Power transmission line fault location method and device based on bimodal traveling wave fusion

Abstract

The invention relates to a power system power transmission line fault location technology, and discloses a power transmission line fault location method and device based on bimodal traveling wave fusion. According to the method, current and voltage signals are collected through multiple channels, high-frequency components are extracted through time synchronization and wavelet decomposition, wave head candidates are recognized through modulus maximum detection and morphological processing, and a real wave head is determined by combining a dynamic threshold value and multiple channel association. A fault distance is calculated through a double-end wave head timestamp, the distance measurement precision is improved according to wave velocity correction, a fault position is mapped into latitude and longitude coordinates, GIS data is superposed to mark a fault point and a nearest tower, a navigation file supporting off-line positioning is generated, and the navigation file is pushed to first-aid repair personnel through a notification module. According to the invention, high precision, strong robustness and visualization of fault location are realized, adaptability and first-aid repair efficiency of fault location are improved, and the method has good engineering application value.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring and diagnosis technology, and in particular to a method and device for fault location of transmission lines based on dual-mode traveling wave fusion. Background Technology

[0002] Fault location technology for transmission lines is a crucial aspect of power system operation and maintenance. Its goal is to quickly and accurately locate fault points to shorten repair time and improve power supply reliability. With the continuous expansion of power grids and increasing reliability requirements, traditional fault location methods have undergone the following main development stages: The first stage, impedance-based fault location methods: Early fault location generally used current and voltage impedance methods. By measuring the current and voltage changes during a fault, combined with line characteristic parameters, the impedance value between the fault point and the measuring end was calculated and converted into distance. However, this method is highly dependent on the accuracy of line parameters, and its location accuracy is difficult to guarantee when the power grid topology is complex or the parameters are uncertain. The second stage, time-of-arrival and frequency domain methods based on waveform recognition: With the development of digital technology, methods using fault current or voltage waveform characteristics for time-of-arrival (TOA) and frequency domain analysis (Fourier / wavelet transform) have emerged. These methods achieve faster location speeds by extracting the traveling wave front or high-frequency components from the fault signal, but they often rely on a single path or single-mode signal, are susceptible to noise and multipath interference, and have high false positive and false negative rates. The third stage involves traveling wave ranging methods based on multi-channel and modal fusion: To overcome the limitations of single-mode methods, multi-channel current and voltage signal fusion technology has been introduced in recent years. By acquiring multiple signals in parallel and jointly extracting and analyzing the characteristics of current and voltage traveling waves, the reliability and positioning accuracy of wavefront detection can be significantly improved. Simultaneously, the introduction of algorithms such as dynamic threshold adjustment and cross-correlation synchronous verification provides technical assurance for robustness under complex operating conditions.

[0003] However, existing multimodal traveling wave fusion methods still have the following shortcomings: First, the threshold setting is static and singular, making it difficult to adapt to the real-time changes in noise levels and signal density on site; Second, the wavefront candidate point screening and noise suppression methods are not perfect, which easily leads to false detections or missed detections; Third, most time synchronization schemes are hardware-level synchronization, lacking software-level cross-correlation verification, and the synchronization error cannot be effectively quantified; Fourth, the fault ranging results lack connection with on-site visualization and navigation, which cannot meet the comprehensive needs of on-site emergency repair.

[0004] Therefore, it is necessary to propose a new method and device for fault location of transmission lines based on dual-modal traveling wave fusion. By combining wavelet decomposition and morphological processing for wavefront extraction, multi-channel dynamic threshold screening and correlation confirmation, synchronous verification through cross-correlation software verification, and geographic information mapping and offline navigation file generation, the accuracy, reliability and operability of fault location can be comprehensively improved. Summary of the Invention

[0005] This invention addresses the problems of existing transmission line fault location methods, such as susceptibility to noise interference from single-mode signals, static threshold settings, difficulty in quantifying time synchronization errors, and disconnection between location results and on-site visualization and navigation. It proposes a transmission line fault location method and device based on dual-mode traveling wave fusion. Through parallel acquisition and fusion extraction of current and voltage traveling wave signals, dynamic threshold filtering, multi-channel correlation confirmation, software cross-correlation synchronization verification, and geographic information mapping and offline navigation file generation, an end-to-end closed loop is achieved from fault wavefront detection to on-site emergency repair navigation.

[0006] In a first aspect, this application provides a method for fault location of transmission lines based on dual-mode traveling wave fusion, the method comprising: S1. Multiple current signals and multiple voltage signals of the transmission line are collected through a wideband instrument transformer group. After bandpass filtering and differential circuit preprocessing, they are converted into digital signals by a multi-channel analog-to-digital converter module and temporarily stored. S2. Utilize high-precision time synchronization technology to receive second pulse signals, obtain nanosecond-level timestamps, calibrate digital signals of each channel, eliminate phase differences, and output time-aligned multi-channel signals. S3. Wavelet decomposition is performed on the multi-channel signals to extract high-frequency components, and modulus maxima detection algorithm is applied to preliminarily screen out wavefront candidate points. Morphological dilation and erosion processing are performed on the wavefront candidate points to obtain a purified wavefront candidate set. S4. Set an initial threshold, and filter the wavefront candidate set based on the initial threshold. For wavefront candidate points that fail the initial threshold screening, dynamically adjust the initial threshold according to the real-time noise level and candidate point density, and then screen again. For all wavefront candidate points that pass the screening, further confirm the final true wavefront based on the multi-channel association rules and the zero-order wavefront priority principle. S5. Exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device according to the wave speed correction formula. S6. Use linear interpolation formulas to map fault points to latitude and longitude coordinates, and combine this with the line GIS data to generate visual annotations; S7. Generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and push the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

[0007] In conjunction with the first aspect, in the first implementation of the first aspect of this application, step 2, which involves using a cross-correlation algorithm to analyze and correct the time residual of any two channel signals, specifically includes: Extract any two channel signals, and based on the consistency between the amplitude change trend of the signals and the time series, calculate the correlation between the two channel signals at different offsets using a sliding delay method to obtain a complete cross-correlation curve; Identify the time offset corresponding to the peak position in the cross-correlation curve and compare it with the synchronization tolerance threshold. If it is less than the synchronization tolerance threshold, mark that any two channel signals have been synchronized. For channel pairs that exceed the synchronization tolerance threshold, the timestamp labels of the channel signals are finely adjusted and compensated, and the entire signal is shifted forward or backward by a corresponding offset along the time axis until the time residual between all channels is less than or equal to the synchronization tolerance threshold.

[0008] In conjunction with the first aspect, in the second implementation of the first aspect of this application, in step 3, the current signal is processed using the db4 wavelet basis and the voltage signal is processed using the db6 wavelet basis. The decomposition layer is three layers in each case. The first layer retains the high-frequency narrowband impulse characteristics, the second layer corresponds to the harmonic response in the mid-frequency region, and the third layer reflects the waveform gradation in the low-frequency region. The wavelet coefficients of each layer establish a mapping relationship with the channel identifier according to the time series, forming a high-frequency characteristic sequence of current and voltage at different scales.

[0009] In conjunction with the first aspect, in the third implementation of the first aspect of this application, in step 3, the modulus maxima detection algorithm scans the high-frequency component sequence according to the amplitude change trend, identifies local extreme points formed by sudden increases or decreases in amplitude of adjacent sampling points, and records the time position, wavelet coefficient amplitude and channel number corresponding to the local extreme points to form a preliminary wavefront candidate set.

[0010] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, a parameter model is pre-constructed based on different voltage levels, line lengths, and historical fault signal databases to form dynamic correspondence rules between the main frequency and the size of structural elements, and between the bandwidth and the length threshold. In step 3, the high-frequency components are subjected to spectral analysis to obtain the characteristic parameters of the signal in the target frequency band. The characteristic parameters include the main frequency component, bandwidth, power spectral density, and frequency energy distribution. Based on the characteristic parameters, the main frequency of the signal, the bandwidth range of the frequency domain energy concentration, and the noise energy ratio are calculated to establish the mapping relationship between the signal spectral characteristics and morphological parameters. Based on the parameter model and the mapping relationship, the size of a specific structural element and a specific length threshold are obtained; The dilation operation aggregates temporally adjacent wavefront candidate points based on the specific structuring element size, while the erosion operation deletes isolated wavefront candidate points whose time span is less than the specific length threshold.

[0011] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, in step 4, the initial threshold is... , among which, T init The initial threshold is... and These are the mean and standard deviation of the historical wavefront amplitude, respectively, and k is an adjustable coefficient, which is configured based on the signal-to-noise environment and historical statistical characteristics of different transmission lines. The dynamic adjustment formula for the initial threshold is: , among which, T adj The threshold is dynamically adjusted. and Here, N is the parameter adjustment coefficient, N is the real-time noise level, and D is the candidate point density.

[0012] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, in step 5, the wave speed correction formula dynamically adjusts the reference wave speed based on line parameters and environmental conditions to obtain a wave speed correction value. The line parameters include conductor type, cross-sectional area, laying method, insulation level, and grounding method. The environmental conditions include real-time temperature, atmospheric pressure, and humidity along the line.

[0013] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, in step 6, the latitude and longitude coordinates of the fault point are calculated using a linear interpolation formula based on the fault distance and the line GIS data, wherein the line GIS data includes the starting point, ending point and latitude and longitude coordinate information of all towers of the transmission line; Calculate the spatial distance between the fault point and the nearest pole, and mark the fault location on a map based on the latitude and longitude coordinates of the fault point, the nearest pole, and the spatial distance.

[0014] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, in step 7, a repair planning route is generated based on the latitude and longitude coordinates of the fault point and the real-time traffic information. The road number, turning node and distance parameters corresponding to the repair planning route in the GIS map are combined to spatially encode the latitude and longitude coordinates of the fault point and the repair route planning result to form the navigation file that does not rely on absolute coordinates but is based on relative path information. The navigation file format follows OpenLR or an equivalent standard and supports offline positioning in the absence of network.

[0015] Secondly, this application provides a transmission line fault location device based on dual-mode traveling wave fusion. The device includes a signal acquisition module, a time synchronization module, a feature extraction module, a wavefront fusion module, a fault calculation module, a geographic information module, and a path planning module corresponding to each step of the method. The signal acquisition module is used to acquire multiple current signals and multiple voltage signals of the transmission line through a wideband current transformer group. After bandpass filtering and differential circuit preprocessing, the signals are converted into digital signals by a multi-channel analog-to-digital converter module and temporarily stored. The time synchronization module is used to receive second pulse signals using high-precision time synchronization technology, obtain nanosecond-level timestamps, calibrate digital signals of each channel, eliminate phase differences, and output time-aligned multi-channel signals. The feature extraction module is used to perform wavelet decomposition on the multi-channel signal to extract high-frequency components, and apply the modulus maxima detection algorithm to initially screen out wavefront candidate points. The wavefront candidate points are then subjected to morphological dilation and erosion processing to obtain a purified wavefront candidate set. The wavefront fusion module filters the wavefront candidate set based on the initial threshold. For wavefront candidate points that fail the initial threshold screening, the initial threshold is dynamically adjusted according to the real-time noise level and candidate point density, and then the selection is repeated. For all wavefront candidate points that pass the screening, the final real wavefront is confirmed based on the multi-channel association rules and the zero-order wavefront priority principle. The fault calculation module is used to set an initial threshold, exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device according to the wave speed correction formula. The geographic information module is used to map the fault point to latitude and longitude coordinates using a linear interpolation formula, and to generate visual annotations by combining the line GIS data. The route planning module is used to generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and push the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

[0016] Compared with the prior art, the present invention has the following significant advantages: 1. By using precise time synchronization technology and wavefront candidate point screening algorithm, the phase difference between channels can be eliminated and the signal timestamp can be accurately synchronized, thereby greatly improving the accuracy of fault location. By dynamically adjusting the initial threshold, noise level and candidate point density, the adaptability and accuracy of fault location in different environments are ensured.

[0017] 2. By employing wavelet decomposition and modulus maxima detection algorithms, high-frequency components are effectively extracted from complex current and voltage signals, and candidate wavefront points are accurately screened. Morphological dilation and erosion operations can remove noise, reduce interference from false wavefront points, reduce false detections and missed detections, and improve the stability and reliability of fault location.

[0018] 3. By adopting an initial threshold based on historical statistical characteristics and a dynamic threshold adjustment strategy that combines real-time noise level and candidate point density, wavehead screening can adapt to noise changes under different operating conditions. Especially when the signal-to-noise ratio changes significantly, the screening conditions can be adjusted in real time, making fault detection more flexible and accurate, and improving the stability and robustness of the system in complex or harsh environments.

[0019] 4. Based on line parameters (such as conductor type, cross-sectional area, laying method, etc.) and environmental conditions (such as temperature, atmospheric pressure, humidity, etc.), wave velocity correction is performed, which makes the calculation of fault distance more accurate, makes up for the wave velocity error caused by neglecting environmental factors in traditional methods, and further improves the positioning accuracy.

[0020] 5. By combining the latitude and longitude of the fault location with GIS data, visual annotations are generated, and real-time traffic information is combined to create navigation files that can be used offline, which are then provided to repair personnel. This not only greatly shortens the response time of repair personnel but also ensures that they can still reach the fault location smoothly even in environments without network connectivity, thus guaranteeing the efficiency of emergency response. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of an embodiment of the transmission line fault location method based on dual-mode traveling wave fusion in this application. Figure 2 This is a schematic diagram of one embodiment of the multi-channel signal acquisition process in this application. Figure 3 This is a schematic diagram of an embodiment of the dynamic threshold adjustment and wavehead confirmation process in this application. Figure 4 This is a schematic diagram of an embodiment of the transmission line fault location device based on dual-mode traveling wave fusion in this application. Detailed Implementation

[0022] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0023] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the transmission line fault location method based on dual-mode traveling wave fusion in this application includes: Step S1: Collect multiple current signals and multiple voltage signals of the transmission line through a wideband current transformer group. After bandpass filtering and differential circuit preprocessing, the signals are converted into digital signals by a multi-channel analog-to-digital converter module and temporarily stored.

[0024] The flowchart for multi-channel signal acquisition is as follows: Figure 2 As shown.

[0025] Specifically, in the transmission line fault location system, the signal acquisition stage uses a wideband instrument transformer group to simultaneously and in parallel sense the line current and voltage. Each analog signal is filtered by a bandpass filter circuit to suppress power frequency and high-frequency interference components, and then passes through a differential circuit to eliminate common-mode noise before entering the multi-channel analog-to-digital converter module for sampling. The bandpass filter intercepts the frequency band of the fault traveling wave, allowing subsequent wavelet decomposition and time-difference analysis algorithms to focus on wavefront characteristics, reducing computational burden by eliminating redundant data processing. The differential circuit inverts two sets of identical signals and then superimposes them to remove in-phase interference components, improve the signal-to-noise ratio, and provide a stable sampling reference for the analog-to-digital conversion stage. The analog-to-digital converter module adopts a multi-channel sampling structure to ensure a consistent clock source for each channel, synchronously acquires current and voltage waveforms, and caches the digitized data in a high-speed memory to meet the continuous data access requirements of subsequent wavefront extraction and threshold filtering modules.

[0026] In the overall technical solution, bandpass filtering and differential circuits constitute the preprocessing stage, ensuring that the signal possesses identifiable traveling wave characteristics before entering the analog-to-digital conversion module. In the subsequent wavelet decomposition module, the digital signal output from the analog-to-digital conversion module uses a parameter-matched filter bank to extract high-frequency components in the current channel, while different filtering parameters are used in the voltage channel to correspond to its characteristics. These two processes are performed in parallel to generate a candidate wavefront dataset. This dataset is mapped to a morphological filtering module to denoise and refine the wavefront positions, and a dynamic threshold algorithm is used to perform real-time closed-loop adjustment of the amplitude distribution of each signal. In this way, the front-end acquisition and preprocessing technical features work closely with the back-end algorithm features: preprocessing ensures data quality, and the algorithm relies on clear signals to achieve high-precision wavefront detection.

[0027] For example, on a 500 kV transmission line, when simultaneously acquiring the traveling waves of phase currents and phase voltages, a bandpass filter circuit retains the 1 kHz to 10 kHz frequency band, suppressing noise to below -40 dB. After eliminating 300 V common-mode interference via a differential circuit, the signal is sent to a 16-bit analog-to-digital converter at a sampling rate of 10 megasamples per second. The digital signal is buffered in a 200 byte buffer for multiple random accesses by subsequent wavelet decomposition and cross-correlation algorithms, preventing packet loss due to insufficient buffer. In this process, the hardware signal channel and the software algorithm support each other. The former provides high-precision, low-latency, and interference-resistant digital input, while the latter uses high-quality data to extract the traveling wave time difference and adaptively adjust the threshold, effectively solving the ranging error problems caused by high noise interference and channel asynchrony.

[0028] Overall, the multi-channel signal acquisition technology and preprocessing module establish a reliable data foundation, enabling the wavefront extraction module to acquire accurate waveforms without additional algorithm compensation. Differential and bandpass filtering methods work in conjunction with analog-to-digital conversion synchronous sampling logic to achieve a dual improvement in signal quality and sampling efficiency. Buffered data supports real-time algorithm access, and together with dynamic threshold adjustment and multi-channel correlation verification, it ultimately achieves a more than 30% improvement in traveling wave ranging accuracy in complex power grid environments, effectively shortening fault location time and improving emergency repair efficiency.

[0029] Step S2: Receive the second pulse signal using high-precision time synchronization technology, obtain the nanosecond-level timestamp, calibrate the digital signals of each channel, eliminate the phase difference, and output the time-aligned multi-channel signal.

[0030] Specifically, the high-precision time synchronization technology utilizes the second pulse signal output from the satellite timing unit, which is then fed into the time synchronization module. This signal, after buffering and shaping, triggers a phase-locked loop to generate a master clock. The master clock is distributed to each channel's analog-to-digital converter (ADC), ensuring all ADC modules sample synchronously on the same clock edge, thus eliminating phase differences caused by clock drift at the hardware level. Each channel's ADC output digital sampling data is accompanied by a nanosecond-level timestamp generated by the time synchronization module with each sample. The timestamp is stored using a double-buffered FIFO structure, supporting continuous writing of real-time data streams while ensuring that the reading timing is unaffected by sampling rate fluctuations. After initial sorting in the buffer, the digital signals enter the software calibration engine. The calibration engine performs cross-correlation analysis on the multi-channel signals, calculates the residual time delay offset based on the natural time difference of the traveling wave arriving at different channels, and performs minor corrections to the time stamps of the relevant channel data using a time offset interpolation algorithm, ensuring nanosecond-level alignment accuracy for different channels during wavefront detection. The corrected digital signal is reloaded into the synchronization filtering module and processed in parallel with the same filtering parameters in the bandpass filter bank, thus ensuring consistency in frequency domain characteristics and providing equal-time and equal-quality data input for subsequent wavefront extraction algorithms. The time synchronization technology and cross-correlation algorithm functionally support each other. The hardware provides a unified clock reference, while the software eliminates minor phase differences caused by factors such as wiring length, temperature drift, and sampling trigger delay through residual calibration, forming an end-to-end closed-loop complementary mechanism.

[0031] For example, in a 500 kV trunk line monitoring scenario, a 1 PPS (Pulse Per Second) signal output from a GPS timing unit drives a PLL to generate a 100 MHz reference clock. Each channel's 16-bit ADC samples at a rate of 10 MS / s, and a dual-port FIFO is used for timestamp marking and data buffering. After the sampled data is sent to the real-time processing platform via a high-speed Ethernet link, the calibration engine compares the phase current and phase voltage traveling wavefront positions based on cross-correlation analysis, measuring an initial inter-channel time difference of ±20 ns. This time difference is then fine-tuned with timestamps to maintain a level of ≤5 ns. This accuracy meets the nanosecond-level time resolution requirement of the traveling wave ranging algorithm, enabling distance calculations based on time differences to be output with meter-level accuracy. In the overall technical solution, the time synchronization and alignment technology features work together with traveling wave extraction, threshold screening and multi-channel fusion algorithms to ensure that the input signals of each channel reflect the fault waveform characteristics at the same time by using a unified time reference. This also enables the multi-channel wavefront fusion module to make reliable correlation judgments within the same time window. Ultimately, this solves the ranging error and misjudgment / missed judgment problems caused by channel asynchrony in the existing technology, and significantly improves the fault location accuracy and system reliability.

[0032] S3. Wavelet decomposition is performed on the multi-channel signals to extract high-frequency components, and the modulus maxima detection algorithm is applied to initially screen out wavefront candidate points. Morphological dilation and erosion processing are then performed on the wavefront candidate points to obtain a purified wavefront candidate set.

[0033] Specifically, the dual-modal traveling wave head extraction technology achieves high-precision identification of fault traveling wave heads by performing wavelet decomposition, modulus maxima detection, and morphological processing in parallel on time-aligned current and voltage signals. These technical features, together with signal synchronization, threshold screening, and multi-channel fusion, form a complementary and mutually supportive synergistic effect in the overall solution, directly contributing to improving the accuracy of wave head extraction, enhancing noise resistance, reducing false detection and false negative rates, and ensuring the quality of the pre-data for fault distance calculation and geographic mapping.

[0034] This technique is based on eight time-aligned digital signals, including four current signals and four voltage signals, all calibrated to nanosecond-level timestamps to ensure time consistency for subsequent frequency domain feature extraction. For each current signal, a three-level wavelet decomposition is performed using the db4 wavelet basis function, peeling away the frequency band components of the original signal layer by layer, retaining high-frequency components from 1kHz to 1MHz to capture the traveling wave front triggered by the fault. The voltage signal is also decomposed using the db6 wavelet basis to obtain high-frequency components of equal frequency bands, matching the frequency domain changes of the voltage response under different transmission characteristics. During the wavelet decomposition process, a direct mapping relationship is established between the original time-series signal and multi-scale frequency domain features, ensuring unified storage and processing of the signal's time and frequency domain features, providing complete data support for subsequent amplitude and wavefront discrimination.

[0035] After obtaining the high-frequency components, the modulus maxima detection algorithm sequentially searches for local amplitude maxima on the coefficient sequences at each scale, filtering out locations where there are significant abrupt changes in amplitude between adjacent sampling points. These locations are defined as candidate wavefronts. This detection strategy is based on the physical characteristic that current and voltage traveling waves are simultaneously excited at the time of a fault, using amplitude abrupt changes as the core criterion for wavefront discrimination to achieve preliminary screening of candidate points. Through this step, the triplet of amplitude, timestamp, and channel identifier in the time-series signal constitutes an index set for candidate wavefronts, establishing a correspondence between wavefronts in different modes and scales. This mapping relationship provides the data layer foundation for the fusion of multi-channel wavefronts and subsequent dynamic threshold screening.

[0036] To address isolated noise points that may still exist after modulus maxima detection, morphological dilation and erosion are implemented. The morphological operation, by defining a length threshold and structuring element, dilates and merges adjacent wavefront candidate points along the time axis, eliminating isolated false detections caused by local noise. Simultaneously, the erosion operation removes noise pseudo-wavefronts that are insufficient in length. The dilation operation enhances signal coherence, while the erosion operation improves signal reliability. Together, they purify the candidate wavefront set, strengthening the constraints on the temporal continuity and physical rationality of candidate points, thereby establishing the stability and noise suppression capability of the wavefront time series.

[0037] This technique employs a cascaded processing chain of wavelet decomposition, modulus maxima detection, and morphological filtering to progressively refine the raw current and voltage time-series signals into a multi-scale, high signal-to-noise ratio (SNR) candidate wavefront set. This provides high-quality initial data for subsequent dynamic threshold selection and multi-channel correlation analysis. This processing flow and the dynamic threshold adjustment technique mutually support each other. The dynamic threshold is adaptively updated based on the amplitude distribution of the purified candidate points. Simultaneously, collaboration with the time synchronization module ensures consistency in time accuracy across different channels, enabling subsequent fault distance calculations to achieve meter-level localization.

[0038] In practical applications, such as 500kV ultra-high voltage transmission lines, when a single-phase grounding or phase-to-phase short-circuit fault occurs, the leading edge of the traveling wave crosses the line within tens of microseconds. The amplitude and spectral characteristics of the fault traveling wave exhibit synchronous abrupt changes and frequency response differences in current and voltage signals. Applying the described dual-mode wavefront extraction process can generate consistent wavefront detection results across different signal modes, overcoming the susceptibility of traditional single-signal ranging to power frequency interference and non-fault wave excitation. This significantly improves the accuracy and stability of wavefront extraction, ensuring reliable fault wavefront identification even in complex power grid environments with strong noise and high interference. It guarantees the positioning accuracy and system robustness of fault ranging results, solving the problems of inaccurate wavefront extraction and frequent false positives and false negatives in the background technology, and improving the anti-interference capability and reliability of ranging in field applications.

[0039] S4. Set an initial threshold and filter the candidate wavefront set based on the initial threshold. For wavefront candidate points that fail the initial threshold screening, the initial threshold is dynamically adjusted according to the real-time noise level and candidate point density, and then the selection is repeated. For all wavefront candidate points that pass the screening, the final true wavefront is confirmed based on the multi-channel association rules and the zero-order wavefront priority principle.

[0040] Specifically, the multi-channel association rule is that the time difference between the current wavefront and the corresponding phase voltage wavefront is less than or equal to a time difference threshold, preferably, the time difference threshold is 5μs.

[0041] The flowchart for dynamic threshold adjustment and wavefront confirmation is as follows: Figure 3 As shown.

[0042] Specifically, the wavefront fusion and confirmation technique is applied to the candidate sets of current and voltage wavefronts obtained from dual-mode traveling wavefront extraction. By setting an initial amplitude threshold and combining it with dynamic threshold adjustment and multi-channel correlation confirmation, the true wavefronts are screened and confirmed. This technique is functionally mutually supportive with modules such as wavefront extraction, dynamic threshold adjustment, time synchronization, and fault location, forming an integrated fault location and wavefront discrimination technical path. The implementation of this technique relies on the extraction of statistical parameters from historical wavefront amplitudes. A mapping relationship is established between the amplitude mean and standard deviation in historical data. Based on this mapping, the initial screening threshold for each channel's candidate wavefront set is calculated. The initial threshold controls the screening sensitivity through an adjustment coefficient to adapt to the wavefront amplitude fluctuation characteristics under different power grid operating conditions. During the initial screening process, candidate points with amplitudes greater than or equal to the initial threshold are directly retained. For candidate points with amplitudes lower than the initial threshold, a dynamic threshold adjustment strategy is introduced. This strategy dynamically adjusts the initial threshold by combining the current real-time noise level with the distribution density of wavefront candidate points in the time series. The formation of the dynamic threshold is directly related to the environmental noise and density of the candidate points, effectively avoiding misjudgments and omissions caused by static threshold settings. The candidate points re-screened after dynamic threshold adjustment are summarized together with the candidate points that passed the initial screening to form a dataset for multi-channel correlation confirmation.

[0043] During the correlation confirmation process, for each pair of currents and their corresponding phase voltage candidate wavefronts, the difference in their timestamps is detected to determine whether it is within a preset time difference tolerance. If the time difference does not exceed a predetermined microsecond threshold, they are considered to be correlated in the physical propagation path. Correlated wavefronts are then further screened according to the zero-order wavefront priority principle to ensure that wavefronts with greater globality and consistency in the fault propagation path are selected first, avoiding misjudgments of phase wavefronts that may be caused by local noise or asymmetrical faults. The multi-channel correlation confirmation process organically combines the relative positions in the time domain, the physical correlation between modes, and the electrical characteristics of the spatial propagation path, forming a mapping and constraint of different mode wavefronts in time and channel space.

[0044] In practical applications of 500 kV ultra-high voltage transmission lines, this technology can effectively filter out isolated false detections and spurious wavefronts caused by multipath interference through the synergistic effect of dynamic thresholds and correlation confirmation, under complex operating conditions such as strong noise interference, multipath transmission, and equipment parameter drift, while ensuring high-fidelity extraction of real wavefronts, thus improving the input signal quality of fault location.

[0045] S5. Exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device based on the wave speed correction formula.

[0046] Specifically, the core step of fault location involves exchanging the timestamps of the local and remote actual wavefronts and calculating the distance from the fault point to the local device based on the wave velocity correction formula. This process directly leverages the actual wavefront information identified in the preceding wavefront fusion and confirmation steps. The actual wavefront information includes the timestamps and channel identifiers for each channel, and the time synchronization error between multiple channels is guaranteed to be controlled within nanoseconds. During the location process, the local device packages the actual wavefront timestamp data recorded by the local device into data frames via a communication link, and exchanges them with the remote device in real-time or near real-time, establishing a correspondence between the two-end wavefront timestamps and creating a time record of the same fault traveling wave leading edge at the local and remote measurement points. The data frames contain timestamps, channel numbers, and amplitude identification information to ensure the correspondence and uniqueness of the wavefront data. After receiving the timestamp data, the two-end devices map the data according to the channel number and synchronization marker, pairing the corresponding local timestamp with the remote timestamp, and calculating the propagation delay of the traveling wave between the two ends based on their time difference. This time difference is a quantitative indicator formed by the difference in path length during the propagation of the fault traveling wave from the fault point to the local and remote devices, and directly reflects the distance ratio of the fault point to the measurement end.

[0047] The ranging process incorporates wave velocity correction technology to overcome the impact of changes in ambient temperature and line parameters on the traveling wave propagation speed. Wave velocity correction is based on the nominal wave velocity of the line, combined with real-time temperature information and the characteristics of the line medium to adjust the wave velocity value, dynamically matching the propagation speed to the actual physical environment and improving the accuracy of distance calculation. Fault distance calculation uses the time difference and the corrected wave velocity as core parameters to directly calculate the distance between the fault point and the local device. In this way, the difference in timestamps between the local and remote ends not only reflects the path length difference but also compensates for the influence of seasonal and climatic factors on the traveling wave velocity through wave velocity correction, avoiding ranging deviations caused by uncompensated velocity errors.

[0048] In the 500 kV ultra-high voltage transmission line scenario where this invention is applied, the traveling wave propagation speed is significantly affected by temperature changes along the line corridor. If the wave speed is not dynamically corrected, the distance error will accumulate with seasonal variations, affecting the accuracy and efficiency of emergency repairs. By stably exchanging timestamps through the communication link and correcting the wave speed with real-time environmental parameters, dynamic coupling between the pairing time difference and the propagation speed is achieved, thereby ensuring meter-level accuracy in fault distance.

[0049] S6. Use linear interpolation formulas to map fault points to latitude and longitude coordinates, and combine this with the line GIS data to generate visual annotations.

[0050] Specifically, the above linear interpolation formulas are Lon=Lon1+(D / L)·(Lon2−Lon1) and Lat=Lat1+(D / L)·(Lat2−Lat1), where (Lon,Lat) are the latitude and longitude coordinates of the fault point, (Lon1, Lat1) are the starting point coordinates, (Lon2,Lat2) are the ending point coordinates, D is the fault distance, and L is the line length.

[0051] Specifically, in this embodiment of the invention, the geographic coordinate transformation and visualization technique establishes a mapping relationship between the distance data from the fault point to the local device and the geographic coordinates of the starting and ending points of the transmission line. The latitude and longitude coordinates of the fault point are calculated using linear interpolation, and the obtained latitude and longitude coordinates are associated with the geographic information system (GIS) data of the transmission line to generate a visual label of the fault point in geographic space. Further, the actual wavefront timestamps of the local device and the remote device are collected, and the distance D of the fault point is calculated according to the wave velocity correction formula. The total length L of the transmission line is also used as input parameters for latitude and longitude interpolation. The ratio of the fault point distance to the total line length is applied to the difference in latitude and longitude coordinates between the starting and ending points to calculate the latitude and longitude coordinates of the fault point. These latitude and longitude coordinates are then integrated with the transmission line GIS data, including line corridor information, tower coordinates and numbers, surrounding topography, and transportation network information. A spatial matching algorithm is used to map the fault point in the GIS system. The fault point is displayed visually on the map in the form of layer overlay, marking its spatial distance and positional relationship with the nearest tower. It also supports zooming in and out, positioning, and attribute information querying within the dispatch interface. After latitude and longitude conversion and GIS labeling, the fault location information is synchronized to the emergency repair route planning module. Combined with road information and traffic status data in the GIS, it further realizes the linked application from fault location to route planning. This geographic coordinate conversion and visualization technology mutually supports the fault distance calculation, time synchronization, wave velocity correction, and emergency repair route planning modules, forming a continuous technical chain from wavefront identification and distance calculation to spatial positioning and visualization. This solves the problem that existing technologies only provide one-dimensional distance in fault ranging and are disconnected from spatial coordinates and visualization. It achieves spatial mapping of fault location and information visualization for dispatch management, possessing the technical effects of simple operation, accurate positioning, and high application integration, and is suitable for rapid fault location and emergency repair dispatch scenarios for ultra-high voltage transmission lines.

[0052] S7. Generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and push the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

[0053] Specifically, in this embodiment of the invention, this step includes: after the latitude and longitude coordinates of the fault point are determined, the path planning module calls the road information of the GIS system and connects with the traffic information platform to obtain the current road traffic status. Based on the fault point and the location of emergency repair resources, the optimal emergency repair path is calculated using a path optimization algorithm. The generated path data is translated into a navigation file conforming to OpenLR or an equivalent format by the path navigation file generation module, supporting path navigation even without a network. After the fault detection is completed, the automatic notification module automatically retrieves the fault summary information and navigation file. Based on the preset contact list of emergency repair personnel and dispatchers, it pushes the fault location, fault type, distance measurement results, and path navigation data via SMS or email, ensuring that emergency repair personnel obtain accurate fault information and available path guidance in a short time. This technical means supports the functions of the fault location, geographic coordinate transformation, and path planning modules, forming a complete chain of fault response, improving the speed of emergency repair response and work efficiency, and is applicable to various emergency handling scenarios for power transmission line faults.

[0054] As a further optimization of the above technical solution, step 2 involves using a cross-correlation algorithm to analyze and correct the time residuals of any two channel signals, specifically including: (1) Extract any two channel signals, and calculate the correlation between the two channel signals at different offsets by means of sliding delay based on the consistency of the amplitude change trend and time series, so as to obtain the complete cross-correlation curve.

[0055] (2) Identify the time offset corresponding to the peak position in the cross-correlation curve and compare it with the synchronization tolerance threshold. If it is less than the synchronization tolerance threshold, mark that any two channel signals have been synchronized.

[0056] (3) For channel pairs that exceed the synchronization tolerance threshold, the timestamp labels of the channel signals are finely adjusted and compensated, and the overall signal is shifted forward or backward by the corresponding offset along the time axis until the time residual between all channels is less than or equal to the synchronization tolerance threshold.

[0057] Specifically, the time residuals of any two channel signals are analyzed using the cross-correlation algorithm Rxy(τ)=∑x(n)·y(n+τ). When the maximum cross-correlation corresponds to τ≤50 ns, the synchronization between channels is confirmed.

[0058] In this embodiment of the invention, step S2 utilizes a cross-correlation algorithm to analyze and correct the time residuals of any two channel signals. By calculating the cross-correlation function values ​​of the two channel signals under different time offsets, the time offset at which the cross-correlation value reaches its maximum is determined, thereby quantifying the time synchronization error between the two channels. When the time offset τ corresponding to the maximum cross-correlation value is less than or equal to 50 nanoseconds, the two channel signals are considered to meet the synchronization condition. This technique pairs the acquired multi-channel digital signals one by one after signal acquisition, performs cross-correlation calculations on each pair of channel signals, and calculates the correlation between one channel signal and another channel signal under different delays using a sliding delay method based on the amplitude change trend and time series consistency of the signals, obtaining a complete cross-correlation curve. The peak position in the cross-correlation curve corresponds to the optimal time alignment point between the two channel signals. The time offset value corresponding to this position is recorded and compared with a synchronization tolerance threshold of 50 nanoseconds. If it is less than this threshold, the two channel signals are marked as having completed synchronization. For channel pairs that exceed this threshold, fine-tuning compensation is applied to the timestamp labels of the channel signals, shifting the entire signal forward or backward by the corresponding offset along the time axis until the time residual between all channels is less than or equal to 50 nanoseconds.

[0059] This process mutually supports the signal's analog-to-digital conversion, timestamp marking, and the nanosecond-level time accuracy requirements of subsequent wavefront extraction. It ensures that the time reference of all channels remains consistent before the signal enters the wavefront detection and distance calculation stages, eliminating minor time errors caused by hardware sampling delays, transmission differences, and device phase drift. The cross-correlation algorithm automatically quantifies channel differences based on the signal's inherent variation characteristics, avoiding the limitations of relying solely on hardware synchronization accuracy and effectively improving the system's time synchronization accuracy and robustness. Taking multi-channel current and voltage sampling of a 500 kV transmission line as an example, this technique can control the synchronization error of eight signals to the nanosecond level, providing a highly consistent time reference for subsequent time difference ranging based on traveling wavefronts. This avoids amplifying fault distance errors caused by channel synchronization deviations, overcoming the technical shortcomings of existing technologies such as insufficient multi-channel synchronization accuracy and inability to meet meter-level fault location requirements, thus improving the timeliness and accuracy of fault ranging.

[0060] As a further optimization of the above technical solution, in step 3, the db4 wavelet basis is used to process the current signal and the db6 wavelet basis is used to process the voltage signal. The decomposition layer is three layers in each case. The first layer retains the high-frequency narrowband impulse characteristics, the second layer corresponds to the harmonic response in the mid-frequency region, and the third layer reflects the waveform gradation in the low-frequency region. The wavelet coefficients of each layer establish a mapping relationship with the channel identifier according to the time series, forming high-frequency characteristic sequences of current and voltage at different scales.

[0061] In this embodiment of the invention, in step S3, wavelet decomposition uses the db4 wavelet basis to process the current signal and the db6 wavelet basis to process the voltage signal, with three decomposition layers in each case. This enables the extraction of high-frequency features from time-aligned multi-channel current and voltage signals. This technique, along with fault traveling wave front extraction, noise suppression, and subsequent wavefront fusion confirmation, functionally supports each other, forming a complete technical path for refined signal feature extraction and noise isolation in transmission line fault location. When a traveling wave fault occurs, the current signal exhibits drastic amplitude changes and a sudden increase in high-frequency energy. The db4 wavelet basis, with its tight support and orthogonality, is suitable for capturing abrupt changes and high-frequency components in the current signal, and is beneficial for preserving the local variation characteristics of the current waveform under multi-scale analysis. The decomposed high-frequency coefficients can accurately characterize the energy concentration area of ​​the traveling wave front. Under fault excitation, the voltage signal exhibits relatively smooth characteristics but is accompanied by harmonics and slowly varying high-frequency components. The db6 wavelet basis has higher smoothness and the ability to maintain signal continuity, enabling better separation of high-frequency disturbances and background low-frequency components in the voltage signal. Performing three-level wavelet decomposition on current and voltage signals separately subdivides the signal spectrum into different frequency bandwidths, covering the typical frequency bands involved in the fault traveling wave propagation process. The first level of decomposition retains the high-frequency narrowband impulse characteristics, the second level corresponds to the harmonic response in the mid-frequency region, and the third level reflects the waveform gradation in the low-frequency region. The wavelet coefficients of each level are mapped according to time series and channel identifiers, forming high-frequency feature sets of current and voltage at different scales. This feature set provides the basic data of signal amplitude and energy distribution for subsequent modulus maxima detection and morphological processing. This wavelet decomposition technique, in conjunction with the subsequent wavefront extraction algorithm, effectively improves the ability to decompose and retain different signal features by distinguishing the modal characteristics of current and voltage and selecting the most suitable wavelet basis. It enhances the accuracy of wavefront identification and anti-interference performance under complex power grid noise backgrounds, solving the problem of insufficient wavefront extraction accuracy caused by the mismatch between wavelet basis and signal characteristics in existing technologies. It is suitable for fault location and diagnosis applications in transmission lines with voltage levels of 500 kV and above.

[0062] As a further optimization of the above technical solution, in step 3, the modulus maximum detection algorithm scans the high-frequency component sequence according to the amplitude change trend, identifies local extreme points formed by sudden increases or decreases in amplitude of adjacent sampling points, and records the time position, wavelet coefficient amplitude and channel number corresponding to the local extreme points to form a preliminary wavefront candidate set.

[0063] In this embodiment of the invention, step S3 employs a modulus maxima detection algorithm to identify candidate wavefront positions in the high-frequency components of the current and voltage signals obtained after wavelet decomposition. This is combined with dilation and erosion operations in morphological processing, and noise points shorter than τ_filter are removed based on a length threshold τ_filter, thus achieving accurate extraction and noise suppression of faulty traveling wavefronts. The modulus maxima detection algorithm scans the high-frequency component sequence according to amplitude variation trends, identifying local extrema points formed by sudden increases or decreases in amplitude between adjacent sampling points. The algorithm records the time position, amplitude, and channel number corresponding to these extrema points, forming a preliminary wavefront candidate set. The candidate set maintains a one-to-one correspondence with the original signal on the time axis and channel index, forming a direct mapping relationship between the candidate wavefronts and the original high-frequency components. Since noise and power frequency interference remain in the high-frequency components, simple maxima detection may identify false detection points caused by transient noise fluctuations rather than actual traveling waves. Therefore, morphological processing needs to be introduced based on the candidate set. The expansion operation aggregates temporally adjacent candidate points through structuring elements, enhancing the coherence of consecutive candidate points. The erosion operation deletes isolated candidate points with a time span smaller than τ_filter, ensuring that the retained candidate wavefronts conform to the physical laws of traveling wave propagation in terms of time length and amplitude. The length threshold τ_filter is set based on historical data and noise characteristics under typical operating conditions, effectively eliminating short pulses and spikes caused by noise. The retained candidate points have temporal persistence and amplitude stability, forming a purified wavefront candidate set. This process supports subsequent steps such as dynamic threshold filtering and multi-channel correlation confirmation. The retained candidate wavefronts provide a high-quality, low-error signal foundation for subsequent real wavefront confirmation and fault distance calculation. By combining modulus maxima detection and morphological filtering, the system maintains sensitive detection capability of fault traveling wavefronts while suppressing the impact of noise interference on the identification results. This solves the technical problems of high wavefront misjudgment and missed judgment rates and insufficient identification accuracy under noise in existing technologies. It is suitable for fault location and positioning applications in high-voltage and ultra-high-voltage transmission lines, possessing engineering feasibility and practical value.

[0064] As a further optimization of the above technical solution, a parameter model is pre-constructed based on different voltage levels, line lengths, and historical fault signal databases to form dynamic correspondence rules between the main frequency and structural element size, and between bandwidth and length threshold. In step 3, spectral analysis is performed on the high-frequency components to obtain the characteristic parameters of the signal in the target frequency band. The characteristic parameters include the main frequency component, bandwidth, power spectral density, and frequency energy distribution. Based on the characteristic parameters, the main frequency of the signal, the bandwidth range of frequency domain energy concentration, and the proportion of noise energy are calculated to establish the mapping relationship between the signal spectral characteristics and morphological parameters. Based on the parameter model and mapping relationship, obtain the size of a specific structural element and a specific length threshold; The dilation operation aggregates temporally adjacent wavefront candidate points based on a specific structuring element size, while the erosion operation deletes isolated wavefront candidate points whose time span is less than a specific length threshold.

[0065] Specifically, addressing the lack of flexibility caused by a fixed multi-channel time difference tolerance Δt, this paper introduces a technique for dynamically adjusting morphological processing parameters. This enables the self-adjusting structure element and length threshold τ_filter based on signal spectral feature feedback, resolving the impact of varying voltage levels, line lengths, and equipment sampling accuracy on the accuracy of fault wavefront extraction. This technique eliminates the use of statically set structure element sizes and length thresholds during morphological dilation and erosion operations. Instead, it achieves adaptive parameter updates through spectral feature extraction, feature index quantization, and dynamic mapping adjustment of the signal under processing. This closely links morphological operations with the frequency and time domain characteristics of the signal itself, functionally forming a mutually supportive linkage mechanism with wavefront identification, dynamic threshold filtering, and multi-channel time difference discrimination.

[0066] In the specific implementation process, wavelet decomposition is first performed on the time-aligned data of the dual-modal traveling wave signal to extract high-frequency components. Then, fast Fourier transform or other spectral analysis methods are used to obtain characteristic parameters such as the principal frequency components, bandwidth, power spectral density, and frequency energy distribution within the target frequency band. Based on these spectral characteristics, the signal's dominant frequency, the bandwidth range of concentrated frequency energy, and the proportion of noise energy are calculated, establishing a mapping relationship between the signal's spectral characteristics and morphological parameters. This mapping relationship is pre-built using a parameter model based on different voltage levels, line lengths, and historical fault signal databases, forming dynamic correspondence rules between the dominant frequency and the size of the structural element, and between the bandwidth and the length threshold τ_filter. For example, when the line length is long, the signal dominant frequency is low, and the bandwidth is narrow, the size and length threshold of the structural element are dynamically increased to ensure the adaptability of wavefront identification to low-frequency wide-band pulse signals, avoiding the misjudgment of wavefront width caused by low frequencies as continuous noise and its incorrect rejection. Conversely, when the main frequency is high and the energy distribution is wide, the structural elements and length thresholds are reduced accordingly to improve the resolution of narrow pulses and high-frequency wavefronts, ensuring that the true wavefront is not excessively eliminated by morphological erosion operations.

[0067] The dynamically adjusted structural element size directly affects the expansion and erosion operations. The expansion operation merges candidate points that are temporally adjacent and have similar amplitudes based on the adjusted element width, strengthening continuity features and avoiding missed detections. The erosion operation uses a dynamic threshold τ_filter as a benchmark to remove short-term isolated signal points smaller than this threshold, filtering out short-period pseudo wavefronts caused by high noise levels or long-distance transmission, thus improving the purity of the wavefront candidate set. This dynamic adjustment mechanism is linked to the subsequent multi-channel time difference Δt screening. Because the dynamic morphological processing has optimized the temporal continuity and amplitude stability of the wavefronts based on spectral characteristics, the extracted wavefront timestamps under different conditions are targeted and accurate. This supports the decision on the tolerance of Δt no longer using a fixed threshold, but rather combining the wavefront time width adjusted by dynamic parameters to adapt to different line characteristics and equipment performance.

[0068] In a specific embodiment of the present invention, to achieve dynamic adjustment of morphological processing parameters, the length and length threshold of the structural element are adaptively adjusted based on signal spectral feature feedback. Specifically, this includes: performing Fourier transform on the high-frequency components of the time-aligned current and voltage signals, calculating the power spectral density (PSD), and extracting the signal's dominant frequency f based on the PSD. peak and effective bandwidth BW eff Main frequency f peak Defined as the frequency at which PSD reaches its maximum value, effective bandwidth BW eff The frequency difference is determined using the half-power point method, which identifies the frequency difference corresponding to a certain percentage (e.g., 0.5 times) of the peak energy. Based on the dominant frequency and effective bandwidth, the length L of the structuring element used for morphological processing is dynamically calculated using a mapping model. struct The formulas for calculating the length threshold τ_filter are as follows: , , where f s K1 and K2 are empirical adjustment coefficients set according to different line and voltage levels, representing the signal sampling rate. This model dynamically reduces the structural element and threshold when the signal frequency is high to adapt to the narrow pulse characteristics of high-frequency signals; when the frequency is low or the bandwidth is narrow, the parameters are increased accordingly to avoid the erroneous rejection of low-frequency wide pulses. The dynamically calculated structural element length is applied to expansion and corrosion operations, and the length threshold τ_filter is used for noise point removal and discrimination during the corrosion process. This achieves adaptive adjustment of parameters to signal characteristics during morphological processing, improving the accuracy of wavefront extraction and adaptability to different power grid conditions. Functionally, it supports wavefront fusion and multi-channel time difference discrimination, ensuring the timing accuracy and identification stability of fault location.

[0069] When applied to long-distance transmission lines of 500 kV and above, this technique can dynamically adjust morphological parameters according to seasonal temperature changes, line parameter variations, and sampling equipment characteristics. This effectively suppresses wavefront extraction errors caused by changes in signal spectral characteristics, improves the reliability and flexibility of time difference (Δt) discrimination in multi-channel fusion, and avoids false or missed screening due to static parameters. Through this method, morphological processing not only serves as a noise suppression technique but also performs adaptive signal feature adjustment. The algorithm features, dynamic screening, and multi-channel time difference discrimination constitute a synergistic optimization approach, jointly achieving high adaptability in fault wavefront extraction and improved ranging accuracy. This technique possesses outstanding substantive characteristics and significant technological advancements, meeting the application needs of various complex power grid conditions.

[0070] As a further optimization of the above technical solution, in step 4, the initial threshold is... , among which, T init As the initial threshold, and These are the mean and standard deviation of the historical wavefront amplitude, respectively, and k is an adjustable coefficient, which is configured based on the signal-noise environment and historical statistical characteristics of different transmission lines. The dynamic adjustment formula for the initial threshold is: , among which, T adj The threshold is dynamically adjusted. and Here, is the parameter adjustment coefficient, N is the real-time noise level, and D is the candidate point density.

[0071] Specifically, the initial threshold T init The adjustable coefficient k ranges from 1 to 3, and the values ​​of the dynamic adjustment constants α and β are also taken.

[0072] In this embodiment of the invention, the initial threshold T in step S4 initThe threshold value is set based on the mean μ and standard deviation σ of historical wavefront amplitudes, and weighted by a coefficient k. The coefficient k ranges from 1 to 3 and can be flexibly configured according to the signal-noise environment and historical statistical characteristics of different transmission lines. When the system is in a low noise level or with significant fault characteristics, the value of k is smaller to improve sensitivity; conversely, in an environment with high noise intensity or complex signal interference, the value of k is increased to enhance the robustness of wavefront screening. After screening candidate wavefront points based on this initial threshold, if the amplitude of a candidate point does not reach the initial threshold, the system introduces a dynamic adjustment mechanism. The threshold is corrected through the combined action of dynamic adjustment constants α and β. α is used to quantify the upward adjustment of the threshold by the real-time noise level N, and β is used to control the downward adjustment of the threshold based on the candidate point density D, thereby achieving dynamic adaptation to changes in environmental noise and signal density. The values ​​of α and β are set based on multiple sets of actual transmission line monitoring data in the embodiments of the specification. The optimal parameter range under different noise levels and density distributions is obtained through experimental calibration to ensure that the adjusted threshold T is optimized. adj It can accurately distinguish between real wavefronts and noise interference signals.

[0073] Based on dynamic adjustment, the length threshold τ_filter in morphological processing is also set synchronously for continuity verification of wavefront candidate points. τ_filter is determined based on the minimum effective wavefront duration of the actual fault signal on lines at different voltage levels, ensuring that short pulses and isolated signals caused by noise interference are eliminated. The dynamically adjusted threshold and the length threshold τ_filter work synergistically. The dynamic threshold ensures environmental adaptability for amplitude screening, while the τ_filter ensures noise suppression and signal continuity control on the time scale. They complement each other, jointly achieving highly reliable extraction of the true wavefront. This technique works in conjunction with wavefront fusion, multi-channel association, and subsequent fault location modules. The adjustment of the dynamic threshold and the setting of the morphological length threshold directly affect the accuracy of the wavefront timestamp in subsequent fault distance calculation and the temporal consistency of channel association. It solves the problem of insufficient adaptability of static thresholds and fixed length standards in existing technologies to simultaneously accommodate noise variations and signal complexity, improving the accuracy and stability of fault location in variable power grid environments. It possesses outstanding substantive characteristics and significant technological advancements.

[0074] As a further optimization of the above technical solution, in step 5, the wave speed correction formula dynamically adjusts the reference wave speed based on line parameters and environmental conditions to obtain the wave speed correction value. The line parameters include conductor type, cross-sectional area, laying method, insulation level and grounding method, and the environmental conditions include real-time temperature, atmospheric pressure and humidity along the line.

[0075] In this embodiment of the invention, the wave velocity correction formula in step S5 is calibrated based on line parameters and environmental conditions to ensure that the accuracy of fault distance calculation meets predetermined requirements. The wave velocity correction is based on the physical parameters of the transmission line and real-time environmental factors. Line parameters include conductor type, cross-sectional area, laying method, insulation level, and grounding method. Environmental conditions include real-time temperature, atmospheric pressure, and humidity along the line. All parameters are acquired in real-time through line design data and an environmental monitoring system. The wave velocity correction formula dynamically adjusts the wave velocity value according to the relationship between the reference wave velocity, line transmission characteristics, and environmental variables, so that it reflects the actual signal propagation speed of the current line. This correction process is supported by both experimental calibration and theoretical modeling. A functional relationship between wave velocity and temperature, pressure, and humidity is established through a large amount of historical distance measurement and actual distance comparison data to form a correction model. After each fault wavefront time difference calculation is completed, the system automatically calls the current environmental parameters and inherent line parameters to calculate the corresponding wave velocity correction value, ensuring that the wave velocity parameters input for distance calculation are consistent with the current physical propagation conditions.

[0076] This wave velocity correction technique complements the fault time difference calculation, wavefront identification time calibration, and spatial mapping functions for fault location. The corrected wave velocity is used to convert the time difference between the two wavefronts into fault distance, directly affecting the spatial accuracy of fault location. In ultra-high voltage long-distance transmission lines of 500 kV and above, due to significant differences in line length and meteorological conditions, the uncorrected nominal wave velocity will cause the ranging error to accumulate linearly, affecting the reliability of the location results. Through the wave velocity correction technique of this embodiment, the propagation velocity model can be adjusted in real time under dynamic environmental changes, effectively compensating for the influence of different seasons, diurnal temperature differences, or geographical environments on the wave velocity. This ensures that fault location meets the predetermined meter-level accuracy requirements under any operating condition, solving the problems of inaccurate ranging and engineering application limitations caused by static wave velocity settings in existing technologies, and improving the application value and applicability of the present invention in actual power fault diagnosis.

[0077] As a further optimization of the above technical solution, in step 6, based on the fault distance and line GIS data, the latitude and longitude coordinates of the fault point are calculated using a linear interpolation formula. The line GIS data includes the starting point, ending point, and latitude and longitude coordinates of all towers of the transmission line. Calculate the spatial distance between the fault point and the nearest pole, and mark the fault location on the map based on the fault point's latitude and longitude coordinates, the nearest pole, and the spatial distance.

[0078] Specifically, in this embodiment of the invention, the GIS data in step S6 includes the latitude and longitude coordinates of the starting and ending points of the transmission line and the latitude and longitude coordinates of all towers along the line. This data, together with the latitude and longitude coordinates of the fault point obtained from fault ranging, forms a spatial positioning benchmark, used to calculate the spatial distance between the fault point and the nearest tower and to mark the fault location on a map. After the latitude and longitude coordinates of the fault point are determined through the preceding ranging and geographic coordinate conversion steps, the system retrieves the complete tower coordinate sequence of the corresponding transmission line from the GIS database and maps it one-to-one with the geographic coordinates according to the tower number order. Using a spatial distance calculation algorithm, the spherical distance is calculated sequentially between the latitude and longitude coordinates of the fault point and the latitude and longitude coordinates of each tower, recording the minimum distance and the corresponding tower number to determine the nearest tower to the fault point. The spatial distance between this tower and the fault point is then used as an important attribute information for fault location. The system generates a label on the GIS map centered on the fault point location, simultaneously marking the number of the nearest tower and the distance between them, visually displaying the fault location and its relative relationship within the power grid's physical topology.

[0079] The aforementioned spatial distance calculation and labeling process mutually supports the functions of the geographic coordinate transformation, fault location, and subsequent path planning modules. Accurate spatial distance calculation not only improves the precision of fault location but also provides a clear starting point and geographic reference for path planning, avoiding path deviations caused by coordinate errors. In application scenarios, such as 500 kV ultra-high voltage lines with long tower spacing and large geographical spans, the accuracy of GIS data directly determines the engineering application effectiveness of fault location. This technology solves the technical problems in existing technologies where the lack of tower spatial information and map labeling functions leads to unclear fault location on the physical line and difficulty in quickly confirming on-site repairs. It improves the spatial visualization capability of fault response and the guidance of on-site operations, possessing reliability and promotional value for engineering applications.

[0080] As a further optimization of the above technical solution, in step 7, a repair planning route is generated based on the latitude and longitude coordinates of the fault point and real-time traffic information. Combined with the road number, turning node and distance parameters corresponding to the repair planning route in the GIS map, the latitude and longitude coordinates of the fault point and the repair route planning results are spatially encoded to form a navigation file that does not rely on absolute coordinates but is based on relative path information. The navigation file format follows OpenLR or equivalent standards and supports offline positioning in the absence of network.

[0081] In this embodiment of the invention, the navigation file format in step S7 follows OpenLR or an equivalent standard. By spatially encoding the latitude and longitude of the fault point and the repair route planning results, a navigation file based on relative path information, independent of absolute coordinates, is formed. This file records the start and end points of the path, the road segments traversed, direction changes, and distances, possessing a compact structure and strong cross-map platform adaptability. After encoding, the path data is converted into a location reference data string. This data string can be decoded and matched with the local map sheet on terminal devices with local map data to reconstruct the navigation path, enabling offline path guidance. The encoding process incorporates the road number, turning nodes, and distance parameters corresponding to the path in the GIS map, ensuring that the terminal can recover navigation information based on the local map even in a network-free environment, avoiding path guidance failure due to network outages or signal blockage. This navigation file is stored in association with the fault point location, the nearest pole number, and the entire repair route information, supporting linkage with an automatic notification module to push notifications to repair personnel terminals via SMS, email, or a dedicated application platform. This technology supports the functions of fault location, path planning, and GIS spatial information processing modules. The standardized coding of navigation files and the dynamic generation of paths are linked to realize a closed-loop information chain for the entire fault repair process. It improves the emergency repair navigation capability in scenarios with complex geographical environments and limited communication conditions for power transmission lines. It overcomes the limitations of existing technologies, such as navigation unavailability or misleading due to path dependence on online map services. It ensures the timeliness and accessibility of fault repair and has outstanding engineering adaptability and application value.

[0082] The above describes the transmission line fault location method based on dual-mode traveling wave fusion in the embodiments of this application. The following describes the transmission line fault location device based on dual-mode traveling wave fusion in the embodiments of this application. Please refer to [link to relevant documentation]. Figure 4 One embodiment of the transmission line fault location device based on dual-mode traveling wave fusion in this application includes a signal acquisition module 10, a time synchronization module 20, a feature extraction module 30, a wavefront fusion module 40, a fault calculation module 50, a geographic information module 60, and a path planning module 70 corresponding to each step of the method.

[0083] The signal acquisition module 10 is used to acquire multiple current signals and multiple voltage signals of the transmission line through the wideband current transformer group. After bandpass filtering and differential circuit preprocessing, the signals are converted into digital signals by the multi-channel analog-to-digital converter module and temporarily stored.

[0084] The time synchronization module 20 is used to receive second pulse signals using high-precision time synchronization technology, obtain nanosecond-level timestamps, calibrate digital signals of each channel, eliminate phase differences, and output time-aligned multi-channel signals.

[0085] The feature extraction module 30 is used to extract high-frequency components by performing wavelet decomposition on the multi-channel signal, and to preliminarily screen out wavefront candidate points using the modulus maximum detection algorithm. The wavefront candidate points are then subjected to morphological dilation and erosion processing to obtain a purified wavefront candidate set.

[0086] The wavefront fusion module 40 filters the wavefront candidate set based on an initial threshold. For wavefront candidate points that fail the initial threshold screening, the initial threshold is dynamically adjusted according to the real-time noise level and candidate point density, and then the selection is repeated. For all wavefront candidate points that pass the screening, the final true wavefront is confirmed based on the multi-channel association rules and the zero-order wavefront priority principle.

[0087] The fault calculation module 50 is used to set the initial threshold, exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device according to the wave speed correction formula.

[0088] The geographic information module 60 is used to map the fault point to latitude and longitude coordinates using a linear interpolation formula, and to generate visual annotations by combining the line GIS data.

[0089] The route planning module 70 is used to generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and pushes the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

[0090] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for fault location in transmission lines based on dual-mode traveling wave fusion, characterized in that, Includes the following steps: S1. Multiple current signals and multiple voltage signals of the transmission line are collected through a wideband instrument transformer group. After bandpass filtering and differential circuit preprocessing, they are converted into digital signals by a multi-channel analog-to-digital converter module and temporarily stored. S2. Utilize high-precision time synchronization technology to receive second pulse signals, obtain nanosecond-level timestamps, calibrate digital signals of each channel, eliminate phase differences, and output time-aligned multi-channel signals. S3. Wavelet decomposition is performed on the multi-channel signals to extract high-frequency components, and modulus maxima detection algorithm is applied to preliminarily screen out wavefront candidate points. Morphological dilation and erosion processing are performed on the wavefront candidate points to obtain a purified wavefront candidate set. S4. Set an initial threshold, and filter the wavefront candidate set based on the initial threshold. For wavefront candidate points that fail the initial threshold screening, dynamically adjust the initial threshold according to the real-time noise level and candidate point density, and then screen again. For all wavefront candidate points that pass the screening, further confirm the final true wavefront based on the multi-channel association rules and the zero-order wavefront priority principle. S5. Exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device according to the wave speed correction formula. S6. Use linear interpolation formulas to map fault points to latitude and longitude coordinates, and combine this with the line GIS data to generate visual annotations; S7. Generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and push the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

2. The method according to claim 1, characterized in that, In step 2, the time residual of any two channel signals is analyzed and corrected using a cross-correlation algorithm, specifically including: Extract any two channel signals, and based on the consistency between the amplitude change trend of the signals and the time series, calculate the correlation between the two channel signals at different offsets using a sliding delay method to obtain a complete cross-correlation curve; Identify the time offset corresponding to the peak position in the cross-correlation curve and compare it with the synchronization tolerance threshold. If it is less than the synchronization tolerance threshold, mark that any two channel signals have been synchronized. For channel pairs that exceed the synchronization tolerance threshold, the timestamp labels of the channel signals are finely adjusted and compensated, and the entire signal is shifted forward or backward by a corresponding offset along the time axis until the time residual between all channels is less than or equal to the synchronization tolerance threshold.

3. The method according to claim 1, characterized in that, In step 3, the db4 wavelet basis is used to process the current signal and the db6 wavelet basis is used to process the voltage signal. The decomposition layer is three layers in each layer. The first layer retains the high-frequency narrowband impulse characteristics, the second layer corresponds to the harmonic response in the mid-frequency region, and the third layer reflects the waveform gradation in the low-frequency region. The wavelet coefficients of each layer establish a mapping relationship with the channel identifier according to the time series, forming high-frequency characteristic sequences of current and voltage at different scales.

4. The method according to claim 3, characterized in that, In step 3, the modulus maxima detection algorithm scans the high-frequency component sequence according to the amplitude change trend, identifies local extreme points formed by sudden increases or decreases in amplitude of adjacent sampling points, and records the time position, wavelet coefficient amplitude and channel number corresponding to the local extreme points to form a preliminary wavefront candidate set.

5. The method according to claim 1, characterized in that, A parameter model is pre-constructed based on different voltage levels, line lengths, and historical fault signal databases to form dynamic correspondence rules between the main frequency and structural element size, and between bandwidth and length threshold. In step 3, the high-frequency components are subjected to spectral analysis to obtain the characteristic parameters of the signal in the target frequency band. The characteristic parameters include the main frequency component, bandwidth, power spectral density, and frequency energy distribution. Based on the characteristic parameters, the main frequency of the signal, the bandwidth range of frequency domain energy concentration, and the noise energy ratio are calculated to establish the mapping relationship between the signal spectral characteristics and morphological parameters. Based on the parameter model and the mapping relationship, the size of a specific structural element and a specific length threshold are obtained; The dilation operation aggregates temporally adjacent wavefront candidate points based on the specific structuring element size, while the erosion operation deletes isolated wavefront candidate points whose time span is less than the specific length threshold.

6. The method according to claim 1, characterized in that, In step 4, the initial threshold is , among which, T init The initial threshold is... and These are the mean and standard deviation of the historical wavefront amplitude, respectively, and k is an adjustable coefficient, which is configured based on the signal-to-noise environment and historical statistical characteristics of different transmission lines. The dynamic adjustment formula for the initial threshold is: , among which, T adj The threshold is dynamically adjusted. and Here, N is the parameter adjustment coefficient, N is the real-time noise level, and D is the candidate point density.

7. The method according to claim 1, characterized in that, In step 5, the wave speed correction formula dynamically adjusts the reference wave speed based on line parameters and environmental conditions to obtain the wave speed correction value. The line parameters include conductor type, cross-sectional area, laying method, insulation level and grounding method. The environmental conditions include real-time temperature, atmospheric pressure and humidity along the line.

8. The method according to claim 1, characterized in that, In step 6, based on the fault distance and the line GIS data, the latitude and longitude coordinates of the fault point are calculated using a linear interpolation formula. The line GIS data includes the starting point, ending point, and latitude and longitude coordinates of all towers of the transmission line. Calculate the spatial distance between the fault point and the nearest pole, and mark the fault location on a map based on the latitude and longitude coordinates of the fault point, the nearest pole, and the spatial distance.

9. The method according to claim 1, characterized in that, In step 7, a repair planning route is generated based on the latitude and longitude coordinates of the fault point and the real-time traffic information. The road number, turning node and distance parameters corresponding to the repair planning route in the GIS map are combined to spatially encode the latitude and longitude coordinates of the fault point and the repair route planning result to form the navigation file that does not rely on absolute coordinates but is based on relative path information. The navigation file format follows OpenLR or equivalent standards and supports offline positioning in the absence of network.

10. A transmission line fault location device based on the method of any one of claims 1 to 9, characterized in that, It includes a signal acquisition module, a time synchronization module, a feature extraction module, a wavefront fusion module, a fault calculation module, a geographic information module, and a path planning module corresponding to each step of the method; The signal acquisition module is used to acquire multiple current signals and multiple voltage signals of the transmission line through a wideband current transformer group. After bandpass filtering and differential circuit preprocessing, the signals are converted into digital signals by a multi-channel analog-to-digital converter module and temporarily stored. The time synchronization module is used to receive second pulse signals using high-precision time synchronization technology, obtain nanosecond-level timestamps, calibrate digital signals of each channel, eliminate phase differences, and output time-aligned multi-channel signals. The feature extraction module is used to perform wavelet decomposition on the multi-channel signal to extract high-frequency components, and apply the modulus maxima detection algorithm to initially screen out wavefront candidate points. The wavefront candidate points are then subjected to morphological dilation and erosion processing to obtain a purified wavefront candidate set. The wavefront fusion module filters the wavefront candidate set based on the initial threshold. For wavefront candidate points that fail the initial threshold screening, the initial threshold is dynamically adjusted according to the real-time noise level and candidate point density, and then the selection is repeated. For all wavefront candidate points that pass the screening, the final real wavefront is confirmed based on the multi-channel association rules and the zero-order wavefront priority principle. The fault calculation module is used to set an initial threshold, exchange the timestamps of the local and remote real wavefronts, and calculate the fault distance from the fault point to the local device according to the wave speed correction formula. The geographic information module is used to map the fault point to latitude and longitude coordinates using a linear interpolation formula, and to generate visual annotations by combining the line GIS data. The route planning module is used to generate a navigation file that supports offline use based on the latitude and longitude coordinates of the fault point and real-time traffic information, and push the route and fault summary to the repair personnel via SMS or email through the automatic notification module.

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