Traveling wave fault location centralized analysis system for distribution network

Abstract

This invention discloses a centralized analysis system for traveling wave fault location in distribution networks, relating to the field of power system fault location technology. It includes a centralized master station and multiple monitoring terminals. The monitoring terminals are deployed at different nodes in the distribution network, synchronously collecting fault traveling wave signals and uploading them to the centralized master station. The centralized master station includes a topology modeling module, a data preprocessing module, a wavefront identification module, a candidate branch screening module, a matching evaluation module, and a precise location module. This invention establishes a full-process quality tracking and adaptive weighting mechanism from preprocessing to precise location, based on a dual-dimensional comprehensive evaluation system of topology sensitivity and signal quality. This enables the system to adjust its location strategy according to the distribution network topology and signal status, maintaining high fault location accuracy and reliability in complex distribution network environments, thus improving the system's robustness and practicality.

Description

Technical Field

[0001] This invention relates to the field of power system fault location technology, and in particular to a centralized analysis system for traveling wave fault location in distribution networks. Background Technology

[0002] As the final link in the power system, the distribution network directly supplies power to end users, and its operational reliability directly affects power quality. With the integration of distributed power sources and the increase in load density, the distribution network structure is becoming increasingly complex, and the frequency of faults is significantly increasing. Traveling wave fault location technology uses the transient traveling wave signal generated by the fault for location, and has the advantages of being unaffected by line parameters and having a fast location speed.

[0003] Existing traveling wave fault location technologies are mainly based on the principle of double-ended or single-ended ranging, locating faults by analyzing the arrival time of the traveling wave. However, distribution networks are characterized by multiple branches, multiple T-connections, and complex topologies. When traveling wave signals pass through multiple branch nodes, they undergo complex refraction and reflection, making wavefront identification difficult. Transient interference generated by distributed generation grid connection and power electronic equipment switching further complicates signal analysis. Furthermore, the deployment of monitoring points is limited, with some points exhibiting poor signal quality or being insensitive to specific faults due to topological influences. Existing technologies often apply equal weight to monitoring data of varying quality, failing to fully consider the impact of topology on the sensitivity of each monitoring point. Therefore, the accuracy and reliability of fault location in complex distribution network environments need improvement. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a centralized analysis system for traveling wave fault location in distribution networks, which solves the technical problem of insufficient accuracy and reliability of fault location in existing technologies due to large differences in signal quality and complex topology in complex distribution network environments.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a centralized analysis system for traveling wave fault location in distribution networks, including a centralized master station and multiple monitoring terminals; Monitoring terminals are deployed at different nodes in the power distribution network to synchronously collect fault traveling wave signals and upload them to the centralized master station; Centralized master stations include: The topology modeling module is used to build distribution network topology models; The data preprocessing module is used to preprocess the fault traveling wave signals uploaded by each monitoring terminal to form a multi-monitoring point traveling wave dataset. The wavefront recognition module is used to identify multiple wavefronts, extract the wavefront arrival time sequence, and construct a traveling wave time series feature map. The candidate branch screening module is used to screen and obtain candidate fault branches based on the relative time difference of the arrival time of the initial wavefront at each monitoring point and the time difference topology constraints established by the distribution network topology model. The matching and evaluation module is used to perform matching and evaluation of each candidate fault branch based on the traveling wave time series feature map, and to determine the target fault branch and the initial fault location. The precise location module is used to establish an adaptive weighted model based on the topological sensitivity of each monitoring point to the target fault branch and the signal quality, and to use a weighted optimization algorithm to determine the precise fault distance and output the fault location result.

[0007] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the topology modeling module includes: Obtain distribution network topology information, which includes node information, branch connection relationships, and line parameters; Obtain monitoring point deployment information, which includes the monitoring point number and installation location; Based on the distribution network topology information, a basic topology graph model is constructed using graph theory methods; Based on the monitoring point deployment information and the basic topology model, the traveling wave propagation path from each branch to each monitoring point is established and the path length is calculated to construct the path topology matrix. Integrate the basic topology graph model and the path topology matrix to establish a distribution network topology model.

[0008] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the data preprocessing module includes: Perform data validity verification and noise reduction filtering on the traveling wave data; The filtered traveling wave data is time-aligned and corrected based on the timestamp to obtain preprocessed traveling wave data.

[0009] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the wavefront identification module includes: Multi-scale wavelet transform was performed on the traveling wave dataset from multiple monitoring points to extract the wavelet modulus maxima sequence for each monitoring point. The waveform correlation between each monitoring point is calculated based on the wavelet modulus maximum sequence. The monitoring points are then grouped according to the correlation to form a high-correlation monitoring point group and a low-correlation monitoring point group. For highly correlated monitoring point groups, a collaborative wavefront identification algorithm is adopted to identify the initial wavefront and subsequent wavefronts by utilizing the temporal consistency among monitoring points; For low-correlation monitoring point groups, an independent wavefront identification algorithm is used to identify the initial and subsequent wavefronts based on the wavelet modulus maxima feature. Extract the arrival time sequence and wavefront characteristic parameters of each monitoring point, and assign a confidence weight to each wavefront based on the grouping results of the monitoring points; Based on the wavefront arrival time sequence, wavefront feature parameters, and confidence weights, a traveling wave time series feature map of each monitoring point is constructed.

[0010] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the candidate branch screening module includes: The initial wavefront arrival time and confidence weight of each monitoring point are obtained from the traveling wave time series feature map; Taking into account both credibility weight and topological centrality, the monitoring point with the highest comprehensive score is selected as the time reference point. The time difference between other monitoring points and the time reference point is calculated to construct the measured relative time difference vector. Traverse each branch in the distribution network topology model, extract the path length from each branch to each monitoring point from the path topology matrix, and determine the traveling wave propagation speed according to the line type; Based on the path length and traveling wave propagation speed, and using the time reference point as a reference, the theoretical relative time difference vector is calculated. Based on the measured relative time difference vector and the theoretical relative time difference vector, a weighted evaluation is performed using a confidence weight, and a set of candidate fault branches is selected and output.

[0011] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the matching evaluation module includes: Traverse each candidate branch in the candidate fault branch set, extract the propagation path information and path length from the candidate branch to each monitoring point from the path topology matrix, and update the credibility weight of each monitoring point according to the complexity of the propagation path. Multiple candidate fault locations are set on the candidate branch; For each candidate fault location, the theoretical multi-wavehead arrival time sequence is calculated based on the path length and traveling wave propagation speed, and the corresponding theoretical waveform characteristic parameters are calculated based on the traveling wave propagation characteristics. The measured arrival time sequence of the multi-wave head at each monitoring point is compared with the theoretical arrival time sequence of the multi-wave head. At the same time, the measured waveform characteristic parameters are compared with the theoretical waveform characteristic parameters. The matching score is calculated based on the time deviation, characteristic parameter deviation and confidence weight. Among all candidate fault locations of all candidate branches, the candidate fault location with the highest matching score is selected as the initial fault location, and the candidate branch where the initial fault location is located is selected as the target fault branch.

[0012] As a preferred embodiment of the traveling wave fault location centralized analysis system for distribution networks of the present invention, the precise positioning module includes: Calculate the topology sensitivity of each monitoring point based on the path length, topology complexity, and path uniqueness from the target fault branch to each monitoring point. Obtain the signal-to-noise ratio, waveform reliability weight, and data integrity index of each monitoring point in the traveling wave time series feature map, and construct a signal quality evaluation vector; Based on topological sensitivity and signal quality evaluation vector, the comprehensive weight coefficient of each monitoring point is calculated, and an adaptive weighted model is established. Based on the arrival time of the wavefront, path length, and comprehensive weighting coefficient of each monitoring point, a weighted least squares optimization model is established and solved with the fault distance as the variable to be determined, so as to obtain the accurate fault distance. Based on the accurate fault distance and optimized residual, the location reliability is calculated, and the fault location result is output.

[0013] Secondly, the present invention provides a centralized analysis method for traveling wave fault location in distribution networks, including: acquiring distribution network topology information and monitoring point deployment information, and establishing a distribution network topology model; Each monitoring point synchronously collects fault traveling wave signals and uploads them to a centralized main station for preprocessing, forming a multi-monitoring point traveling wave dataset; Multi-wavehead identification was performed on a multi-monitoring-point traveling wave dataset, and the wavehead arrival time sequence was extracted to construct a traveling wave time series feature map. Based on the relative time difference of the arrival time of the initial wavefront at each monitoring point, and combined with the distribution network topology model, time difference topology constraints are established to screen the distribution network branches and obtain candidate fault branches. Based on the traveling wave time series feature map, each candidate fault branch is matched and evaluated to determine the target fault branch and the initial fault location. Based on the topological sensitivity of each monitoring point to the target fault branch, an adaptive weighted model is established in conjunction with signal quality. A weighted optimization algorithm is then used to determine the precise fault distance and output the fault location result.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the centralized analysis system for traveling wave fault location of distribution networks as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the centralized analysis system for traveling wave fault location of distribution networks as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By establishing a full-process quality tracking and adaptive weighting mechanism from preprocessing, wavefront identification, candidate branch screening, matching evaluation to precise positioning, this invention achieves effective transmission and dynamic application of data quality assessment results among various modules, ensuring that low-quality data is reasonably suppressed while high-quality data is fully utilized. Based on a dual-dimensional comprehensive evaluation system of topology sensitivity and signal quality, the system can automatically adjust its positioning strategy according to the complex topology of the distribution network and the actual signal state. Even in distribution network environments with uneven monitoring point deployment, large differences in signal quality, and complex topology, it can still maintain high fault location accuracy and reliability, significantly improving the robustness and practicality of the traveling wave fault location system in practical engineering applications. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a structural diagram of the centralized master station functional modules of a centralized analysis system for traveling wave fault location in power distribution networks.

[0019] Figure 2 This is a flowchart of the topology modeling module for a centralized analysis system for traveling wave fault location in distribution networks.

[0020] Figure 3 This is a flowchart of the wavefront identification module of a centralized analysis system for traveling wave fault location in distribution networks.

[0021] Figure 4 This is a flowchart of a centralized analysis method for traveling wave fault location in distribution networks. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a centralized analysis system for traveling wave fault location in distribution networks, including a centralized master station and multiple monitoring terminals; Monitoring terminals are deployed at different nodes in the power distribution network to synchronously collect fault traveling wave signals and upload them to a centralized master station.

[0026] The structure diagram of the centralized main station functional modules is as follows: Figure 1 As shown, it includes: The topology modeling module is used to build distribution network topology models; The data preprocessing module is used to preprocess the fault traveling wave signals uploaded by each monitoring terminal to form a multi-monitoring point traveling wave dataset. The wavefront recognition module is used to identify multiple wavefronts, extract the wavefront arrival time sequence, and construct a traveling wave time series feature map. The candidate branch screening module is used to screen and obtain candidate fault branches based on the relative time difference of the arrival time of the initial wavefront at each monitoring point and the time difference topology constraints established by the distribution network topology model. The matching and evaluation module is used to perform matching and evaluation of each candidate fault branch based on the traveling wave time series feature map, and to determine the target fault branch and the initial fault location. The precise location module is used to establish an adaptive weighted model based on the topological sensitivity of each monitoring point to the target fault branch and the signal quality, and to use a weighted optimization algorithm to determine the precise fault distance and output the fault location result.

[0027] The monitoring terminal includes a synchronous acquisition unit and a communication unit. The synchronous acquisition unit obtains a unified time reference based on satellite timing, synchronously acquires fault traveling wave signals, and timestamps them. Satellite timing includes GPS, BeiDou, Galileo, or GLONASS. The communication unit uploads the timestamped traveling wave data to the centralized master station. The communication unit uploads data via fiber optic communication networks, wireless communication networks, power line carrier communication networks, or industrial Ethernet.

[0028] Specifically, the flowchart for the topology modeling module is as follows: Figure 2As shown, this includes: acquiring distribution network topology information and monitoring point deployment information; the distribution network topology information includes node information, branch connection relationships, and line parameters, with line parameters including line length and line type; the monitoring point deployment information includes monitoring point number and installation location. Based on the distribution network topology information, a basic topology graph model is constructed using graph theory methods. The basic topology graph model represents the electrical connection relationships of the distribution network with nodes as vertices and branches as edges.

[0029] Furthermore, based on the monitoring point deployment information and the basic topology model, the traveling wave propagation paths from each branch to each monitoring point are established, and the path lengths are calculated to construct a path topology matrix. The specific steps are as follows: Based on the basic topology model, a breadth-first search algorithm is used to traverse each branch of the distribution network, establishing a branch node association table. Based on the monitoring point deployment information, the node positions of each monitoring point in the distribution network topology are determined, forming a monitoring point-node mapping relationship.

[0030] For each branch, the traveling wave propagation path from that branch to each monitoring point is calculated. The traveling wave propagation path is determined using the shortest path algorithm, including the branch sequence and node sequence. The physical length of each propagation path is calculated based on the line length and line type in the line parameters. For different line types, the path length is corrected using the corresponding traveling wave velocity parameters. A path topology matrix is ​​established, where the row index is the branch number, the column index is the monitoring point number, and the matrix elements are the path lengths from the corresponding branch to the monitoring point. For branch-monitoring point combinations with no connected paths, the matrix elements are assigned an infinity value.

[0031] Furthermore, the basic topology graph model and the path topology matrix are integrated to establish a distribution network topology model. The steps are as follows: The path topology matrix is ​​attached as a topology attribute to the basic topology graph model, forming an enhanced topology graph containing path information. An index structure is established for the distribution network topology model, including branch number index, monitoring point number index, and node number index, to support fast querying and access. A topology constraint rule set is constructed, including traveling wave propagation timing constraints, path connectivity constraints, and wave velocity consistency constraints, for topology rationality verification in subsequent fault location analysis.

[0032] Preferably, the topology modeling module of the present invention pre-establishes a path topology matrix, pre-calculating and storing the traveling wave propagation path length from each branch to each monitoring point. This avoids real-time path search calculations after a fault occurs, significantly improving the response speed of fault location. Simultaneously, the path topology matrix establishes an explicit mapping relationship between branches and monitoring points, providing direct topological constraints for subsequent candidate fault branch selection and improving the computational efficiency of branch selection. Furthermore, path length correction is performed using corresponding traveling wave velocity parameters for different line types, improving the accuracy of path length calculation.

[0033] Specifically, the data preprocessing module includes: data validity verification and noise reduction filtering of the traveling wave data, including the following steps: The centralized master station performs data integrity checks on the received traveling wave data, detecting missing data packets, abnormal timestamps, missing sampling points, and GPS timing status. For the traveling wave data that passes the integrity check, signal quality is assessed, calculating the signal-to-noise ratio, first-wave steepness, and waveform integrity, and assigning a quality weight coefficient to each monitoring point. Wavelet transform is performed on the traveling wave data, adaptively determining the frequency band threshold based on the signal energy distribution characteristics, retaining mid-to-high frequency wavelet coefficients, and suppressing low-frequency power frequency components and high-frequency random noise; variational mode decomposition is used to enhance weak fault signals. Distribution network characteristic interference is identified, and adaptive notch filtering and morphological filtering are used to suppress interference. The retained wavelet coefficients are reconstructed to obtain the noise-reduced and filtered traveling wave data.

[0034] Further, time alignment correction is performed on the filtered traveling wave data based on the timestamps to obtain preprocessed traveling wave data. This includes the following steps: extracting the timestamps of the traveling wave data from each monitoring point; calculating the time correction amount based on communication transmission delay and inherent equipment delay; and establishing a dynamic delay compensation model. The timestamps of each monitoring point are uniformly corrected using the time correction amount. The waveform cross-correlation function is further calculated by extracting the time window before the first wave, and fine-tuning the correction based on the relevant peak positions. The theoretical traveling wave propagation time difference between adjacent monitoring points is calculated based on the distribution network topology model and compared with the measured time difference for verification. If the time difference exceeds the limit, it is marked as a time synchronization anomaly and recalibration is triggered. The time-corrected traveling wave data is organized according to the monitoring point number and timestamp, and quality weights and reliability labels are added to form a multi-monitoring-point traveling wave dataset.

[0035] Ideally, through signal quality assessment, adaptive noise reduction, and multi-level time correction, the preprocessing module can improve signal quality and time synchronization accuracy in complex distribution network environments, enabling subsequent positioning algorithms to adaptively adjust data weights and improve system robustness and positioning reliability.

[0036] Furthermore, the flowchart of the wave head recognition module is as follows: Figure 3 As shown, the process includes: performing multi-scale wavelet transform on a multi-monitoring point traveling wave dataset to extract wavelet modulus maxima sequences for each monitoring point; calculating waveform correlation between monitoring points based on the wavelet modulus maxima sequences; and grouping the monitoring points according to the correlation to form high-correlation monitoring point groups and low-correlation monitoring point groups. The correlation threshold is adaptively determined based on the distribution network topology and the deployment location of the monitoring points; monitoring points within the high-correlation monitoring point group are typically located on similar topological paths.

[0037] For highly correlated monitoring point groups, a cooperative wavefront identification algorithm is used. This algorithm leverages the temporal consistency among monitoring points to identify initial and subsequent wavefronts. The process includes: searching for modulus maxima points that simultaneously appear at multiple monitoring points within the highly correlated monitoring point group; determining whether they meet the cross-scale persistence condition; identifying modulus maxima points that meet the condition as cooperative wavefronts; and identifying initial and subsequent wavefronts in chronological order of arrival. The cross-scale persistence condition, as shown in the architecture diagram, requires that the modulus maxima point exist at at least three consecutive scales and that the temporal coordinate deviation does not exceed the temporal consistency threshold.

[0038] For low-correlation monitoring point groups, an independent wavefront identification algorithm is adopted to identify the initial and subsequent wavefronts based on the wavelet modulus maxima characteristics. This includes: independently analyzing the wavelet modulus maxima sequence of each monitoring point, performing amplitude screening on the modulus maxima points that meet the cross-scale persistence condition, and retaining the modulus maxima points with wavelet modulus values ​​greater than the amplitude screening threshold as wavefronts. The amplitude screening threshold of the architecture diagram is adaptively determined based on the signal noise level.

[0039] Further, the arrival time sequence and wavefront feature parameters of each monitoring point are extracted. Based on the grouping results of the monitoring points, a confidence weight is assigned to each wavefront. The steps are as follows: The arrival times of the initial and subsequent wavefronts identified at each monitoring point are extracted and arranged in chronological order to form a wavefront arrival time sequence. Wavefront feature parameters are extracted, including wavefront polarity, wavefront amplitude, and wavefront steepness. For highly correlated monitoring point groups, a confidence weight is assigned based on the number of collaboratively identified monitoring points. For low-correlation monitoring point groups, a confidence weight is assigned based on a comprehensive consideration of the cross-scale consistency of the wavefront, amplitude intensity, and signal quality weights provided by the preprocessing module.

[0040] Furthermore, based on the wavefront arrival time sequence, wavefront feature parameters, and confidence weights, a traveling wave time series feature map of each monitoring point is constructed. Specifically, a two-dimensional matrix structure is established with the monitoring point as the row index and time as the column index. The wavefronts of each monitoring point are labeled according to their arrival time. The labeling information includes wavefront feature parameters and confidence weights, thus forming a traveling wave time series feature map.

[0041] Preferably, by utilizing the temporal consistency of highly correlated monitoring point groups through a collaborative wavefront identification algorithm, single-point pseudo-wavefront interference can be effectively suppressed, improving the accuracy and reliability of wavefront identification. For low-correlation monitoring point groups, an independent identification algorithm is employed, combined with cross-scale persistence criteria and adaptive amplitude filtering, ensuring accurate extraction of wavefront features even when signal quality differences are significant. A credibility-based weighting mechanism based on monitoring point grouping results and signal quality weights enables the traveling wave temporal feature map to reflect the reliability of each wavefront, providing differentiated weighting criteria for subsequent candidate branch selection and precise positioning, thereby improving the overall positioning performance of the system in complex distribution network environments.

[0042] Specifically, the candidate branch selection module includes: obtaining the initial wavefront arrival time and confidence weight of each monitoring point from the traveling wave time series feature map; comprehensively considering the confidence weight and topological centrality, selecting the monitoring point with the highest comprehensive score as the time reference point; calculating the time difference of other monitoring points relative to the time reference point; and constructing a measured relative time difference vector. The steps are as follows: calculating the average path length from each monitoring point to each branch based on the distribution network topology model, and determining the topological centrality of each monitoring point; calculating the comprehensive score of each monitoring point based on the confidence weight and topological centrality, and selecting the monitoring point with the highest comprehensive score as the time reference point; calculating the initial wavefront arrival time difference of other monitoring points relative to the time reference point, and constructing a measured relative time difference vector.

[0043] Furthermore, by traversing each branch in the distribution network topology model, the path length from each branch to each monitoring point is extracted from the path topology matrix, and the traveling wave propagation speed is determined according to the line type. Based on the path length and traveling wave propagation speed, the theoretical relative time difference vector is calculated using the time reference point as a reference.

[0044] Furthermore, based on the measured relative time difference vector and the theoretical relative time difference vector, a weighted evaluation is performed using confidence weights to screen and output a set of candidate faulty branches. This includes: calculating the difference between corresponding elements of the measured relative time difference vector and the theoretical relative time difference vector to obtain the time difference deviation vector; weighting the time difference deviation vector according to the confidence weights to calculate the weighted deviation value; comparing the weighted deviation value with a time difference matching threshold, and selecting candidate branches with weighted deviation values ​​less than the time difference matching threshold as candidate faulty branches. The time difference matching threshold is determined based on the time measurement uncertainty and the line length.

[0045] Ideally, by selecting the time reference point by comprehensively considering both reliability weights and topology centrality, the problem of improper topological location caused by relying solely on signal quality is avoided. This ensures that the reference point for time difference calculation is located in a topologically advantageous position, reducing the theoretical time difference calculation error. Employing a reliability-weighted time difference matching strategy automatically reduces the impact of low-quality monitoring point data on the screening results, improving the accuracy and robustness of candidate branch selection. Adaptively adjusting the time difference matching threshold based on line length makes the screening strategy more consistent with the actual characteristics of different line lengths in the distribution network, effectively narrowing down the range of candidate branches and providing a reliable foundation for subsequent precise positioning.

[0046] Specifically, the matching evaluation module includes: traversing each candidate branch in the candidate fault branch set, extracting the propagation path information and path length from the candidate branch to each monitoring point from the path topology matrix, and updating the credibility weight of each monitoring point according to the complexity of the propagation path. The steps are as follows: extracting the propagation path from the candidate branch to each monitoring point from the path topology matrix, counting the number of topology nodes and node types in the propagation path as path complexity indicators, adjusting the credibility weight of each monitoring point according to the path complexity and node type, and setting differentiated weight decay coefficients for different node types.

[0047] Preferably, the weight of the direct connection path remains unchanged, the weight of the path passing through the T-connection node decreases by 8%, the weight of the path passing through the bus node decreases by 15%, and the weight of the path passing through the transformer node decreases by 20%. The attenuation effect of multiple nodes is calculated cumulatively.

[0048] Furthermore, multiple candidate fault locations are set on the candidate branches. Preferably, the candidate locations are adaptively set according to the branch length and positioning accuracy requirements.

[0049] Furthermore, for each candidate fault location, the theoretical multi-wavefront arrival time sequence is calculated based on the path length and traveling wave propagation speed, and the corresponding theoretical waveform characteristic parameters are calculated based on the traveling wave propagation characteristics. Specific steps include: for each candidate fault location on a candidate branch, calculating the direct propagation path and main reflection path from that location to each monitoring point; calculating the theoretical multi-wavefront arrival time sequence, including the initial wavefront arrival time and subsequent reflected wave arrival times, prioritizing the calculation of primary reflected waves at the end of the line and major branch nodes, and determining whether to calculate secondary reflected waves based on the signal quality assessment results.

[0050] Furthermore, theoretical waveform characteristic parameters are calculated based on the traveling wave propagation characteristics, including: the theoretical amplitude of each wavefront, calculated based on the propagation distance and attenuation coefficient; the theoretical polarity of each wavefront, determined based on the type of reflection node, with open-circuit reflections maintaining polarity and short-circuit reflections reversing polarity; and waveform time-domain characteristics, including rise time and pulse width.

[0051] Furthermore, the measured arrival time sequences of multiple wavefronts (MWHs) at each monitoring point are compared with the theoretical MWHs arrival time sequences. Simultaneously, the measured waveform feature parameters are compared with the theoretical waveform feature parameters. A matching score is calculated based on the time deviation, feature parameter deviation, and confidence weight. The steps are as follows: The measured time sequence is matched with the theoretical time sequence using a dynamic time warping algorithm or a cross-correlation algorithm, and the time matching degree is calculated. The measured waveform feature parameters are compared with the theoretical waveform feature parameters, and the feature matching degree is calculated. The time matching degree and feature matching degree are weighted and fused, and combined with the confidence weight of the monitoring point, the matching score of that monitoring point for the current candidate position is calculated. The matching scores of all monitoring points are then summarized to obtain the comprehensive matching score for the candidate position.

[0052] In one embodiment, the time-matching weight is set to 0.65 and the feature-matching weight is set to 0.35, reflecting the dominant role of time-series information.

[0053] Furthermore, among all candidate fault locations on all candidate branches, the candidate fault location with the highest matching score is selected as the initial fault location, and the candidate branch containing the initial fault location is selected as the target fault branch. Preferably, the top 3 to 5 candidate locations can be retained as alternatives to improve the reliability of the location.

[0054] Ideally, by employing a dual matching strategy of node type-differentiated weight adjustment and multi-wavelength time-series waveform matching, the accuracy of fault branch identification is improved, avoiding misjudgments caused by the traditional method's equal treatment of all monitoring points. Adaptive candidate position setting optimizes computational efficiency while ensuring positioning accuracy, providing a reliable input basis for the precise positioning module.

[0055] Specifically, the precise location module includes: calculating the topological sensitivity of each monitoring point based on the path length, topological complexity, and path uniqueness from the target faulty branch to each monitoring point. The steps are as follows: extracting the direct path length and the number and type of topological nodes traversed from the target faulty branch to each monitoring point from the path topology matrix, and calculating the path topological complexity; counting the number of independent propagation paths from the target faulty branch to each monitoring point, and evaluating path uniqueness; and calculating the topological sensitivity of each monitoring point to the target faulty branch based on the direct path length, topological complexity, and path uniqueness. Among these, topological sensitivity is negatively correlated with path length, positively correlated with path uniqueness, and negatively correlated with topological complexity.

[0056] Furthermore, the signal-to-noise ratio (SNR), waveform reliability weight, and data integrity index for each monitoring point in the traveling wave time-series feature map are obtained to construct a signal quality evaluation vector. Specifically, the SNR calculated by the preprocessing module and the waveform reliability weight determined by the wavefront identification module are obtained; based on the wavefront arrival time sequence integrity and waveform continuity at each monitoring point, the data integrity index is calculated to construct the signal quality evaluation vector. The data integrity index includes the wavefront identification completeness rate and the waveform sampling continuity rate.

[0057] Furthermore, based on the topology sensitivity and signal quality evaluation vector, the comprehensive weight coefficient of each monitoring point is calculated, and an adaptive weighted model is established. This includes: normalizing the topology sensitivity and signal quality evaluation vector respectively, setting a topology weight factor and a signal quality weight factor for weighted fusion, and calculating the comprehensive weight coefficient of each monitoring point; based on the comprehensive weight coefficient of each monitoring point and the topology characteristics of the target fault branch, an adaptive weighted model is established, and the adaptive weighted model dynamically adjusts the weight contribution of each monitoring point according to the change of the fault location.

[0058] Furthermore, based on the arrival time of the wavefront, path length, and comprehensive weighting coefficient of each monitoring point, a weighted least squares optimization model is established and solved with the fault distance as the variable to be determined, to obtain the accurate fault distance. This includes: calculating the estimated single-point fault distance for each monitoring point based on the arrival time of the wavefront, path length, and traveling wave propagation speed of each monitoring point; establishing a ranging equation system with the fault distance as the variable to be determined, and establishing a weighted least squares objective function using the comprehensive weighting coefficient of each monitoring point as the weight matrix; and using an optimization algorithm to solve for the accurate fault distance, including the least squares method or gradient descent method.

[0059] Furthermore, based on the precise fault distance and optimized residuals, the location reliability is calculated, and the fault location result is output. The fault location result includes the target fault branch, the fault distance, and the location reliability. Specifically, the ranging residuals of each monitoring point are calculated, and the ranging residuals are weighted and summed to calculate the optimized residual. Based on the magnitude of the optimized residual and the distribution characteristics of the ranging residuals of each monitoring point, a confidence assessment model is used to calculate the location reliability. The target fault branch, precise fault distance, and location reliability are integrated to generate and output the fault location result. The location reliability is negatively correlated with the optimized residual. The fault location result also includes information on the monitoring points involved in the location and the comprehensive weight coefficient of each monitoring point.

[0060] Ideally, by employing an adaptive weighted model that comprehensively considers topology sensitivity and signal quality, the weight contribution of each monitoring point can be dynamically adjusted, effectively suppressing the impact of low-quality data and topology-insensitive monitoring points on the location results. The weighted least squares optimization method fully utilizes information from multiple monitoring points, improving the accuracy and stability of fault distance calculation. Location reliability assessment provides maintenance personnel with quantitative indicators of result reliability, enhancing the system's practicality.

[0061] This embodiment also provides a centralized analysis method for traveling wave fault location in distribution networks, the flowchart of which is shown below. Figure 4 As shown, it includes: Obtain information on the distribution network topology and monitoring point deployment, and establish a distribution network topology model; Each monitoring point synchronously collects fault traveling wave signals and uploads them to a centralized main station for preprocessing, forming a multi-monitoring point traveling wave dataset; Multi-wavehead identification was performed on a multi-monitoring-point traveling wave dataset, and the wavehead arrival time sequence was extracted to construct a traveling wave time series feature map. Based on the relative time difference of the arrival time of the initial wavefront at each monitoring point, and combined with the distribution network topology model, time difference topology constraints are established to screen the distribution network branches and obtain candidate fault branches. Based on the traveling wave time series feature map, each candidate fault branch is matched and evaluated to determine the target fault branch and the initial fault location. Based on the topological sensitivity of each monitoring point to the target fault branch, an adaptive weighted model is established in conjunction with signal quality. A weighted optimization algorithm is then used to determine the precise fault distance and output the fault location result.

[0062] This embodiment also provides a computer device suitable for a centralized analysis system for traveling wave fault location in a distribution network, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the centralized analysis system for traveling wave fault location in a distribution network as proposed in the above embodiment.

[0063] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0064] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the centralized analysis system for traveling wave fault location in distribution networks as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0065] In summary, this invention establishes a comprehensive quality tracking and adaptive weighting mechanism covering the entire process from preprocessing, wavefront identification, candidate branch screening, matching evaluation to precise location. This mechanism enables the effective transfer and dynamic application of data quality assessment results across modules, ensuring that low-quality data is reasonably suppressed while high-quality data is fully utilized. Based on a dual-dimensional comprehensive evaluation system of topology sensitivity and signal quality, the system can automatically adjust its location strategy according to the complex topology of the distribution network and the actual signal conditions. Even in distribution network environments with uneven monitoring point deployment, large differences in signal quality, and complex topologies, it maintains high fault location accuracy and reliability, significantly improving the robustness and practicality of the traveling wave fault location system in practical engineering applications.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A centralized analysis system for traveling wave fault location in distribution networks, characterized in that: Includes a centralized main station and multiple monitoring terminals; The monitoring terminal is deployed at different nodes of the power distribution network to synchronously collect fault traveling wave signals and upload them to the centralized master station; The centralized master station includes: The topology modeling module is used to build distribution network topology models; The data preprocessing module is used to preprocess the fault traveling wave signals uploaded by each monitoring terminal to form a multi-monitoring point traveling wave dataset. The wavefront recognition module is used to identify multiple wavefronts, extract the wavefront arrival time sequence, and construct a traveling wave time series feature map. The candidate branch screening module is used to screen and obtain candidate fault branches based on the relative time difference of the arrival time of the initial wavefront at each monitoring point and the time difference topology constraints established by the distribution network topology model. The matching and evaluation module is used to perform matching and evaluation on each candidate fault branch based on the traveling wave time series feature map, and to determine the target fault branch and the preliminary fault location. The precise location module is used to establish an adaptive weighted model based on the topological sensitivity of each monitoring point to the target fault branch and the signal quality, and to use a weighted optimization algorithm to determine the precise fault distance and output the fault location result. The wave head recognition module includes: Multi-scale wavelet transform was performed on the traveling wave dataset from multiple monitoring points to extract the wavelet modulus maxima sequence for each monitoring point. The waveform correlation between each monitoring point is calculated based on the wavelet modulus maxima sequence. The monitoring points are then grouped according to the correlation to form a high-correlation monitoring point group and a low-correlation monitoring point group. For highly correlated monitoring point groups, a collaborative wavefront identification algorithm is adopted to identify the initial wavefront and subsequent wavefronts by utilizing the temporal consistency among monitoring points; For low-correlation monitoring point groups, an independent wavefront identification algorithm is used to identify the initial and subsequent wavefronts based on the wavelet modulus maxima feature. Extract the arrival time sequence and wavefront characteristic parameters of each monitoring point, and assign a confidence weight to each wavefront based on the grouping results of the monitoring points; Based on the wavefront arrival time sequence, wavefront feature parameters, and confidence weights, a traveling wave time series feature map of each monitoring point is constructed. The precise positioning module includes: Calculate the topology sensitivity of each monitoring point based on the path length, topology complexity, and path uniqueness from the target fault branch to each monitoring point. Obtain the signal-to-noise ratio, waveform reliability weight, and data integrity index of each monitoring point in the traveling wave time series feature map, and construct a signal quality evaluation vector; Based on the topology sensitivity and the signal quality evaluation vector, calculate the comprehensive weight coefficient of each monitoring point and establish an adaptive weighted model. Based on the arrival time of the wavefront, path length, and comprehensive weighting coefficient of each monitoring point, a weighted least squares optimization model is established and solved with the fault distance as the variable to be determined, so as to obtain the accurate fault distance. Based on the precise fault distance and optimized residual, the location confidence is calculated, and the fault location result is output.

2. The centralized analysis system for traveling wave fault location in distribution networks as described in claim 1, characterized in that: The topology modeling module includes: Obtain distribution network topology information, which includes node information, branch connection relationships, and line parameters; Obtain monitoring point deployment information, which includes the monitoring point number and installation location; Based on the aforementioned power distribution network topology information, a basic topology graph model is constructed using graph theory methods; Based on the monitoring point deployment information and the basic topology model, establish the traveling wave propagation path from each branch to each monitoring point and calculate the path length, and construct the path topology matrix; By integrating the basic topology graph model and the path topology matrix, a distribution network topology model is established.

3. The centralized analysis system for traveling wave fault location in distribution networks as described in claim 1, characterized in that: The data preprocessing module includes: Perform data validity verification and noise reduction filtering on the traveling wave data; The filtered traveling wave data is time-aligned and corrected based on the timestamp to obtain preprocessed traveling wave data.

4. The centralized analysis system for traveling wave fault location in distribution networks as described in claim 1, characterized in that: The candidate branch screening module includes: The initial wavefront arrival time and confidence weight of each monitoring point are obtained from the traveling wave time series feature map; Taking into account both credibility weight and topological centrality, the monitoring point with the highest comprehensive score is selected as the time reference point. The time difference of other monitoring points relative to the time reference point is calculated, and the measured relative time difference vector is constructed. Traverse each branch in the power distribution network topology model, extract the path length from each branch to each monitoring point from the path topology matrix, and determine the traveling wave propagation speed according to the line type; Based on the path length and the traveling wave propagation speed, and using the time reference point as a reference, calculate the theoretical relative time difference vector; Based on the measured relative time difference vector and the theoretical relative time difference vector, a weighted evaluation is performed using a confidence weight, and a set of candidate fault branches is screened and output.

5. The centralized analysis system for traveling wave fault location in distribution networks as described in claim 1, characterized in that: The matching evaluation module includes: Traverse each candidate branch in the candidate fault branch set, extract the propagation path information and path length from the candidate branch to each monitoring point from the path topology matrix, and update the confidence weight of each monitoring point according to the complexity of the propagation path. Multiple candidate fault locations are set on the candidate branch; For each candidate fault location, the theoretical multi-wavehead arrival time sequence is calculated based on the path length and traveling wave propagation speed, and the corresponding theoretical waveform characteristic parameters are calculated based on the traveling wave propagation characteristics. The measured arrival time sequence of the multi-wave head at each monitoring point is compared with the theoretical arrival time sequence of the multi-wave head. At the same time, the measured waveform characteristic parameters are compared with the theoretical waveform characteristic parameters. The matching score is calculated based on the time deviation, characteristic parameter deviation and confidence weight. Among all candidate fault locations of all candidate branches, the candidate fault location with the highest matching score is selected as the initial fault location, and the candidate branch where the initial fault location is located is selected as the target fault branch.

6. A centralized analysis method for traveling wave fault location in distribution networks, based on the centralized analysis system for traveling wave fault location in distribution networks as described in any one of claims 1 to 5, characterized in that: include: Obtain information on the distribution network topology and monitoring point deployment, and establish a distribution network topology model; Each monitoring point synchronously collects fault traveling wave signals and uploads them to a centralized main station for preprocessing, forming a multi-monitoring point traveling wave dataset; Multi-wavehead identification is performed on the multi-monitoring point traveling wave dataset, the wavehead arrival time sequence is extracted, and a traveling wave time series feature map is constructed; Based on the relative time difference of the arrival time of the initial wavefront at each monitoring point, and combined with the distribution network topology model, time difference topology constraints are established to screen the distribution network branches and obtain candidate fault branches. Based on the traveling wave time series feature map, each candidate fault branch is matched and evaluated to determine the target fault branch and the initial fault location. Based on the topological sensitivity of each monitoring point to the target fault branch, an adaptive weighted model is established in conjunction with signal quality. A weighted optimization algorithm is then used to determine the precise fault distance and output the fault location result.

7. 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 centralized analysis system for traveling wave fault location of distribution networks as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the centralized analysis system for traveling wave fault location of distribution networks as described in any one of claims 1 to 5.

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