, and energy distribution. By analyzing the signal energy and characteristics corresponding to the signal features, it is possible to identify potential faults in the line when weak transient signals (i.e., partial discharge signals) are detected, and to preliminarily locate the line section that may be faulty. When a traveling wave signal is captured, the fault location can be accurately located by combining it with the previously preliminarily located fault section. This not only helps to identify and preliminarily locate the fault section in the early stage of a fault, thus aiding in the prevention of line faults, but also allows for rapid location of the fault within the identified fault section when a real fault occurs, based on the early warning information. This greatly shortens the fault investigation and repair time and improves operation and maintenance efficiency.

[0021] In one embodiment, the traveling wave synchronization monitoring device needs to be deployed at multiple monitoring nodes in the mixed line. The selection of monitoring nodes needs to be adapted to the characteristics of different lines, prioritizing the deployment of the traveling wave synchronization monitoring device at key locations, such as the beginning and end of the line, T-junction branch points, and major load nodes, to ensure comprehensive coverage of all branches of the mixed line. Therefore, in order to determine the optimal location for deploying a limited number of monitoring devices on complex lines, an optimal balance between cost and performance needs to be achieved.

[0022] Specifically, topology data of the hybrid power lines is acquired. Topology data is a model describing the electrical connections of the power lines, including the length of line segments, electrical parameters, number of branches, and connections between nodes (such as substations, towers, and branch boxes). Based on this topology data, key nodes on the line are first pre-selected. These key nodes need to cover all major power flow directions and branch origins, including at least the beginning and end of the line and all T-junctions. However, the pre-selected key nodes may have monitoring blind spots; therefore, dead zone analysis is also required. A dead zone segment refers to a line segment where, when a fault occurs, due to the symmetry of the traveling wave propagation path or defects in the monitoring point layout, the time difference between the initial traveling wave received by different monitoring points is zero or indistinguishable, making it impossible to uniquely determine the fault location. For example, in the middle region of a line segment with a long distance between two adjacent monitoring nodes, the time it takes for the fault traveling wave to reach these two nodes is almost the same, making it difficult to determine which end the fault is closer to. To address this issue, supplementary nodes need to be added to the dead zone sections. Specifically, within the identified dead zone sections, one or more new locations are selected as supplementary nodes. Finally, all pre-selected key nodes and the supplementary nodes determined after analysis are merged to determine the monitoring nodes on the hybrid line where traveling wave synchronization monitoring devices need to be deployed, thus forming a blind-zone-free monitoring node deployment scheme.

[0023] Traditional dual-end methods deploy synchronization devices only at both ends of the line. In complex multi-branch lines, deploying monitoring devices only at both ends becomes completely ineffective because branches cannot be located. To solve the branch location problem, an independent dual-end detection system needs to be configured for each branch, leading to a sharp increase in total cost and system complexity. In contrast, the embodiments of this application deploy monitoring devices at multiple nodes. This networked deployment method divides the complex multi-branch line into multiple monitorable segments, eliminating the need to configure an independent dual-end system for each individual branch. This achieves full line coverage while avoiding an increase in the number of devices, effectively controlling deployment costs.

[0024] In one embodiment, after initially formulating a monitoring node deployment plan, considering deployment costs, the number of monitoring nodes needs to be optimized to maximize economic benefits while ensuring coverage detection for each line segment. When optimizing the deployment plan, the ideality of the plan needs to be evaluated using the coverage metric. Coverage refers to the proportion of mixed lines that can be located by at least two traveling wave synchronous monitoring devices. Therefore, by traversing the coverage of each location in the mixed lines by the traveling wave synchronous monitoring devices, the coverage of the monitoring nodes in the current deployment plan can be determined. It is then determined whether the coverage exceeds a preset coverage threshold. If it does, it indicates that the monitoring performance of the currently set monitoring nodes has met the standard. However, if the number of monitoring nodes exceeds a preset upper limit, the current deployment plan may result in performance overkill and resource waste, requiring a reduction in the number of monitoring nodes.

[0025] When streamlining monitoring nodes, nodes are not deleted arbitrarily. Instead, they are removed sequentially based on their importance. Specifically, the contribution of each monitoring node to overall coverage is assessed. This is calculated by recalculating the coverage of the node after its removal; the decrease in coverage is the measure of that node's contribution. Monitoring nodes with the least impact on global positioning capabilities are removed in ascending order of contribution. After each node is removed, the remaining number of nodes and the current coverage are checked in real time. This optimization process continues until the number of monitoring nodes is reduced to within a preset upper limit. Through this dynamic optimization, the lowest-cost deployment scheme with the fewest nodes can be found while strictly meeting predetermined positioning performance requirements, maximizing economic benefits.

[0026] S102: Based on signal characteristics and the signal energy of high-frequency transient signals, perform fault pre-identification on the hybrid line to determine the pre-fault events generated in the hybrid line and the pre-fault sections corresponding to the pre-fault events.

[0027] After extracting the signal characteristics of high-frequency transient signals, fault pre-identification can be performed on hybrid lines based on these characteristics and signal energy. Fault pre-identification refers to the early detection of potential fault hazards by analyzing the characteristics of high-frequency transient signals before a fault develops into a real fault. These potential fault hazards are called pre-fault events, which are events that may present potential fault hazards but have not yet resulted in a substantial fault. By identifying whether hybrid lines are likely to experience pre-fault events, the pre-fault sections where such events may occur can be preliminarily located, enabling appropriate preventative measures to be taken in the early stages of a fault to prevent its further expansion and deterioration.

[0028] In one embodiment, before performing pre-fault identification, a typical early warning signal feature library needs to be established through experiments or simulations. This library contains traveling wave waveform features of partial discharge signals caused by insulator deterioration, tree discharge, and hardware loosening. The extracted signal features are matched against the pre-defined early warning signal feature library. By calculating the similarity between the extracted features and the features in the library, the presence of potential fault hazards in the mixed line is determined. The early warning signal feature library contains signal features of various known fault types, such as amplitude, polarity, steepness, and energy distribution. When the similarity of multiple signal features exceeds a preset threshold, or the weighted average similarity of multiple features is greater than a preset threshold, an anomaly is preliminarily determined to exist in the line. Multi-feature joint judgment can effectively improve the accuracy and reliability of pre-fault identification, avoiding erroneous early warnings caused by misjudgments based on a single feature. After initially determining that there was an anomaly in the line, the signal energy of the high-frequency transient signal was further determined. Since the traveling wave signal energy generated by a real permanent fault is huge, while the partial discharge signal energy generated by early insulation defects is weak, if the signal energy of the high-frequency transient signal is less than a preset multiple of the energy threshold of the real fault signal, it indicates that the signal fault characteristics are obvious but the energy value is far below the fault threshold. At this time, due to insufficient signal energy, it can be determined that no substantial fault has occurred in the hybrid line. Finally, this event was determined to be a pre-fault event, that is, a potential fault that has not yet caused a substantial short circuit or ground fault, but indicates the deterioration of the insulation condition.

[0029] When a potential fault occurs in a hybrid line, multiple monitoring nodes can detect abnormal signals. Therefore, after identifying the pre-fault event, it is first necessary to determine the specific monitoring node that acquired the high-frequency transient signal. Then, by using the arrival timestamp of the high-frequency transient signal recorded by the specified monitoring node and the reception time of the signal propagation in space, the approximate range of the potential fault source can be preliminarily calculated in complex multi-branch hybrid lines. This allows for the inversion of the pre-fault section that caused the pre-fault event, timely capture and identification of weak signals generated by early defects, and a shift from post-event processing to pre-event prevention. This provides maintenance personnel with early warning reports containing specific location information, guiding them to conduct precise inspections and repairs.

[0030] In one embodiment, this application uses the signal reception time and theoretical arrival time of a high-frequency transient signal to perform spatiotemporal inversion to determine the specific location of the pre-fault section. In power line fault analysis, candidate potential hazard points are virtual sets of spatial locations where faults may occur or potential hazard signals may be generated. The topology of the entire hybrid line is discretized at fixed intervals, such as 10 meters or 50 meters, thereby generating a large number of uniformly distributed virtual points. Each such virtual point is a candidate potential hazard point. By dividing the potential hazard points, the problem of locating continuous lines can be transformed into a problem of screening a finite number of discrete points, effectively reducing the difficulty of fault location.

[0031] After establishing a set of candidate potential hazards, the shortest propagation path from each candidate hazard to each designated monitoring node (i.e., the actual physical location where a traveling wave synchronous monitoring device is deployed) is determined based on the line topology of the mixed lines. The shortest propagation path refers to the path with the shortest electrical distance for a high-frequency transient signal to travel from the candidate hazard to the monitoring node. Since the propagation speed of traveling waves in different types of lines is known (overhead lines approach the speed of light, while cables are about half the speed of light), the theoretical arrival time of the high-frequency transient signal generated by any candidate hazard at each designated monitoring node can be calculated based on the shortest propagation path length and wave speed. The theoretical arrival times of all designated monitoring nodes constitute the theoretical arrival time sequence for that candidate hazard.

[0032] After acquiring the high-frequency transient signal generated by the actual pre-fault event on the mixed line, the actual reception time of this high-frequency transient signal arriving at each designated monitoring node is determined. The reception times of all designated monitoring nodes constitute a reception time series. This reception time series is then compared one by one with the theoretical arrival time series of all candidate potential fault points. The comparison metric is the error value, typically the root mean square error of the difference between the two time series. After obtaining the comparison results, the point that best matches the theoretical time series with the actual reception time series among all candidate potential fault points needs to be identified, i.e., the candidate potential fault point whose error value is less than the preset error. Finally, the line section where this selected candidate potential fault point is located is determined as the pre-fault section, thus achieving spatial localization of potential fault sources such as insulation defects.

[0033] After detecting high-frequency transient signals, it is only necessary to quickly match the signal reception time recorded by each designated monitoring node with the pre-stored theoretical arrival time database. By finding the candidate hidden danger point with the smallest error, the most likely pre-fault section of the hidden danger source can be directly determined. This transforms the complex fault signal propagation analysis problem into a numerical matching problem, which can effectively adapt to the complex topology of multi-branch mixed lines and overcome the problem of fuzzy positioning near branch points in traditional methods.

[0034] S103: If the detected signal characteristics exceed the preset fault initiation threshold, determine that the hybrid line generates a traveling wave, and obtain the wavefront arrival time corresponding to the traveling wave signal received by the monitoring node.

[0035] The above process describes how to locate a pre-fault section when a partial discharge signal is generated in a hybrid line. When the pre-fault event develops into a substantial fault, the fault point will generate a powerful abrupt traveling wave. The criterion for determining whether a traveling wave has been generated in the hybrid line is whether the signal characteristics exceed a preset fault initiation threshold. The fault initiation threshold is a critical value for signal characteristics used to determine whether a substantial fault has occurred in the hybrid line, and it needs to be lower than the fault characteristic threshold corresponding to the signal characteristics of the hybrid line under fault conditions. This is because in the early stages of a fault, the signal characteristics may not have reached the fault characteristic threshold, but have already significantly exceeded the normal range. If the fault characteristic threshold is used as the initiation condition at this time, the best opportunity for fault location will be missed. Therefore, the preset fault initiation threshold should be set between the normal signal characteristics and the fault characteristic threshold to ensure that the location process can be triggered in the early stages of the fault. When the high-frequency transient signal characteristics collected by the monitoring nodes exceed the preset fault initiation threshold, it can be determined that a traveling wave has been generated in the hybrid line. At this time, the arrival time of the traveling wave signal wavefront received by each monitoring node must be recorded immediately. The wavefront arrival time is the moment when the traveling wave signal first arrives at the monitoring node. Based on the wavefront arrival time and the previously detected pre-fault sections that may cause faults, the actual fault point is quickly located.

[0036] S104: Based on the line topology of the hybrid line and the order of wavefront arrival times corresponding to different monitoring nodes, determine the fault branch that generates traveling waves in the pre-fault section.

[0037] By collecting the wavefront arrival times (WAT) data from all monitoring nodes and analyzing the hybrid line topology, the connection relationships between different monitoring nodes and the signal propagation path can be determined. Since traveling waves propagate along the line's topology in a hybrid line, the order of WAT arrival times received by different monitoring nodes reflects the signal propagation path and direction. By analyzing the order of WAT arrival times corresponding to different monitoring nodes, the fault branch generating the traveling wave in the pre-fault section can be identified.

[0038] In one embodiment, the line segment where the first and second monitoring nodes to detect the traveling wave signal are located is the main trunk or branch where the fault occurs. First, from the monitoring nodes that received the traveling wave signal, the target monitoring node with the earliest arrival time and its adjacent monitoring nodes are selected. Adjacent monitoring nodes include at least one or more of the following: upstream main trunk monitoring nodes, downstream main trunk monitoring nodes, and branch monitoring nodes. Based on the line topology of the mixed line, the node distance between the adjacent monitoring nodes and the target monitoring node is determined, i.e., the electrical length of the line, which is the basis for propagation time estimation. Then, the arrival time order of the wavefront of the target monitoring node and each of its adjacent monitoring nodes is compared. According to the traveling wave propagation principle, the signal propagates to all branches after passing through each T-junction during propagation; therefore, the fault point must be located within the line area covered by the target monitoring node. By comparing the time sequence and node distances, the direction of signal origin can be deduced. For example, if the fault occurs on a branch, the traveling wave will first reach the monitoring node at the end of that branch before propagating to adjacent nodes on the main trunk. If the fault occurs on the downstream trunk line of the target node, then the downstream neighboring node will receive the signal earlier than the upstream neighboring node. Through this timing logic reasoning, it is possible to clearly analyze the direction from which the travel wave signal propagates to the target node, thereby determining the propagation path of the faulty branch.

[0039] Traditional two-end methods rely on the time difference between the two ends for calculation. Once the number of branches increases and the signal propagation path becomes complex, this method fails. However, the embodiments of this application are based on the wavefront arrival time sorting of the entire monitoring network. By comparing the order in which multiple monitoring nodes detect the initial traveling wave and combining it with the known line topology, the specific branch where the fault occurred can be uniquely inferred. This transforms the location problem from two-point ranging to full-network time sequence analysis, perfectly adapting to the multi-branch structure of hybrid lines.

[0040] In one embodiment, after identifying the faulty branch, it is also necessary to accurately locate the fault point to provide maintenance personnel with accurate maintenance recommendations. Specifically, assuming the fault point is located on a faulty branch between monitoring node M and monitoring node N, the branch line length L of the faulty branch is determined, as well as the arrival times of the wavefronts at both ends of the faulty branch. and To determine the distance between the midpoint and the fault point of the faulty branch, obtain the propagation speed V of the traveling wave. Then, based on the difference in arrival times of the wavefronts at both ends and the propagation speed, calculate the deviation distance between the midpoint and the fault point of the faulty branch. Finally, based on the average deviation distance and the branch length, determine the distance between the fault point and both ends of the faulty branch. The above calculation process can be expressed by the following formula:

[0041] In the above formula, the deviation distance between the midpoint of the faulty branch and the faulty point is expressed as: The final calculated This represents the distance from monitoring node M. To calculate the distance between the fault point and monitoring node N, use the formula above... and Simply change the calculation order.

[0042] The fault location method based on traveling wave propagation time is unaffected by transition resistance, system impedance, load fluctuations, and fault type, effectively improving location accuracy. Furthermore, the physical phenomenon of traveling waves propagating from the fault point to both sides is independent of the power frequency flow direction in the line; regardless of changes in the line power flow, the fault location logic and accuracy remain unaffected, demonstrating strong adaptability.

[0043] The above are embodiments of the methods proposed in this application. Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0044] Figure 2 This is a schematic diagram of the structure of a traveling wave fault location device for a multi-branch hybrid line, provided as an embodiment of this application. Figure 2 As shown, it includes: At least one processor; and, At least one processor-communication-connected memory; wherein, The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to: perform a traveling wave fault location method for a multi-branch hybrid circuit as described in any of the preceding claims.

[0045] This application provides a non-volatile computer storage medium storing computer-executable instructions, which are configured as follows: A traveling wave fault location method for a multi-branch hybrid line as described in any of the preceding items.

[0046] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0047] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0048] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0050] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0052] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0053] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0054] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for locating traveling wave faults in a multi-branch hybrid line, characterized in that, The method includes: Based on the traveling wave synchronous monitoring device deployed on the monitoring node in the hybrid line, high-frequency transient signals generated in the hybrid line are collected, and the signal features corresponding to the high-frequency transient signals are extracted. Based on the signal characteristics and the signal energy of the high-frequency transient signal, the hybrid line is pre-identified to determine the pre-fault events generated in the hybrid line and the pre-fault sections corresponding to the pre-fault events. If the signal characteristics are detected to exceed a preset fault initiation threshold, it is determined that the hybrid line generates a traveling wave, and the arrival time of the wavefront corresponding to the traveling wave signal received by the monitoring node is obtained. Based on the line topology of the hybrid line and the order of wavefront arrival times corresponding to different monitoring nodes, the fault branch that generates the traveling wave in the pre-fault section is determined.

2. The traveling wave fault location method for a multi-branch hybrid line according to claim 1, characterized in that, Based on the line topology of the hybrid line and the order of wavefront arrival times at different monitoring nodes, the fault branch that generates the traveling wave in the pre-fault section is determined, specifically including: From the monitoring nodes that receive the traveling wave signal, select the target monitoring node with the earliest arrival time of the traveling wave and the adjacent monitoring nodes of the target monitoring node; wherein, the adjacent monitoring nodes include at least one or more of the following: upstream main line monitoring nodes, downstream main line monitoring nodes and branch monitoring nodes; Based on the line topology of the hybrid line, determine the node distance between the adjacent monitoring node and the target monitoring node; Based on the order of arrival times of the wavefronts of the target monitoring node and the adjacent monitoring nodes, and according to the node distance, the propagation path of the traveling wave signal is analyzed. The fault branch that generates the traveling wave in the pre-fault section is determined through the propagation path.

3. The traveling wave fault location method for a multi-branch hybrid line according to claim 1, characterized in that, Based on the signal characteristics and the signal energy of the high-frequency transient signal, fault pre-identification is performed on the hybrid line to determine the pre-fault events generated in the hybrid line and the pre-fault sections corresponding to the pre-fault events, specifically including: Fault pre-identification is performed on the hybrid line to match the signal features with a preset warning signal feature library and determine the similarity between the signal features and each warning signal feature in the warning signal feature library; wherein, the warning signal features include amplitude, polarity, steepness and energy distribution; If the similarity exceeds a preset threshold and the signal energy of the high-frequency transient signal is less than a preset multiple of the energy threshold of the real fault signal, a pre-fault event is determined to have occurred in the hybrid line. Identify the designated monitoring node among the monitoring nodes that acquired the high-frequency transient signal; Based on the reception time of the high-frequency transient signal received by the designated monitoring node, the pre-fault section in the hybrid line that generates the pre-fault event is obtained by inversion.

4. The traveling wave fault location method for a multi-branch hybrid line according to claim 3, characterized in that, Based on the reception time of the high-frequency transient signal received by the designated monitoring node, the pre-fault section in the hybrid line that generates the pre-fault event is retrieved, specifically including: The hybrid line is divided into multiple candidate potential hazard points according to a fixed distance; wherein, each line section has one candidate potential hazard point; Based on the shortest propagation path from the candidate potential hazard point to each designated monitoring node, calculate the theoretical arrival time of the high-frequency transient signal from the candidate potential hazard point to each designated monitoring node when the high-frequency transient signal is generated. Based on the error between the received time of the high-frequency transient signal and the theoretical arrival time, the line section where the candidate potential hazard point with the error value is less than the preset error is selected as the pre-fault section in the hybrid line where the pre-fault event occurs.

5. The traveling wave fault location method for a multi-branch hybrid line according to claim 1, characterized in that, After determining the fault branch in the pre-fault section that generates the traveling wave, the method further includes: Determine the branch line length of the faulty branch, and the arrival time of the wavefronts of the traveling wave at both ends of the faulty branch; The propagation speed of the traveling wave is obtained, and the deviation distance between the midpoint of the fault branch and the fault point is calculated based on the difference in the arrival time of the wavefronts at both ends and the propagation speed. The distance between the fault point and both ends of the faulty branch is determined based on the average of the deviation distance and the branch line length.

6. The traveling wave fault location method for a multi-branch hybrid line according to claim 1, characterized in that, Before acquiring the high-frequency transient signal generated in the hybrid circuit, the method further includes: Obtain the topology data of the hybrid lines; Based on the topology data, key nodes in the hybrid line are pre-selected; wherein, the key nodes include at least the beginning and end of the line and all T-junction branch points; Dead zone analysis was performed on the pre-selected key nodes to identify dead zone sections that could not be located by any traveling wave synchronous monitoring device. Supplementary nodes are added to the dead zone section, and the monitoring nodes on the hybrid line that require the deployment of traveling wave synchronization monitoring devices are determined based on the positions of the supplementary nodes and the key nodes.

7. The traveling wave fault location method for a multi-branch hybrid line according to claim 6, characterized in that, After determining the monitoring nodes on the hybrid line where traveling wave synchronization monitoring devices need to be deployed based on the locations of the supplementary nodes and the key nodes, the method further includes: Determine the coverage of the monitoring node over the hybrid line; wherein, the coverage refers to the proportion of the hybrid line that can be located by at least two traveling wave synchronous monitoring devices; If the coverage exceeds a preset coverage threshold and the number of monitoring nodes exceeds a preset upper limit, the monitoring nodes are removed sequentially in ascending order of their contribution to the coverage, until the number of removed monitoring nodes meets the preset upper limit.

8. The traveling wave fault location method for a multi-branch hybrid line according to claim 1, characterized in that, The fault initiation threshold corresponding to the signal feature is lower than the fault feature threshold corresponding to the signal feature in the case of a fault in the hybrid line.

9. A traveling wave fault location device for a multi-branch hybrid line, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a traveling wave fault location method for a multi-branch hybrid line as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: A traveling wave fault location method for a multi-branch hybrid line as described in any one of claims 1-8.

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