Power transmission line fault monitoring and positioning method, system, equipment and medium

Abstract

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system, device and medium. BACKGROUND

[0002] With the continuous expansion of the power grid scale and the in-depth promotion of smart grid construction, fast and accurate positioning of power transmission line faults has become a key link to ensure the safe and stable operation of the power grid. Especially in complex terrain and extreme weather conditions, quickly identifying fault types and accurately locating fault points is of great significance to shorten the power outage time and improve power supply reliability.

[0003] Existing power transmission line fault positioning technology is mainly based on the traveling wave propagation theory. By installing traveling wave distance measurement devices at both ends of the line, the traveling wave signals generated at the moment of fault are collected, and the time difference of traveling wave arrival at both ends is used for fault point positioning calculation. This method can achieve high positioning accuracy in single-point fault conditions.

[0004] However, when multiple faults occur on the power transmission line, the traditional double-end positioning method is difficult to accurately identify and locate multiple fault points. At the same time, the ability to identify and classify different types of faults is limited, and there is a lack of systematic fault feature extraction and classification methods, which affects the accuracy of fault positioning; this situation needs to be further improved. SUMMARY

[0005] In order to solve the problem that the existing positioning method is difficult to accurately identify and locate multiple fault points, and lacks a systematic fault feature extraction and classification method, which affects the accuracy of fault positioning, the present application provides a power transmission line fault monitoring and positioning method, system, device and medium, which adopts the following technical solutions: In a first aspect, the present application provides a power transmission line fault monitoring and positioning method, comprising the following steps: Collecting original current signals and performing digital conversion to obtain time series data; According to the time series data, performing fault event identification and classification through threshold detection and feature extraction algorithm to obtain classification labels; Based on the classification labels, using traveling wave propagation model and time difference calculation for fault location positioning, wherein the multiple wave head data in the preset time window are subjected to cluster analysis to determine single / multiple fault points, and the positioning result is obtained; According to the positioning result, transmitting to the center server through the communication network for comprehensive verification and alarm management to form a fault response.

[0006] By adopting the technical scheme, in order to solve the problem of difficult multi-point fault positioning of the power transmission line, the current fault positioning method mainly relies on simple time difference calculation, and in actual application, the positioning accuracy is often not high; for example, when multiple faults occur on the line at the same time, due to the mutual superposition and interference of the wave head signals, it is difficult to accurately determine the number and position of the fault points; the application first digitizes the original current signal to obtain time sequence data, then identifies and classifies the fault event through threshold detection and feature extraction algorithm, and obtains the classification label of the fault type; then, based on the classification label, a traveling wave propagation model is established and time difference calculation is performed, and at the same time, multiple wave head data are clustered and analyzed in a preset time window, and whether it is a single-point fault or a multi-point fault is determined through the clustering result; finally, the positioning result is transmitted to the central server for comprehensive verification and alarm management, forming the final fault response; by introducing the time sequence data analysis and clustering judgment mechanism, not only the fault type can be accurately identified, but also the single-point and multi-point fault conditions can be effectively distinguished, and at the same time, the reliability of the positioning result is improved through the comprehensive verification of the central server.

[0007] Optionally, according to the time sequence data, the fault event identification and classification are performed through a threshold detection and feature extraction algorithm to obtain a classification label, specifically including the following steps: According to the time sequence data, an adaptive threshold algorithm based on historical data statistics is applied to exclude abnormal values to obtain an abnormal event detection result; According to the abnormal event detection result, feature data is extracted; Based on the feature data, a preset feature extraction algorithm is used for fault type classification to obtain a fault classification result, wherein the preset feature extraction algorithm includes judging lightning fault through the polarity, amplitude ratio and time domain feature of the traveling wave signal, judging lightning shielding or counterattack through multi-peak waveform and reflection coefficient analysis, judging grounding and short circuit fault through zero sequence component and phase-to-phase imbalance calculation, and distinguishing transient fault and permanent fault according to signal attenuation characteristics and duration; The fault classification result is self-diagnosed and noise corrected to form a final classification label.

[0008] By adopting the technical scheme, since the fault types of the power transmission line are complex and diverse, including lightning, shielding, grounding and short circuit and the like, the traditional fixed threshold detection method is difficult to accurately identify the fault characteristics, the adaptive threshold algorithm based on historical data statistics is applied to the time series data to remove the abnormal values, and the reliability of the detection result is ensured; then the feature data is extracted from the abnormal event detection result; then the multi-dimensional feature extraction algorithm is used for fault classification, including identifying lightning faults by the polarity, amplitude ratio and time domain characteristics of the traveling wave signal, determining lightning shielding or counterattack by the multi-peak waveform and reflection coefficient analysis, identifying grounding and short circuit faults by the zero sequence component and phase imbalance calculation, and distinguishing transient and permanent faults according to the signal attenuation characteristics and duration; finally, self-diagnosis and noise correction are performed on the fault classification result to form the final classification label; by introducing the adaptive threshold and multi-dimensional feature analysis mechanism, the characteristics of different fault types are combined to realize accurate identification and classification of various faults, and the accuracy of the classification result is improved through self-diagnosis and noise correction.

[0009] Optionally, according to the time series data, an adaptive threshold algorithm based on historical data statistics is applied to remove abnormal values to obtain an abnormal event detection result, specifically including the following steps: The time stamp validity is checked according to the line length and wave speed to calculate the theoretical propagation time, and the time stamp validity judgment result is obtained; Based on the time stamp validity judgment result, the historical records are used to establish the time difference expectation value and standard deviation to obtain the statistical characteristic parameters; According to the statistical characteristic parameters, the abnormal values are removed to obtain the abnormal value judgment result; According to the abnormal value judgment result and the preset sample quantity determination threshold, the detection data is marked to form the abnormal event detection result.

[0010] By adopting the technical scheme, the time stamp of the time series data is first checked for legality, the validity of the time stamp is verified by calculating the theoretical propagation time according to the line length and wave speed; then based on the valid time stamp data, the expectation value and standard deviation of the time difference are calculated using historical records to establish statistical characteristic parameters; then the abnormal value is determined according to these statistical characteristic parameters, and the data deviating from the normal range is marked as an abnormal value; finally, the detection data is marked in combination with the abnormal value determination result and the sample quantity determination threshold to generate the final abnormal event detection result; by introducing the time stamp legality check and historical data statistical analysis, an adaptive abnormal value determination standard is established to realize accurate identification and removal of abnormal data, and the reliability of the detection result is ensured through sample quantity control.

[0011] Optionally, based on the classification label, fault location positioning is performed by using a traveling wave propagation model and time difference calculation, and specifically includes the following steps: According to line parameters, a traveling wave propagation speed is calculated, a traveling wave propagation model is established, and a propagation speed parameter is obtained; Based on the classification label, corresponding waveform features and positioning algorithms are selected, wherein for single fault, a double-end time difference positioning algorithm is used, and for complex fault, multi-wave head clustering analysis is performed within a preset time window, the number of fault points is determined according to the distance of the clustering center, and the number of fault points is obtained; The traveling wave arrival time difference is obtained by using a multi-terminal GPS synchronous clock, and the initial fault distance is calculated by using a time difference positioning algorithm according to the propagation speed parameter; According to the initial fault distance, attenuation compensation and parameter correction are performed for a long-distance line to obtain a corrected fault distance value; The corrected fault distance value, the number of fault points and the classification label are encapsulated to form a positioning result.

[0012] By using the above technical solution, since both single fault and complex fault need to be considered during the fault location process of the power transmission line, and there is a problem of signal attenuation and parameter drift in the long-distance power transmission line, the traditional single positioning algorithm is difficult to adapt to the positioning needs of different fault types. According to the line parameters, the traveling wave propagation speed is calculated and the propagation model is established. Then, based on the fault classification label, the corresponding positioning strategy is selected. For single fault, a double-end time difference positioning algorithm is used, and for complex fault, multi-wave head clustering analysis is performed within a preset time window. The number of fault points is determined by the distance of the clustering center. Then, the traveling wave arrival time difference is obtained by using a multi-terminal GPS synchronous clock, and the initial fault distance is calculated in combination with the propagation speed parameter. Then, for the characteristics of the long-distance line, attenuation compensation and parameter correction are performed on the initial fault distance. Finally, the corrected fault distance value, the number of fault points and the classification label are encapsulated to form a complete positioning result. By introducing the fault type adaptive selection and long-distance compensation mechanism, accurate positioning of different types of faults is realized, and the accuracy of long-distance positioning is improved by parameter correction.

[0013] Optionally, within a preset time window, multi-wave head clustering analysis is performed, the number of single / multiple fault points is determined according to the distance of the clustering center, and the number of fault points is obtained, specifically including the following steps: Energy feature extraction is performed on the wave head data within the preset time window to establish a wave head feature vector; According to the wave head feature vector, initial clustering is performed to obtain a clustering center set; The distances between the center points in the clustering center set are calculated and compared with a clustering distance threshold to obtain the initial number of fault points; Sort the wave head energy of each cluster center based on the initial fault point quantity, and determine the primary and secondary fault points; According to the energy ratio and time relationship of the primary and secondary fault points, it is judged whether it is multiple reflections of the same fault, and the final fault point quantity is formed.

[0014] By adopting the above technical scheme, since the traveling wave signals generated when the power transmission line fails will have multiple wave heads, and these wave heads may come from different fault points or multiple reflections of the same fault point, the traditional wave head analysis method is difficult to distinguish the influence of multiple fault points and reflected waves; the application first extracts energy features from the wave head data in the preset time window, and constructs a wave head feature vector; then the feature vectors are used for initial clustering to obtain a cluster center set; then the distances between the cluster centers are calculated, and the initial fault point quantity is determined by comparing with the clustering distance threshold; then the wave head energy of each cluster center is sorted based on the initial fault point quantity to distinguish the primary and secondary fault points; finally, by analyzing the energy ratio and time relationship of the primary and secondary fault points, it is judged whether there is multiple reflections of the same fault, so as to determine the final fault point quantity; the accurate identification of multiple fault points is realized, and the fault points and reflected waves are effectively distinguished through energy ratio and time relationship analysis.

[0015] Optionally, the traveling wave arrival time difference is obtained by using a multi-terminal GPS synchronous clock, and an initial fault distance is calculated by applying a time difference positioning algorithm according to the propagation speed parameter, specifically including the following steps: Check the ratio of the two-end traveling wave arrival time difference to the theoretical propagation time of the line to obtain a time stamp legality verification result; According to the time stamp legality verification result, the clock difference of the two-end receivers is obtained and the time stamp is corrected to obtain a corrected time difference value; Based on the corrected time difference value, the propagation speed parameter is corrected in combination with the temperature and the line type to obtain an equivalent wave speed; The equivalent wave speed and the corrected time difference value are used to calculate the fault point position to obtain a fault distance value; According to the fault distance value and the line length, a boundary test is performed to form an initial fault distance.

[0016] By adopting the technical scheme, firstly, the ratio of the time difference of the two end traveling waves to the theoretical propagation time of the line is compared to verify the legitimacy of the time stamp; then, the clock difference of the two end receivers is calculated according to the verification result, and the time stamp is corrected to obtain an accurate time difference value; then, the propagation speed parameter is corrected in combination with the real-time temperature and the line type information to obtain the equivalent wave speed under the actual operation condition; then, the fault point position is calculated by using the corrected equivalent wave speed and the time difference value; finally, the boundary test is performed with the line length to ensure the validity of the fault distance value; by introducing the time stamp legitimacy verification and the multi-parameter correction mechanism, the clock error is effectively compensated, the influence of the temperature and the line type is considered, and the accuracy of the time difference positioning is improved.

[0017] Optionally, the time difference is transmitted to the central server through the communication network for comprehensive verification and alarm management, and specifically includes the following steps: An error model including line length error, time service error, wave speed error and terrain error is established to obtain error calculation parameters; According to the error calculation parameters, the overall uncertainty is calculated to obtain a standard deviation range; Based on the standard deviation range and the deviation of the current positioning result and the historical positioning result, a confidence index is calculated; According to the comparison result of the confidence index and the confidence determination threshold, it is determined whether multiple measurement averaging is needed; According to the multiple measurement averaging result and the fault type, alarm information is generated to form a fault response result.

[0018] By adopting the technical scheme, firstly, an integrated error model including line length error, time service error, wave speed error and terrain error is established to obtain error calculation parameters; then, the overall uncertainty is calculated according to these parameters to determine a standard deviation range; then, the current positioning result is compared with the historical positioning result, and the confidence index is calculated in combination with the standard deviation range; then, whether multiple measurement averaging is needed is determined by comparing the confidence index with the confidence determination threshold; finally, corresponding alarm information is generated according to the multiple measurement averaging result and the fault type; by introducing the multi-source error analysis and the confidence evaluation mechanism, the reliability of the positioning result is comprehensively evaluated, and the accuracy of the alarm decision is improved through multiple measurement averaging.

[0019] In a second aspect, the application provides a power transmission line fault monitoring and positioning system, comprising: A time sequence data acquisition module is configured to acquire original current signals and perform digital conversion to obtain time sequence data; A classification label acquisition module is configured to identify and classify fault events according to the time sequence data through threshold detection and feature extraction algorithms to obtain a classification label; a positioning result acquisition module configured to perform fault location positioning based on the classification label by using a traveling wave propagation model and time difference calculation, wherein the multiple wave front data in a preset time window are subjected to cluster analysis to determine single / multiple fault points, and a positioning result is obtained; a fault response module configured to transmit the positioning result to a central server through a communication network for comprehensive verification and alarm management to form a fault response.

[0020] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the power transmission line fault monitoring and positioning method when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the power transmission line fault monitoring and positioning method.

[0022] In summary, the present application has at least one of the following beneficial technical effects: The present application first digitizes the original current signal to obtain time series data, then identifies and classifies fault events by threshold detection and feature extraction algorithm to obtain the classification label of fault type; then, based on the classification label, a traveling wave propagation model is established and time difference calculation is performed, and at the same time, multiple wave front data in a preset time window are subjected to cluster analysis, and the cluster result is used to determine whether it is a single point fault or a multiple point fault; finally, the positioning result is transmitted to a central server for comprehensive verification and alarm management to form the final fault response; by introducing time series data analysis and cluster judgment mechanism, not only the fault type can be accurately identified, but also the single point and multiple point fault conditions can be effectively distinguished, and the reliability of the positioning result is improved through the comprehensive verification of the central server; The present application first applies an adaptive threshold algorithm based on historical data statistics to remove outliers from time series data, ensuring the reliability of the detection result; then, feature data is extracted from the abnormal event detection result; then, a multi-dimensional feature extraction algorithm is used for fault classification, including identifying lightning fault by the polarity, amplitude ratio and time domain feature of the traveling wave signal, analyzing lightning shielding or counterattack by multi-peak waveform and reflection coefficient, identifying grounding and short circuit fault by combining zero sequence component and phase-to-phase imbalance calculation, and distinguishing transient and permanent fault according to signal attenuation characteristics and duration; finally, self-diagnosis and noise correction are performed on the fault classification result to form the final classification label; by introducing adaptive threshold and multi-dimensional feature analysis mechanism, combined with the characteristic parameters of different fault types, the accurate identification and classification of various faults are realized, and the accuracy of the classification result is improved through self-diagnosis and noise correction; Since the traveling wave signals generated by the transmission line fault will appear multiple wave heads, and these wave heads may come from different fault points or multiple reflections of the same fault point, the traditional wave head analysis method is difficult to distinguish the influence of multiple fault points and reflected waves; the application firstly extracts the energy features of the wave head data in the preset time window, constructs the wave head feature vector; then uses these feature vectors for initial clustering, obtains the clustering center set; then calculates the distance between each clustering center, compares with the clustering distance threshold to determine the initial fault point number; then sorts the wave head energy of each clustering center based on the initial fault point number, distinguishes the primary and secondary fault points; finally, by analyzing the energy ratio and time relationship of the primary and secondary fault points, it is judged whether there is multiple reflection of the same fault, so as to determine the final fault point number; the accurate identification of multiple fault points is realized, and the fault points and reflected waves are effectively distinguished through energy ratio and time relationship analysis. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 2 is a flowchart of step S200 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 3 is a flowchart of step S210 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 4 is a flowchart of step S300 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 5 is a flowchart of step S320 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 6 is a flowchart of step S330 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 7 is a flowchart of step S400 in a transmission line fault monitoring and positioning method according to an embodiment of the application; Figure 8 is a module diagram of a transmission line fault monitoring and positioning system according to an embodiment of the application; Figure 9 is an internal structure diagram of an electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0024] The terminology used in the following description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the description of the embodiments and the appended claims herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It also will be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0025] Hereinafter, the terms "first", "second" are used only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0026] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0027] In a first aspect, the present application provides a power transmission line fault monitoring and positioning method, referring to Figure 1 , comprising the following steps: S100, collecting original current signals and performing digital conversion to obtain time sequence data.

[0028] In this embodiment, the original current signal includes power frequency fault current and traveling wave current signal; digital conversion refers to the process of converting analog signal to digital quantity; time sequence data refers to the sequence of sampling data recorded in time order, containing time stamp and corresponding sampling value information.

[0029] Specifically, the monitoring terminal is directly installed on the high-voltage transmission conductor, and a double Rogowski coil sensor is used to collect power frequency fault current and traveling wave current signal. The frequency response range of the traveling wave current is 1 kHz to 1 MHz, the sampling rate is not less than 1 MHz, the single conductor measurement range (peak value) covers 5A to 5000A, and the continuous recording time is 1000 microseconds. The terminal is built-in double-channel high-precision AD conversion chip, integrated GPS module and constant temperature crystal oscillator, and the time error is controlled within 0.1 microseconds. The frequency response range of the power frequency current collection is 1 Hz to 1 kHz, the sampling rate is not less than 2 kHz, the measurement range (effective value) is 4A to 5000A, and the recording time is 500 milliseconds. The terminal real-time monitors the line current change, and automatically switches to high sampling mode when detecting mutation, and the data is temporarily stored in the local buffer.

[0030] S200, according to the time sequence data, the threshold detection and feature extraction algorithm are used for fault event identification and classification, and the classification label is obtained.

[0031] In this embodiment, threshold detection refers to a method of judging signal characteristics by a preset criterion; feature extraction refers to extracting characteristic quantities representing fault properties from original signals; and classification label refers to identification coding of fault types.

[0032] Specifically, the system applies an adaptive threshold algorithm to analyze current amplitude, duration, and frequency spectrum, detects abnormal events by comparing the deviation of the current signal from historical baseline data, and triggers fault identification when the deviation exceeds a preset threshold. Key features including waveform peak, rising slope, harmonic component, and energy distribution are extracted, and machine learning models or rule-based algorithms are used for classification.

[0033] S300, based on the classification label, the traveling wave propagation model and the time difference calculation are used for fault location positioning, wherein the multiple wave head data in the preset time window are clustered and analyzed to determine single / multiple fault points, and the positioning result is obtained.

[0034] In this embodiment, the traveling wave propagation model refers to a mathematical model describing the propagation law of electromagnetic waves on a transmission line; and the time difference calculation refers to a method of positioning by using the time difference of fault waves arriving at both ends of the line.

[0035] Specifically, the propagation speed of the traveling wave on the line is first calculated, and then based on the classification label, the time difference of multi-terminal synchronous data is used to adopt a double-end or multi-end time difference positioning algorithm, and the fault point distance is calculated by the formula d = (v * Δt) / 2, wherein v is the wave speed and Δt is the time difference between the first and last waves, and the error is controlled within ±300 meters. For complex faults such as lightning-induced reflected waves, the algorithm introduces waveform matching and iterative correction to eliminate multi-path interference and improve the reliability of interval positioning to 99%.

[0036] S400, according to the positioning result, through the communication network to the central server for comprehensive verification and alarm management, forming the fault response.

[0037] In this embodiment, comprehensive verification refers to the process of reliability evaluation of the positioning result; and alarm management refers to a rule system for determining processing priority and notification method according to fault properties and verification results.

[0038] Specifically, the positioning output, classification label and related data packets are transmitted in real time to the monitoring server through the 4G communication network, and packet sending is adopted to prevent interruption. Fault tripping alarm is completed within 5 minutes, and diagnosis alarm does not exceed 30 minutes. The server side performs comprehensive verification, cross-compares multi-terminal data to eliminate single-point errors, generates alarm signals, and pushes them through SMS, WeChat or WEB interface. The data storage logic enables local caching when communication is interrupted, and automatically supplements transmission after recovery.

[0039] In one embodiment, refer to Figure 2In step S200, according to the time series data, fault event identification and classification are performed through threshold detection and feature extraction algorithms to obtain a classification label, and the specific steps include the following steps. S210, according to the time series data, an adaptive threshold algorithm based on historical data statistics is applied to remove outliers to obtain an abnormal event detection result.

[0040] In this embodiment, the historical data statistics refer to probability distribution analysis of past normal operation data; the adaptive threshold algorithm refers to a method of dynamically adjusting the judgment standard according to the data characteristics; the outliers refer to data points that significantly deviate from the normal distribution; and the abnormal event detection result includes three basic attributes of abnormal occurrence time, duration and abnormal degree.

[0041] S220, according to the abnormal event detection result, feature data is extracted.

[0042] In this embodiment, the feature data refers to a set of key parameters that can represent the nature of the fault; the waveform feature includes the amplitude, polarity, rise time and peak value of the signal; the frequency domain feature includes the main frequency component, harmonic content and energy distribution; and the statistical feature includes the mean, variance, skewness and kurtosis.

[0043] Specifically, a feature extraction mapping table is established, and corresponding feature extraction schemes are set for different types of abnormal events. First, waveform analysis is performed in the time domain to extract four basic features of the signal, including peak value, rising slope, duration and waveform shape. Then, the frequency spectrum is calculated by fast Fourier transform to extract the main frequency component and energy distribution feature. For multi-phase data, inter-phase relationship features are calculated, including phase difference, amplitude ratio and zero sequence component. The system organizes all extracted features into a feature vector according to a predetermined format as input for subsequent classification. The feature extraction process adopts a segmented processing strategy, and features are calculated for the three stages before, during and after the abnormal event to capture the dynamic characteristics of the fault development process.

[0044] S230, based on the feature data, a preset feature extraction algorithm is used to classify the fault type to obtain a fault classification result.

[0045] The preset feature extraction algorithm includes judging lightning strike fault through the polarity, amplitude ratio and time domain features of the traveling wave signal, judging lightning shielding or counterattack through multi-peak waveform and reflection coefficient analysis, judging grounding and short circuit fault through zero sequence component and inter-phase imbalance calculation, and distinguishing transient fault and permanent fault according to signal attenuation characteristics and duration.

[0046] Specifically, the system establishes a fault feature template library to store the standard feature patterns of typical fault cases. The classification process adopts a multi-level judgment mechanism: first, lightning fault is identified through the polarity and amplitude characteristics of the traveling wave signal, and matching is performed based on a preset criterion table; then, the peak value number and reflection characteristics of the waveform are analyzed to determine the specific lightning type; for non-lightning faults, the zero sequence component and phase imbalance are calculated to determine the fault nature; finally, the signal attenuation rate and duration are used to determine the fault persistence. The system calculates a matching degree score for each fault type, and selects the type with the highest score as the classification result.

[0047] S240, self-diagnosis and noise correction are performed on the fault classification result to form a final classification label.

[0048] In this embodiment, self-diagnosis refers to the automatic evaluation of the reliability of the classification result by the system; noise correction refers to eliminating the influence of environmental interference on the classification result; and the final classification label includes four attributes: fault type, confidence index, time information, and associated features.

[0049] Specifically, a classification result evaluation rule library is established, including three dimensions: feature consistency test, time sequence logic verification, and environmental factor influence evaluation. The system first checks whether the correlation between features conforms to physical laws to verify the rationality of the classification result. Then, a digital filtering algorithm is applied to denoise the original features, and feature matching is performed again to verify the stability of the classification. Finally, a classification label containing complete information is generated, recording the fault occurrence time, fault type, confidence score, and key feature identifier, providing input basis for subsequent positioning calculation.

[0050] In one embodiment, with reference to Figure 3 In step S210, based on the time series data, an adaptive threshold algorithm based on historical data statistics is applied to remove outliers to obtain an abnormal event detection result, including the following steps: S211, time stamp legality check is performed on the time series data, and the theoretical propagation time is calculated based on the line length and wave speed to obtain a time stamp validity judgment result.

[0051] In this embodiment, time stamp legality refers to whether the time label of the sampling data conforms to the physical law; the theoretical propagation time refers to the time required for electromagnetic waves to propagate on the transmission line; and the time stamp validity judgment result includes three attributes: time stamp validity identifier, error range, and confidence.

[0052] Specifically, a timestamp verification table is established, including line structure parameters (length, material, erection height) and wave speed calculation formula. The system first checks the continuity and incrementality of the timestamp, and eliminates obviously incorrect time markers. Then, according to the line length and nominal wave speed calculation theoretical propagation delay, a time window criterion is established. The timestamp of each data point is compared with the theoretical delay, and the sampling points outside the reasonable range are marked. Finally, a judgment result including validity identification, time error and credibility score is generated.

[0053] S212, based on the timestamp validity judgment result, the history record is used to establish the time difference expectation value and standard deviation, and the statistical characteristic parameter is obtained.

[0054] In this embodiment, the history record refers to the time difference data set in the past normal operation state; the time difference expectation value refers to the central tendency of the statistical distribution; the standard deviation refers to the measure of data dispersion; and the statistical characteristic parameter includes mean, variance and distribution form three basic quantities.

[0055] Specifically, a time difference characteristic database is established in advance to record the time difference distribution characteristics under different operating conditions. The system selects the corresponding historical data set according to the current operating state, and calculates the mean and standard deviation of the time difference. The sliding time window method is used to update the statistical parameters, and the window length is dynamically adjusted according to the data stability. The skewness and kurtosis are calculated to describe the distribution form, and a complete statistical characteristic model is established.

[0056] S213, according to the statistical characteristic parameter, the outlier is removed, and the outlier judgment result is obtained.

[0057] In this embodiment, outlier removal refers to identifying and removing data points that significantly deviate from the normal distribution; the judgment rule includes amplitude limit, change rate limit and duration limit; and the outlier judgment result includes abnormal point position, deviation degree and duration.

[0058] Specifically, a multi-level outlier judgment rule table is established, and a judgment threshold based on statistical characteristics is set. First, check whether the data point falls within the normal distribution interval, and mark the points outside the range of plus or minus three times the standard deviation of the mean. Then, analyze the change rate between adjacent points to identify the mutation point. Finally, the abnormal duration is evaluated to distinguish between transient disturbance and sustained anomaly. The system calculates the comprehensive score of each abnormal point, and records its position and characteristic information.

[0059] S214, according to the outlier judgment result and the preset sample number determination threshold, the detection data is marked to form an abnormal event detection result.

[0060] In this embodiment, the sample quantity determination threshold refers to the minimum sample size requirement for triggering an abnormal event; the detection data label refers to the classification identification of the abnormal attributes of the data points; and the abnormal event detection result contains three elements of event start and end time, abnormality degree, and sample quantity.

[0061] Specifically, an abnormal event determination table is established to specify the minimum sample size requirements for different types of abnormalities. The system first counts the number of consecutive abnormal points and compares it with the preset threshold. When the number of abnormal points exceeds the threshold, the related data segment is marked as an abnormal event. The characteristic parameters of each abnormal event are calculated, including duration, average deviation, and peak deviation. Finally, an abnormal event detection report containing complete time information and characteristic description is generated.

[0062] In one embodiment, with reference to Figure 4 , in step S300, based on the classification label, the fault location is located using the traveling wave propagation model and the time difference calculation, specifically including the following steps: S310, calculate the traveling wave propagation speed according to the line parameters, establish the traveling wave propagation model, and obtain the propagation speed parameter.

[0063] In this embodiment, the line parameters include conductor type, erection height, and tower structure; the traveling wave propagation model refers to a mathematical model describing the propagation characteristics of electromagnetic waves on a transmission line; and the propagation speed parameter includes nominal wave speed, temperature coefficient, and attenuation coefficient.

[0064] Specifically, a line parameter database is established to record the propagation characteristics of different types of lines. For overhead lines, the nominal wave speed is set to 298 meters per microsecond, and the temperature coefficient is taken as negative 0.4x10⁻³ per degree Celsius; for cable lines, the nominal wave speed is set to 200 meters per microsecond. For mixed lines, a segmented calculation method is used to divide the line into overhead and cable segments, and the propagation time delay is calculated respectively, and finally the equivalent wave speed is obtained. The system obtains real-time temperature through an online temperature measuring device and corrects the wave speed according to the temperature coefficient. For different conductor types, an attenuation coefficient lookup table is established for subsequent distance correction.

[0065] S320, based on the classification label, select the corresponding waveform feature and positioning algorithm, wherein for single fault, use the double-end time difference positioning algorithm, for complex fault, perform multi-wave head clustering analysis in a preset time window, judge single / multiple fault points according to the cluster center distance, and obtain the number of fault points.

[0066] In this embodiment, the waveform feature refers to the amplitude, polarity, and rise time of the traveling wave signal; the positioning algorithm includes double-end time difference method and multi-wave head clustering method; and the preset time window refers to the time range for wave head analysis, which is set to 1 millisecond.

[0067] Specifically, the system pre-establishes a waveform feature template library, and stores standard waveform features for different fault types. When a fault is detected, the corresponding feature template is first selected according to the classification label. For a single fault, a double-end time difference positioning algorithm is directly applied; for the case where multiple fault points may exist, the wave head data is collected within a preset time window, the wave head feature vector is extracted, and a distance-based clustering algorithm is used to classify similar wave heads. By calculating the distance between the cluster centers, when the distance exceeds a preset threshold, it is determined as a multiple fault point.

[0068] S330, the multi-terminal GPS synchronous clock is used to obtain the traveling wave arrival time difference, and the time difference positioning algorithm is applied to calculate the initial fault distance according to the propagation speed parameter.

[0069] In this embodiment, the GPS synchronous clock refers to a time service system that provides a unified time reference for each measurement terminal; the traveling wave arrival time difference refers to the time interval of the fault wave arriving at both ends of the line; and the time difference positioning algorithm refers to a mathematical method for calculating the fault distance based on the time difference.

[0070] Specifically, a GPS time service module is installed at both ends of the line, and a dual-frequency receiver is used to provide nanosecond-level time synchronization. The system records the absolute time of the traveling wave arriving at each terminal, and obtains the time difference by subtracting the time stamp. According to the full length of the line and the corrected propagation speed, the double-end positioning formula is used to calculate the fault point distance. For multiple fault points, the position of each wave head cluster is calculated.

[0071] S340, according to the initial fault distance, attenuation compensation and parameter correction are performed for long-distance lines to obtain a corrected fault distance value.

[0072] In this embodiment, attenuation compensation refers to considering the energy loss of the signal during transmission; parameter correction refers to correcting the calculation parameters according to the actual operating environment; and the corrected fault distance value includes distance value and uncertainty.

[0073] Specifically, an attenuation correction model is established, considering the influence of line loss on wave speed. For long-distance lines, the system calculates the transmission loss according to the line length and attenuation coefficient, and compensates the initial distance. An error correction curve is established through historical positioning data to realize adaptive parameter correction. Finally, the distance value including the measurement error range is output.

[0074] S350, the corrected fault distance value, the number of fault points and the classification label are packaged to form a positioning result.

[0075] In this embodiment, the fault distance value refers to the physical distance from the fault point to the end point of the line; the number of fault points refers to the number of independent fault positions detected; and the positioning result includes distance value, uncertainty and reliability.

[0076] Specifically, a positioning result data structure is established, containing four fields of fault type code, distance value, measurement error and fault point number. The system fills the corrected fault distance value into the data structure and calculates the comprehensive confidence index. The result is packaged into a standard message through a preset data format, a time stamp and a check code are added to ensure data integrity. Finally, a positioning result report containing complete information is generated.

[0077] In one embodiment, with reference to Figure 5 In step S320, multi-wave head clustering analysis is performed within a preset time window, single / multi fault points are judged according to the clustering center distance, and the number of fault points is obtained, which specifically includes the following steps: S321, energy feature extraction is performed on the wave head data within the preset time window, and a wave head feature vector is established.

[0078] In this embodiment, the preset time window refers to a fixed time period for wave head analysis, which is set to 1 millisecond after the fault occurs; the energy feature refers to the amplitude, polarity, rise time and duration of the wave head signal; and the wave head feature vector refers to a standard data structure formed by organizing multiple feature quantities.

[0079] Specifically, a wave head feature extraction table is established, and the feature calculation method is defined. First, the original sampling data is digitally filtered, and a Butterworth high-pass filter is selected to remove the power frequency component. Then, the wave head is searched within the preset time window, the first derivative of the signal is calculated to identify the mutation point, and the starting position of each wave head is recorded. Four basic features are calculated for each wave head: the peak amplitude is obtained by maximum value statistics, the polarity is determined by the first mutation direction, the rise time is calculated by the duration of the waveform rising edge, and the duration is determined by the time when the waveform decays to one quarter of the peak value. Finally, the four features are combined into a feature vector as input data for subsequent clustering analysis.

[0080] S322, initial clustering is performed according to the wave head feature vector, and a clustering center set is obtained.

[0081] In this embodiment, the initial clustering refers to the process of preliminarily grouping the wave head feature vectors; the clustering center refers to the representative feature vector of each group; and the clustering center set refers to the data set composed of all clustering centers.

[0082] Specifically, a distance-based clustering rule table is established to predefine the distance calculation method in the feature space. An improved K-means clustering algorithm is adopted, and the initial number of clusters is set to 2. First, the similarity between wave heads is calculated by the Euclidean distance of feature vectors, and similar wave heads are classified into the same category. The center vector of each category is calculated, including four parameters of average amplitude, dominant polarity, average rise time and average duration. The category division is continuously adjusted through iterative optimization until the clustering center is stable or the maximum iteration number is reached. Finally, a set containing multiple clustering centers is obtained, and each center represents a potential fault feature.

[0083] S323, calculate the distance between each center point in the clustering center set, and compare it with the clustering distance threshold to obtain the initial fault point number.

[0084] In this embodiment, the center point distance refers to the Euclidean distance of the clustering center in the feature space; the clustering distance threshold refers to the criterion for judging whether different clusters belong to the same fault; and the initial fault point number refers to the number of potential fault points obtained by distance comparison.

[0085] Specifically, a distance judgment standard table is established, which contains weight coefficients of different feature dimensions. The weighted Euclidean distance between any two clustering centers is calculated, and the weight coefficient reflects the importance of each feature quantity: the amplitude weight is 0.4, the polarity weight is 0.3, the rise time weight is 0.2, and the duration weight is 0.1. The calculated distance value is compared with the preset clustering distance threshold, and when the distance is greater than the threshold, it is considered to belong to different fault points. By traversing all center point pairs, the number of independent clusters that meet the conditions is counted to obtain the initial fault point number.

[0086] Further, in order to improve the adaptability of the system to different working conditions, the application establishes an intelligent threshold adjustment mechanism based on historical data and operating state. First, a working condition feature library is constructed to divide the line operating conditions into three basic states of normal load, heavy load and light load, and record three key parameters of temperature, humidity and load rate under each state. Then, a working condition-threshold mapping table is established to obtain the optimal value range of the clustering distance threshold under different working conditions through statistical analysis of historical fault data. In real-time operation, the system collects working condition parameters every 1 hour, calculates the membership degree of the current working condition through fuzzy rules, and realizes online identification of the working condition. Based on the identification result, the system queries the working condition-threshold mapping table to obtain the reference threshold, and dynamically fine-tunes the reference threshold combined with the fault location accuracy rate in the last 24 hours. When the positioning accuracy rate is lower than the preset value, the system automatically reduces the threshold adjustment step to improve the stability of threshold updating.

[0087] S324, based on the initial fault point number, sort the wave head energy of each clustering center to determine the primary and secondary fault points.

[0088] In this embodiment, the wave head energy refers to the integrated energy value of the wave head signal; the primary and secondary fault points refer to the sequence of fault points sorted by energy size; and the energy sorting refers to the process of priority division of the fault points according to the energy value.

[0089] Specifically, an energy calculation rule table is established to define the calculation method of the energy value. For each cluster center, the energy value is obtained by calculating the square integral of the representative waveform, and the integral interval is twice the duration of the wave head. All cluster centers are sorted in descending order of energy value, and the one with the largest energy value is defined as the primary fault point, and the rest are secondary fault points. The time sequence of each fault point is recorded for subsequent reflection judgment. The system outputs an ordered fault point list containing energy values and timestamps.

[0090] S325, according to the energy ratio and time relationship of the primary and secondary fault points, whether it is multiple reflections of the same fault is judged, and the final number of fault points is formed.

[0091] In this embodiment, the energy ratio refers to the ratio of the energy of the secondary fault point to the primary fault point; the time relationship refers to the sequence of the occurrence of the fault points; the reflection judgment refers to the process of identifying multiple reflection waves; and the final number of fault points refers to the actual number of fault points after eliminating reflections.

[0092] Specifically, a reflection wave identification table is established, which contains typical reflection coefficients and time delay characteristics. First, the energy ratio of each secondary fault point to the primary fault point is calculated, which is compared with the standard reflection coefficient. Then, the time interval of the fault points is analyzed, and the theoretical reflection time delay is calculated through the line length and wave speed. When the energy ratio and time interval both meet the reflection characteristics, the secondary fault point is marked as a reflection wave. Finally, the number of fault points that are not marked as reflection waves is counted as the final number of fault points output.

[0093] In one embodiment, with reference to Figure 6 , in step S330, the traveling wave arrival time difference is obtained by using the multi-terminal GPS synchronous clock, and the initial fault distance is calculated by using the time difference positioning algorithm according to the propagation speed parameter, which specifically includes the following steps: S331, check the ratio of the two-end traveling wave arrival time difference to the theoretical propagation time of the line to obtain the timestamp legality verification result.

[0094] S332, according to the timestamp legality verification result, the clock difference of the two-end receivers is obtained and the timestamp is corrected to obtain the corrected time difference value.

[0095] S333, based on the corrected time difference value, the propagation speed parameter is corrected in combination with the temperature and the line type to obtain the equivalent wave speed.

[0096] In this embodiment, temperature correction refers to a compensation mechanism considering the influence of temperature on wave speed; line type refers to the classification of overhead line or cable line; and equivalent wave speed refers to the actual propagation speed considering various influencing factors.

[0097] Specifically, a wave speed correction database is established, containing temperature coefficients and line parameters. For overhead lines, the correction coefficient of wave speed with temperature change is -0.4x10-3 per degree Celsius. For hybrid lines, a segmented calculation method is used to divide the line into overhead and cable segments, and the equivalent wave speed is calculated by weighted average. The system obtains real-time temperature data through an online temperature measuring device and calculates the actual wave speed by applying the correction formula.

[0098] Further, first, a wave speed calculation class is established, containing a wave speed correction database, which stores three basic parameters of nominal wave speed, temperature coefficient and reference temperature of overhead lines and cables. For single type line segments, the actual wave speed is calculated by the temperature correction formula: actual wave speed = nominal wave speed x (1 + temperature coefficient x (current temperature - reference temperature)). For hybrid lines, the line is divided into multiple line segments, each containing type and length information. The system calculates the equivalent wave speed by accumulating the propagation time of each segment, first calculates the actual wave speed of each segment, then divides the segment length by the actual wave speed to obtain the propagation time of the segment, and finally divides the total length by the total propagation time to obtain the equivalent wave speed. At the same time, the system also considers the influence of high altitude, and when the line altitude exceeds the preset value, the equivalent wave speed is corrected for height, and the correction coefficient changes linearly with altitude. The final output equivalent wave speed is used as the basic parameter for subsequent positioning calculation.

[0099] S334, using the equivalent wave speed and the corrected time difference value, calculating the fault point position to obtain the fault distance value.

[0100] In this embodiment, the fault point position refers to the physical distance from the fault point to one end of the line; the fault distance value is the initial result of positioning calculation; and the calculation accuracy is the uncertainty of the positioning result.

[0101] Specifically, a positioning calculation rule library is established, containing calculation models under different working conditions. The equivalent wave speed and the corrected time difference are substituted into the calculation by using the double-end positioning formula. For overhead lines, the standard formula is directly applied; for hybrid lines, a segmented positioning method is used.

[0102] S335, boundary test is performed according to the fault distance value and the line length to form an initial fault distance.

[0103] In this embodiment, boundary test refers to verifying whether the fault distance is within the physically possible range; line length refers to the actual length of the transmission line; and initial fault distance refers to the effective positioning result after verification.

[0104] In one embodiment, reference is made toFigure 7 In step S400, the data is transmitted to the central server through the communication network for comprehensive verification and alarm management, including the following steps: S410, an error model containing line length error, time error, wave speed error and terrain error is established to obtain error calculation parameters.

[0105] In this embodiment, the line length error refers to the deviation of the actual length from the design value; the time error refers to the time synchronization error of the GPS time system; the wave speed error refers to the uncertainty of the wave speed calculation; the terrain error refers to the path deviation caused by the change of the terrain; the error calculation parameters contain the standard deviation and the weight coefficient of each error source.

[0106] S420, according to the error calculation parameters, the overall uncertainty is calculated to obtain the standard deviation range.

[0107] In this embodiment, the overall uncertainty refers to the result of the comprehensive action of each error source; the standard deviation range is the possible distribution interval of the positioning result; the error propagation refers to the influence mode of each error source on the final result.

[0108] Specifically, an error propagation calculation model is established, and the error sum of squares method is used. First, each error source is converted to the same physical unit, and then the comprehensive standard deviation is calculated according to the error propagation law. The weight coefficients of different error sources are set as follows: the line length error weight is 0.3, the time error weight is 0.3, the wave speed error weight is 0.2, and the terrain error weight is 0.2. The overall standard deviation is obtained by the weighted sum of squares method, and the 95% confidence interval is calculated.

[0109] S430, based on the standard deviation range and the deviation of the current positioning result from the historical positioning result, the confidence index is calculated.

[0110] S440, according to the comparison result of the confidence index and the confidence determination threshold, it is determined whether multiple measurements are needed.

[0111] In this embodiment, the confidence determination threshold refers to the critical value of triggering repeated measurement; the multiple measurements refer to repeated positioning of the same fault point; the measurement average refers to statistical processing of multiple measurement results.

[0112] S450, according to the multiple measurement average result and the fault type, the alarm information is generated to form the fault response result.

[0113] In this embodiment, the multiple measurement average result refers to the final positioning value after statistical processing; the fault type refers to the fault nature determined according to the waveform characteristics; the fault response result contains three elements of fault position, type and severity.

[0114] Specifically, a fault alarm configuration table is established to define alarm levels of different types of faults. The system generates alarm information in a standard format according to the final positioning result, including four basic fields of fault occurrence time, position coordinates, fault type and measurement error range. At the same time, the alarm priority is determined according to the fault type, and the alarm information is pushed to the relevant personnel through the preset communication channel. Finally, a fault response report containing complete information is generated for fault analysis and processing.

[0115] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] In a second aspect, the present application provides a power transmission line fault monitoring and positioning system. The power transmission line fault monitoring and positioning system of the present application will be described below in combination with the power transmission line fault monitoring and positioning method described above.

[0117] Reference Figure 8 A power transmission line fault monitoring and positioning system, comprising: a time series data acquisition module for acquiring and digitizing original current signals to obtain time series data; a classification label acquisition module for identifying and classifying fault events through threshold detection and feature extraction algorithms based on the time series data to obtain a classification label; a positioning result acquisition module for positioning the fault location based on the classification label using a traveling wave propagation model and time difference calculation, wherein the multiple wave head data in the preset time window are subjected to cluster analysis to determine single / multiple fault points to obtain a positioning result; a fault response module for transmitting the positioning result to a central server through a communication network for comprehensive verification and alarm management to form a fault response.

[0118] In one embodiment, the present application provides an electronic device, which can be a server, and its internal structure diagram can be as shown in Figure 9 The electronic device includes a processor, a memory and a network interface connected through a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a power transmission line fault monitoring and positioning method.

[0119] Those skilled in the art can understand that Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] In one embodiment, an electronic device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The above-mentioned computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not as a limitation, RAM can be in various forms such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0122] The above are the preferred embodiments of the present application, which do not limit the protection scope of the present application, therefore: any equivalent changes made on the structure, shape, principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A method of transmission line fault monitoring and location, characterized by, The method comprises the following steps: Collecting original current signals and performing digital conversion to obtain time series data; According to the time series data, performing fault event identification and classification through threshold detection and feature extraction algorithms to obtain classification labels; Based on the classification labels, using a traveling wave propagation model and time difference calculation to locate the fault position, wherein a plurality of wave head data in a preset time window are subjected to cluster analysis to determine single / multiple fault points to obtain a positioning result; According to the positioning result, transmitting to a central server through a communication network for comprehensive verification and alarm management to form a fault response.

2. The method of claim 1, wherein, According to the time series data, performing fault event identification and classification through threshold detection and feature extraction algorithms to obtain classification labels, specifically comprising the following steps: According to the time series data, applying an adaptive threshold algorithm based on historical data statistics to remove outliers to obtain an abnormal event detection result; According to the abnormal event detection result, extracting feature data; Based on the feature data, using a preset feature extraction algorithm to classify fault types to obtain a fault classification result, wherein the preset feature extraction algorithm includes determining lightning strike faults through the polarity, amplitude ratio and time domain characteristics of the traveling wave signal, determining lightning shielding or counterattack through multi-peak waveform and reflection coefficient analysis, determining grounding and short circuit faults through zero sequence component and phase-to-phase imbalance calculation, and distinguishing transient faults and permanent faults according to signal attenuation characteristics and duration; Performing self-diagnosis and noise correction on the fault classification result to form a final classification label.

3. The method of claim 2, wherein, According to the time series data, applying an adaptive threshold algorithm based on historical data statistics to remove outliers to obtain an abnormal event detection result, specifically comprising the following steps: Performing timestamp legality check on the time series data, calculating the theoretical propagation time according to the line length and wave speed to obtain a timestamp validity judgment result; Based on the timestamp validity judgment result, using historical records to establish time difference expectation value and standard deviation to obtain statistical characteristic parameters; According to the statistical characteristic parameters, removing outliers to obtain an outlier judgment result; According to the outlier judgment result and a preset sample quantity judgment threshold, marking the detection data to form an abnormal event detection result.

4. The method of claim 1, wherein, Based on the classification labels, using a traveling wave propagation model and time difference calculation to locate the fault position, specifically comprising the following steps: Calculating the traveling wave propagation speed according to the line parameters to establish a traveling wave propagation model to obtain propagation speed parameters; Selecting corresponding waveform characteristics and positioning algorithms based on the classification labels, wherein for single fault, a double-end time difference positioning algorithm is used, and for complex faults, a multi-wave head cluster analysis is performed in a preset time window, and the number of fault points is determined according to the cluster center distance to obtain the number of fault points; Using a multi-terminal GPS synchronous clock to obtain the traveling wave arrival time difference, and according to the propagation speed parameters, applying a time difference positioning algorithm to calculate the initial fault distance; According to the initial fault distance, performing attenuation compensation and parameter correction for long-distance lines to obtain a corrected fault distance value; The modified fault distance value, the fault point number and the classification label are packaged to form a positioning result.

5. The method of claim 4, wherein, The multi-wave head clustering analysis is performed in a preset time window, the single / multi fault point is judged according to the clustering center distance, the fault point number is obtained, and the specific steps include the following steps: Energy feature extraction is performed on the wave head data in the preset time window, and a wave head feature vector is established; Initial clustering is performed according to the wave head feature vector, and a clustering center set is obtained; The distances between the center points in the clustering center set are calculated and compared with a clustering distance threshold to obtain an initial fault point number; Based on the initial fault point number, the wave head energy of each clustering center is sorted to determine the primary and secondary fault points; According to the energy ratio and time relationship of the primary and secondary fault points, it is judged whether it is the same fault multiple reflections to form the final fault point number.

6. The method of claim 4, wherein, The traveling wave arrival time difference is obtained by using the multi-terminal GPS synchronous clock, and the initial fault distance is calculated by applying the time difference positioning algorithm according to the propagation speed parameter, and the specific steps include the following steps: The ratio of the two-end traveling wave arrival time difference to the theoretical propagation time of the line is checked to obtain a timestamp legality verification result; According to the timestamp legality verification result, the receiver clock difference of both ends is obtained and the timestamp is corrected to obtain a modified time difference value; Based on the modified time difference value, the propagation speed parameter is corrected in combination with the temperature and the line type to obtain the equivalent wave speed; The fault point position is calculated by using the equivalent wave speed and the modified time difference value to obtain the fault distance value; The boundary test is performed according to the fault distance value and the line length to form the initial fault distance.

7. The method of claim 1, wherein, The comprehensive verification and alarm management are transmitted to the central server through the communication network, and the specific steps include the following steps: An error model including line length error, time error, wave speed error and terrain error is established to obtain error calculation parameters; According to the error calculation parameters, the overall uncertainty is calculated to obtain a standard deviation range; Based on the standard deviation range and the deviation of the current positioning result and the historical positioning result, a confidence index is calculated; According to the comparison result of the confidence index and the confidence determination threshold, it is determined whether multiple measurements are needed to be averaged; According to the multiple measurement average result and the fault type, alarm information is generated to form a fault response result.

8. A power line fault monitoring and locating system, characterized by It includes: A time series data acquisition module for acquiring original current signals and performing digital conversion to obtain time series data; A classification label acquisition module for identifying and classifying fault events according to the time series data through threshold detection and feature extraction algorithm to obtain a classification label; A positioning result acquisition module for positioning the fault position based on the classification label by using the traveling wave propagation model and time difference calculation, wherein the multiple wave head data in the preset time window are clustered to judge the single / multi fault point to obtain the positioning result; A fault response module for transmitting the positioning result to the central server through the communication network for comprehensive verification and alarm management to form a fault response.

9. An electronic device, comprising: A computer program product comprising a memory, a processor, and a computer program stored on the memory and loadable on the processor, the processor implementing the steps of the method for monitoring and locating a fault on a power transmission line according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program product, when executed by the processor, implements the steps of the method for monitoring and locating a fault on a power transmission line according to any one of claims 1 to 7.

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