Power transmission line fault traveling wave front calibration method, device, equipment, medium and product

Abstract

This invention discloses a method, apparatus, equipment, medium, and product for calibrating the wavefront of a traveling wave in a transmission line fault, relating to the field of traveling wave fault calibration technology. The method includes: acquiring the traveling wave signal of a fault in a target transmission line to construct a discrete signal sequence; performing multi-order differential operations on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; performing a product operation on the first-order differential sequence and the third-order differential sequence to construct a target detection sequence; performing zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points; using the candidate zero-crossing points as centers, extracting a target signal segment from the discrete signal sequence; constructing a morphological feature spectrum based on the target signal segment; and calibrating the arrival time of the traveling wavefront of the fault in the target transmission line based on the morphological feature spectrum. This invention eliminates the need for manually setting parameters such as the number of decomposition layers and wavelet bases, solving the technical problems of high computational load and difficulty in embedded deployment in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of fault traveling wave calibration technology, and in particular to a method, apparatus, equipment, medium and product for calibrating the wavefront of a traveling wave in a power transmission line fault. Background Technology

[0002] When a transmission line fault occurs, a transient traveling wave is generated at the fault point, propagating towards both ends. By accurately calibrating the arrival time of the traveling wave wavefront at the measurement point and combining this with the traveling wave propagation speed, the fault point can be precisely located. Therefore, the calibration accuracy of the arrival time of the traveling wave wavefront directly determines the accuracy of fault location and is the core element of traveling wave fault location technology.

[0003] Currently, common methods for traveling wave head calibration include modulus maxima detection based on wavelet transform, abrupt change point extraction based on empirical mode decomposition, nonlinear enhancement based on the Teager energy operator, and image processing based on Radon transform. These methods generally suffer from complex algorithmic structures and high computational overhead. Most methods require multiple steps such as signal decomposition, feature extraction, and threshold determination, and some even require converting one-dimensional signals into two-dimensional image processing, resulting in high computational demands, poor real-time performance, and difficulty in efficient deployment on embedded devices or edge computing terminals. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for calibrating the traveling wave head of a transmission line fault, which can achieve accurate calibration of the arrival time of the traveling wave head of a transmission line fault without the need for complex parameter settings and with low computational overhead.

[0005] In a first aspect, embodiments of the present invention provide a method for calibrating the traveling wave front of a transmission line fault, comprising: The traveling wave signal of the fault in the target transmission line is collected to construct a discrete signal sequence, and multi-order differential operations are performed on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein the traveling wave signal of the fault is collected at a fixed sampling frequency. The first-order difference sequence and the third-order difference sequence are multiplied to construct a target detection sequence, and zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; Centered on the candidate zero-crossing point, a target signal segment is extracted from the discrete signal sequence. Based on the target signal segment, a morphological feature spectrum is constructed, and based on the morphological feature spectrum, the arrival time of the fault traveling wavefront of the target transmission line is determined.

[0006] This invention converts continuous analog signals into discrete digital points, providing foundational data for subsequent differential operations. A fixed sampling frequency ensures time axis uniformity. First-order and third-order differential sequences are then obtained through differential operations. The first-order differential reflects the signal change rate (amplitude of abrupt changes), while the third-order differential reflects the acceleration change rate (pulse response at zero crossings). Both sequences are used to extract the dual characteristics of large amplitude and rapid zero crossing of the wavefront. A target detection sequence is constructed by multiplying the first-order and third-order differential sequences. The high correlation between the two sequences at the wavefront allows the product to form a zero-crossing-peak composite feature at the wavefront, while suppressing random noise (noise rarely satisfies both large amplitude and zero crossing simultaneously). Possible wavefront locations are then selected using sign flipping and amplitude conditions, which is more robust and computationally simpler than directly finding the peak value. By truncating the target signal segment, the analysis scope is narrowed, focusing on local waveforms and reducing subsequent computation. Furthermore, morphological feature spectra are constructed to further highlight the most dramatic changes in the local signal, improving positioning accuracy. Finally, the wavefront arrival time is determined based on the morphological feature spectra for fault location. Compared with existing technologies, the technical features of this invention are all based on subtraction, multiplication, and comparison operations, avoiding complex mathematical operations such as wavelet transform, mode decomposition, time-frequency analysis, or image processing, thus achieving wavefront detection with extremely low computational overhead. At the same time, by utilizing the product of the first-order difference (abrupt amplitude) and the third-order difference (zero-crossing impulse response), a sharp "zero-crossing-peak" composite feature is formed at the wavefront, while random noise is effectively suppressed in the product. Therefore, strong noise resistance and high sensitivity are obtained with only simple calculations. The entire process does not require manual setting of parameters such as the number of decomposition layers and wavelet basis, solving the core problems of existing technologies such as large computational load and difficulty in embedded deployment.

[0007] In some preferred embodiments of the first aspect, zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points, specifically as follows: Traverse the target detection sequence. If there are two adjacent sampling points in the target detection sequence whose product is less than or equal to zero and whose absolute values ​​are both greater than or equal to the adaptive scaling factor, then any one of the adjacent sampling points is determined as a candidate zero-crossing point. The adaptive scaling factor is automatically determined based on the background noise segment in the target detection sequence.

[0008] This invention ensures the detection of true zero-crossing points by flipping the signs of adjacent points (product ≤ 0), and eliminates random zero-crossings with small amplitudes caused by noise by requiring both points to have sufficiently large absolute values. In particular, the adaptive scaling factor is calculated from the background noise segment of the signal itself, eliminating the need for manually setting a fixed threshold. This allows the algorithm to automatically adapt to different signal-to-noise ratio environments, maintaining high sensitivity (without missing weak wavefronts) while avoiding false detections, significantly improving the method's field adaptability and robustness.

[0009] In some preferred embodiments of the first aspect, the target signal segment is extracted from the discrete signal sequence centered on the candidate zero-crossing point, specifically as follows: Centered on the candidate zero-crossing point, a preset number of sampling points are extracted from the discrete signal sequence in front of and behind to construct a target signal segment; wherein the preset number is determined according to the fixed sampling frequency.

[0010] This invention determines the number of segments to be truncated based on a fixed sampling frequency, ensuring that the truncated signal segments remain constant in actual time length (e.g., always corresponding to 50 microseconds before and after), rather than a fixed number of points. This guarantees that regardless of the sampling frequency of the measuring device (e.g., 1MHz or 10MHz), the time window width for morphological feature spectral line analysis remains consistent, and the wavefront positioning accuracy is unaffected by changes in the sampling rate. Furthermore, this invention eliminates the need for manual specification of the truncated length, as it is automatically calculated by the sampling frequency, further reducing manual parameter settings and enhancing the algorithm's versatility and portability.

[0011] In some preferred embodiments of the first aspect, morphological feature spectral lines are constructed based on the target signal segment, specifically as follows: The target signal segment is normalized to obtain a normalized signal segment, and a first-order difference operation is performed on the normalized signal segment to obtain morphological feature spectral lines.

[0012] This invention eliminates the interference of different fault types and varying traveling wave amplitudes on waveform shape through normalization operations, ensuring that the morphological characteristic spectrum only reflects the relative change trend of local waveforms. The subsequent first-order difference is equivalent to the second-order difference of the original signal, with its maximum value corresponding to the point of maximum curvature of the original signal, i.e., the precise location of the steepest wavefront leading edge. This invention achieves fine calibration of coarse positioning candidate points with minimal arithmetic operations (division to find the maximum absolute value, subtraction to find the difference), compensating for potential positional shifts caused by difference operations, thereby achieving sub-sampling level wavefront positioning accuracy with low computational overhead.

[0013] In some preferred embodiments of the first aspect, the arrival time of the fault traveling wavefront of the target transmission line is determined based on the morphological characteristic spectral lines, specifically as follows: The target sampling point is obtained by searching for the sampling point with the largest absolute value in the morphological feature spectrum, and the arrival time of the fault traveling wave front of the target transmission line is determined according to the sampling time of the target sampling point.

[0014] This invention directly and explicitly maps the point with the most dramatic change in the spectrum (the largest absolute value of the first-order difference) to the time output without any additional calculations or parameter adjustments, ensuring that the endpoint of the entire calibration process is unique and reproducible. At the same time, since the morphological characteristic spectrum has been normalized and differentially processed, the point with the largest absolute value has a clear physical meaning (the position where the waveform changes the fastest in the local signal segment). Therefore, this output time is the arrival time of the traveling wavefront required in engineering and can be directly used for subsequent single-end or double-end fault ranging calculations.

[0015] Among the preferred options in the first aspect are: If there are multiple candidate zero-crossing points in the target detection sequence, the calibration results of each candidate zero-crossing point are obtained, and the time interval between the calibration results of adjacent candidate zero-crossing points is calculated; wherein, the calibration result is the arrival time of the wavefront of the corresponding fault traveling wave; The time interval is sequentially determined to be less than a preset minimum wavefront interval threshold, and consecutive adjacent candidate zero-crossing points with time intervals less than the preset minimum wavefront interval threshold are identified as the target merged wavefront set; wherein the target merged wavefront set includes at least two candidate zero-crossing points; Based on the morphological feature spectral lines constructed from each candidate zero-crossing point in the target merged wavefront set, a wavefront merging operation is performed on the target merged wavefront set.

[0016] This invention addresses the problem of multiple candidate zero-crossing points being detected for the same real wavefront due to noise, signal oscillation, or weak reflected waves. Specifically, it filters out candidate points that are very close in time by using a time interval threshold, as they are likely to point to the same physical wavefront. Then, it retains the most reliable one based on the amplitude of the morphological characteristic spectral line, thereby deduplicating and correcting errors. This ensures that each real wavefront outputs only one time point, significantly improving the uniqueness and reliability of the calibration results and facilitating direct engineering applications.

[0017] Secondly, embodiments of the present invention provide a transmission line fault traveling wavefront calibration device, comprising a differential sequence acquisition module, a zero-crossing detection module, and a fault wavefront calibration module, wherein... The differential sequence acquisition module is used to acquire the fault traveling wave signal of the target transmission line to construct a discrete signal sequence, and perform multi-order differential operations on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein, the fault traveling wave signal is acquired at a fixed sampling frequency; The zero-crossing detection module is used to perform a product operation on the first-order difference sequence and the third-order difference sequence to construct a target detection sequence, and to perform zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; The fault wavefront calibration module is used to extract a target signal segment from the discrete signal sequence with the candidate zero-crossing point as the center, construct a morphological feature spectrum based on the target signal segment, and calibrate the arrival time of the fault traveling wavefront of the target transmission line based on the morphological feature spectrum.

[0018] This invention employs a differential sequence acquisition module to convert a continuous analog signal into discrete digital points, providing fundamental data for subsequent differential operations. A fixed sampling frequency ensures time axis uniformity. Next, first-order and third-order differential sequences are obtained through differential operations. The first-order differential reflects the signal's rate of change (amplitude of abrupt changes), while the third-order differential reflects the rate of change of acceleration (pulse response at zero crossings). Both sequences are used to extract the dual characteristics of large wavefront amplitude and rapid zero crossing. A zero-crossing detection module performs a product operation on the first-order and third-order differential sequences to construct a target detection sequence, thereby utilizing the two... The high correlation between the signals at the wavefront results in a zero-crossing-peak composite feature at the wavefront, while simultaneously suppressing random noise (noise rarely satisfies both amplitude and zero-crossing simultaneously). Possible wavefront locations are then selected through sign reversal and amplitude conditions, which is more robust and computationally simpler than directly finding the peak value. The fault wavefront calibration module extracts the target signal segment, narrowing the analysis scope and focusing on local waveforms to reduce subsequent computation. Furthermore, by constructing morphological feature spectra, the most dramatic changes in the local signal are highlighted, improving positioning accuracy. Finally, the wavefront arrival time is calibrated based on the morphological feature spectra for fault location.

[0019] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of the transmission line fault traveling wave head calibration method as described in any of the above.

[0020] Fourthly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus where the computer-readable storage medium is located to perform the transmission line fault traveling wave head calibration method as described in any of the preceding claims.

[0021] Fifthly, embodiments of the present invention provide a computer program product, including a computer program or instructions, wherein the computer program or instructions execute the transmission line fault traveling wave head calibration method as described in any of the above.

[0022] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of a method for calibrating the traveling wave head of a power transmission line fault, provided in an embodiment of the present invention. Figure 2 This is a structural diagram of a power transmission line fault traveling wave head calibration device provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1: like Figure 1 As shown, an embodiment of the present invention provides a method for calibrating the traveling wave front of a power transmission line fault, comprising the following steps: S101, acquire the fault traveling wave signal of the target transmission line to construct a discrete signal sequence, and perform multi-order differential operations on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein, the fault traveling wave signal is acquired at a fixed sampling frequency; In one specific embodiment, the step of acquiring the fault traveling wave signal of the target transmission line to construct a discrete signal sequence specifically involves: when a fault occurs on the line, acquiring fault voltage and current data through a device installed at the measurement point, then performing phase mode transformation to obtain a traveling wave signal, and obtaining a discrete sampling sequence x(n) of length N, where n = 1, 2, ..., N, and the sampling frequency is... .

[0026] Furthermore, by performing multi-order differential operations on the acquired discrete traveling wave signal, the rate and acceleration characteristics of signal change are extracted layer by layer, providing a basic sequence for the subsequent construction of a differential zero-crossing detection function.

[0027] In one specific embodiment, the process of obtaining the first-order difference sequence is as follows: Assume the fault traveling wave signal, after sampling, yields a sequence of length... discrete sequence ,in , where n is the index of the sampling point in the difference sequence, and the sampling frequency is . First-order difference sequence Defined as the amplitude difference between adjacent sampling points, its formula is as follows: It should be noted that the first-order difference reflects the instantaneous rate of change of the original signal within each sampling interval and is the most direct indicator of the steepness of the signal's abrupt change front. At the moment the traveling wave front arrives, due to the step jump in the fault transient voltage or current, the first-order difference exhibits a significant maximum value at that point.

[0028] In one specific embodiment, the process of obtaining the second-order difference sequence is as follows: based on the first-order difference sequence, the change in adjacent difference values ​​is further calculated to obtain the second-order difference sequence. : It should be noted that the second-order difference describes the "acceleration" characteristic of the signal's rate of change, that is, the changing trend of the first-order difference itself. At the moment of wavefront arrival, the first-order difference changes from a rapid rise to a rapid fall, and the second-order difference will inevitably undergo a sign reversal (from positive to negative or from negative to positive) near this point, i.e., a zero-crossing occurs. Therefore, the zero-crossing position of the second-order difference is an important indicator for determining the arrival of the wavefront.

[0029] In one specific embodiment, the process of obtaining the third-order difference sequence is as follows: Similarly, the second-order difference sequence is subjected to a difference operation to obtain the third-order difference sequence. : It should be noted that the third-order difference further characterizes the variation features of the second-order difference. At the moment of wavefront arrival, the second-order difference rapidly crosses the zero point, while the third-order difference forms an impulse response with specific polarity and amplitude at the corresponding position of the zero-crossing point. This invention utilizes the product of the first-order and third-order differences to construct a composite criterion, precisely taking advantage of the high correlation between these two at the wavefront: the first-order difference has a large amplitude at this point, and the third-order difference also has a significant response at this point, while random noise is effectively suppressed in the product of the two.

[0030] Preferably, since the sequence length decreases by 1 with each difference operation, after three difference operations, , , The valid index ranges are as follows: That is, to ensure index alignment in subsequent product operations, this embodiment of the invention uses the common index range of the three. This is considered the effective calculation range. For the few lost sampling points outside the boundary (a total of 3 points), since the traveling wavefront cannot appear within the very short initial time period, it does not affect the actual wavefront calibration accuracy.

[0031] Preferably, difference operations can be pipelined directly in hardware using shift registers and subtractors, eliminating the need for multiplier resources and further reducing the computational demands on the embedded processor. For example, for The data stream at the sampling rate only needs to complete three integer subtraction operations per microsecond to be updated synchronously. , , The current value fully meets the real-time requirements.

[0032] S102, perform a product operation on the first-order difference sequence and the third-order difference sequence to construct a target detection sequence, and perform zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; In one specific embodiment, the first-order difference sequence and the third-order difference sequence are multiplied to construct a target detection sequence. Specifically, a difference zero-crossing detection function Z(n) (i.e., the target detection sequence) is constructed based on the multi-order difference sequences. In a preferred embodiment, the difference zero-crossing detection function is defined as follows: That is, take the product of the first-order difference sequence and the third-order difference sequence at the same index position.

[0033] It should be noted that the principle of the traveling wave front arrival time detection of the differential zero-crossing detection function Z(n) is as follows: the traveling wave front exhibits a signal abrupt change characteristic. At the arrival time of the wave front, the first-order difference of the traveling wave signal reaches its maximum value, while the second-order difference undergoes a sign change (from positive to negative or from negative to positive) near this point. The third-order difference also exhibits corresponding characteristics at the zero-crossing point. By constructing the product function of the first-order and third-order differences, a sharp "zero-crossing-peak" composite feature can be formed at the arrival of the wave front: that is, on both sides of the zero-crossing point, Z(n) exhibits a significant sign reversal and a large amplitude. This composite feature has strong robustness to noise interference—although simple random noise may cause interference in a single-order difference, it is difficult to simultaneously satisfy the conditions of sign reversal and large amplitude in the product function of multiple-order differences.

[0034] In this embodiment, zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points. Specifically, the target detection sequence is traversed, and if there exists a product of two adjacent sampling points in the target detection sequence that is less than or equal to zero and the absolute value of the two adjacent sampling points is greater than or equal to the adaptive scaling factor, then any one of the adjacent sampling points is determined as a candidate zero-crossing point; wherein, the adaptive scaling factor is automatically determined based on the background noise segment in the target detection sequence.

[0035] In one specific embodiment, zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points. Specifically, this involves traversing the differential zero-crossing detection function sequence Z(n) and searching for candidate zero-crossing point indices that satisfy the following conditions. : Condition 1: and The signs are opposite, that is Where k is the index of the candidate zero-crossing point; Condition 2: ,in for The absolute maximum value of the sequence. For adaptive scaling factor, ; Condition 3: .

[0036] An index that simultaneously meets all three of the above conditions It is marked as a candidate zero-crossing point.

[0037] Preferably, the adaptive scaling factor The background noise level is automatically determined based on the signal background noise level: the first M sampling points of the Z(n) sequence (M is the number of points corresponding to 1 to 2 power frequency cycles of the sampling frequency) are taken as the background noise segment, and the mean of the absolute value of Z(n) in this segment is calculated. ,Pick .

[0038] S103, with the candidate zero-crossing point as the center, extract the target signal segment from the discrete signal sequence, construct a morphological feature spectrum based on the target signal segment, and determine the arrival time of the fault traveling wavefront of the target transmission line based on the morphological feature spectrum.

[0039] In this embodiment, the target signal segment is extracted from the discrete signal sequence centered on the candidate zero-crossing point. Specifically, a preset number of sampling points are extracted from the discrete signal sequence forward and backward from the candidate zero-crossing point to construct the target signal segment; wherein the preset number is determined according to the fixed sampling frequency.

[0040] In one specific embodiment, the target signal segment is extracted from the discrete signal sequence centered on the candidate zero-crossing point, specifically: in the original traveling wave signal... Above, with candidates crossing zero. Centered on a point, take L points forward and L points backward to form a local signal segment of length 2L+1. Where m = 1, 2, ..., 2L+1, and the value of L is related to the sampling frequency, generally taken as... .

[0041] In this embodiment, morphological feature spectral lines are constructed based on the target signal segment. Specifically, the target signal segment is normalized to obtain a normalized signal segment, and a first-order difference operation is performed on the normalized signal segment to obtain morphological feature spectral lines.

[0042] In one specific embodiment, for local signal segments Normalization is performed, specifically as follows: in, For local signal segments The maximum absolute value of the signal amplitude corresponding to the sampling point.

[0043] In one specific embodiment, a morphological feature spectral line S(m) is constructed, where S(m) is defined as the first-order difference of the normalized signal, and its calculation formula is as follows: In one specific embodiment, the arrival time of the fault traveling wavefront of the target transmission line is determined based on the morphological feature spectral line. Specifically, in the morphological feature spectral line S(m), the maximum absolute value of S(m) is searched from the center outwards. This maximum value point corresponds to the moment of most drastic change in the local signal segment, and it is mapped back to the index position of the original sampling sequence as the precise calibration point for the arrival time of the wavefront.

[0044] In this embodiment, the method further includes: if there are multiple candidate zero-crossing points in the target detection sequence, obtaining the calibration results of each candidate zero-crossing point and calculating the time interval between the calibration results of adjacent candidate zero-crossing points; wherein the calibration result is the arrival time of the wavefront of the corresponding fault traveling wave; sequentially determining whether the time interval is less than a preset minimum wavefront interval threshold, and determining consecutive adjacent candidate zero-crossing points with time intervals less than the preset minimum wavefront interval threshold as a target merging wavefront set; wherein the target merging wavefront set includes at least two candidate zero-crossing points; and performing a wavefront merging operation on the target merging wavefront set based on the morphological feature spectral lines constructed by each candidate zero-crossing point in the target merging wavefront set.

[0045] In one specific embodiment, if there are multiple candidate zero-crossing points, they are processed sequentially in chronological order. For multiple calibration results with a time difference less than a preset minimum wavefront interval threshold, the one with the larger maximum value of the morphological feature spectral line is retained to achieve wavefront merging and deduplication.

[0046] In this embodiment, the arrival time of the fault traveling wavefront of the target transmission line is calibrated based on the morphological feature spectral lines. Specifically, the sampling point with the largest absolute value is searched from the morphological feature spectral lines to obtain the target sampling point, and the arrival time of the fault traveling wavefront of the target transmission line is determined based on the sampling time of the target sampling point. The sampling point index corresponding to the arrival time of the wavefront is converted into absolute time or relative time, and the calibration result is output. The arrival time of the wavefront can be used for subsequent single-end or double-end traveling wave fault ranging calculations.

[0047] This invention converts continuous analog signals into discrete digital points, providing foundational data for subsequent differential operations. A fixed sampling frequency ensures time axis uniformity. First-order and third-order differential sequences are then obtained through differential operations. The first-order differential reflects the signal change rate (amplitude of abrupt changes), while the third-order differential reflects the acceleration change rate (pulse response at zero crossings). Both sequences are used to extract the dual characteristics of large amplitude and rapid zero crossing of the wavefront. A target detection sequence is constructed by multiplying the first-order and third-order differential sequences. The high correlation between the two sequences at the wavefront allows the product to form a zero-crossing-peak composite feature at the wavefront, while suppressing random noise (noise rarely satisfies both large amplitude and zero crossing simultaneously). Possible wavefront locations are then selected using sign flipping and amplitude conditions, which is more robust and computationally simpler than directly finding the peak value. By truncating the target signal segment, the analysis scope is narrowed, focusing on local waveforms and reducing subsequent computation. Furthermore, morphological feature spectra are constructed to further highlight the most dramatic changes in the local signal, improving positioning accuracy. Finally, the wavefront arrival time is determined based on the morphological feature spectra for fault location. Compared with existing technologies, the technical features of this invention are all based on subtraction, multiplication, and comparison operations, avoiding complex mathematical operations such as wavelet transform, mode decomposition, time-frequency analysis, or image processing, thus achieving wavefront detection with extremely low computational overhead. At the same time, by utilizing the product of the first-order difference (abrupt amplitude) and the third-order difference (zero-crossing impulse response), a sharp "zero-crossing-peak" composite feature is formed at the wavefront, while random noise is effectively suppressed in the product. Therefore, strong noise resistance and high sensitivity are obtained with only simple calculations. The entire process does not require manual setting of parameters such as the number of decomposition layers and wavelet basis, solving the core problems of existing technologies such as large computational load and difficulty in embedded deployment.

[0048] Example 2: like Figure 2As shown, this embodiment provides a transmission line fault traveling wavefront calibration device, including a differential sequence acquisition module 201, a zero-crossing detection module 202, and a fault wavefront calibration module 203, wherein... The differential sequence acquisition module 201 is used to acquire the fault traveling wave signal of the target transmission line to construct a discrete signal sequence, and perform multi-order differential operations on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein, the fault traveling wave signal is acquired at a fixed sampling frequency; The zero-crossing detection module 202 is used to perform a product operation on the first-order difference sequence and the third-order difference sequence to construct a target detection sequence, and to perform zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; In this embodiment, the zero-crossing detection module 202 performs zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points. Specifically, the zero-crossing detection module 202 traverses the target detection sequence. If there exists a product of two adjacent sampling points in the target detection sequence that is less than or equal to zero and the absolute values ​​of the two adjacent sampling points are both greater than or equal to the adaptive scaling factor, then any one of the adjacent sampling points is determined as a candidate zero-crossing point. The adaptive scaling factor is automatically determined based on the background noise segment in the target detection sequence.

[0049] The fault wavefront calibration module 203 is used to extract a target signal segment from the discrete signal sequence with the candidate zero-crossing point as the center, construct a morphological feature spectrum based on the target signal segment, and calibrate the arrival time of the fault traveling wavefront of the target transmission line based on the morphological feature spectrum.

[0050] In this embodiment, the fault wavefront calibration module 203 extracts a target signal segment from the discrete signal sequence centered on the candidate zero-crossing point. Specifically, the fault wavefront calibration module 203 extracts a preset number of sampling points from the discrete signal sequence forward and backward centered on the candidate zero-crossing point to construct the target signal segment; wherein, the preset number is determined according to the fixed sampling frequency.

[0051] In this embodiment, the fault wavefront calibration module 203 constructs morphological feature spectral lines based on the target signal segment. Specifically, the fault wavefront calibration module 203 performs a normalization operation on the target signal segment to obtain a normalized signal segment, and performs a first-order difference operation on the normalized signal segment to obtain morphological feature spectral lines.

[0052] In this embodiment, the fault wavefront calibration module 203 calibrates the arrival time of the fault traveling wavefront of the target transmission line according to the morphological feature spectrum. Specifically, the fault wavefront calibration module 203 searches for the sampling point with the largest absolute value in the morphological feature spectrum to obtain the target sampling point, and determines the arrival time of the fault traveling wavefront of the target transmission line according to the sampling time of the target sampling point.

[0053] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0054] This embodiment of the invention uses a differential sequence acquisition module 201 to convert a continuous analog signal into discrete digital points, providing basic data for subsequent differential operations. A fixed sampling frequency ensures time axis uniformity. Then, a first-order and third-order differential sequence are obtained through differential operations. The first-order differential reflects the signal change rate (amplitude of abrupt changes), and the third-order differential reflects the acceleration change rate (pulse response at zero crossings). Both are used to extract the dual features of large wavefront amplitude and fast zero crossing. The zero-crossing detection module 202 performs a product operation on the first-order and third-order differential sequences to construct a target detection sequence, thereby utilizing… The high correlation between the two at the wavefront results in a zero-crossing-peak composite feature at the wavefront, while suppressing random noise (noise is difficult to satisfy both amplitude and zero crossing simultaneously). Then, possible wavefront positions are screened through sign reversal and amplitude conditions, which is more robust and computationally simpler than directly finding the peak. Through the fault wavefront calibration module 203, the target signal segment is intercepted, the analysis range is narrowed, the local waveform is focused, and the subsequent calculation is reduced. Then, by constructing morphological feature spectral lines, the most dramatic change points in the local signal are further highlighted to improve the positioning accuracy. Finally, the wavefront arrival time is calibrated according to the morphological feature spectral lines for fault ranging.

[0055] Example 3: This embodiment provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of the transmission line fault traveling wave head calibration method as described in any of the above.

[0056] Example 4: This invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the transmission line fault traveling wave head calibration method as described in any of the preceding claims.

[0057] Example 5: This invention provides a computer program product, including a computer program or instructions, wherein the computer program or instructions execute the transmission line fault traveling wave head calibration method as described in any of the above embodiments.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0059] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for calibrating the traveling wave head of a transmission line fault, characterized in that, include: The traveling wave signal of the fault in the target transmission line is collected to construct a discrete signal sequence, and multi-order differential operations are performed on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein the traveling wave signal of the fault is collected at a fixed sampling frequency. The first-order difference sequence and the third-order difference sequence are multiplied to construct a target detection sequence, and zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; Centered on the candidate zero-crossing point, a target signal segment is extracted from the discrete signal sequence. Based on the target signal segment, a morphological feature spectrum is constructed, and based on the morphological feature spectrum, the arrival time of the fault traveling wavefront of the target transmission line is determined.

2. The method for calibrating the traveling wave head of a transmission line fault as described in claim 1, characterized in that, Zero-crossing detection is performed on the target detection sequence to obtain candidate zero-crossing points, specifically as follows: Traverse the target detection sequence. If there are two adjacent sampling points in the target detection sequence whose product is less than or equal to zero and whose absolute values ​​are both greater than or equal to the adaptive scaling factor, then any one of the adjacent sampling points is determined as a candidate zero-crossing point. The adaptive scaling factor is automatically determined based on the background noise segment in the target detection sequence.

3. The method for calibrating the traveling wave head of a transmission line fault as described in claim 2, characterized in that, Using the candidate zero-crossing point as the center, the target signal segment is extracted from the discrete signal sequence, specifically as follows: Centered on the candidate zero-crossing point, a preset number of sampling points are extracted from the discrete signal sequence in front of and behind to construct a target signal segment; wherein the preset number is determined according to the fixed sampling frequency.

4. The method for calibrating the traveling wave head of a transmission line fault as described in claim 3, characterized in that, Based on the target signal segment, morphological feature spectral lines are constructed as follows: The target signal segment is normalized to obtain a normalized signal segment, and a first-order difference operation is performed on the normalized signal segment to obtain morphological feature spectral lines.

5. The method for calibrating the traveling wave head of a transmission line fault as described in claim 4, characterized in that, Based on the morphological characteristic spectral lines, the arrival time of the fault traveling wavefront of the target transmission line is determined, specifically as follows: The target sampling point is obtained by searching for the sampling point with the largest absolute value in the morphological feature spectrum, and the arrival time of the fault traveling wave front of the target transmission line is determined according to the sampling time of the target sampling point.

6. The method for calibrating the traveling wave head of a transmission line fault as described in claim 2, characterized in that, Also includes: If there are multiple candidate zero-crossing points in the target detection sequence, the calibration results of each candidate zero-crossing point are obtained, and the time interval between the calibration results of adjacent candidate zero-crossing points is calculated; wherein, the calibration result is the arrival time of the wavefront of the corresponding fault traveling wave; The time interval is sequentially determined to be less than a preset minimum wavefront interval threshold, and consecutive adjacent candidate zero-crossing points with time intervals less than the preset minimum wavefront interval threshold are identified as the target merged wavefront set; wherein the target merged wavefront set includes at least two candidate zero-crossing points; Based on the morphological feature spectral lines constructed from each candidate zero-crossing point in the target merged wavefront set, a wavefront merging operation is performed on the target merged wavefront set.

7. A device for calibrating a traveling wave head for a power transmission line fault, characterized in that, It includes a differential sequence acquisition module, a zero-crossing detection module, and a fault wavefront calibration module, among which, The differential sequence acquisition module is used to acquire the fault traveling wave signal of the target transmission line to construct a discrete signal sequence, and perform multi-order differential operations on the discrete signal sequence to obtain a first-order differential sequence and a third-order differential sequence; wherein, the fault traveling wave signal is acquired at a fixed sampling frequency; The zero-crossing detection module is used to perform a product operation on the first-order difference sequence and the third-order difference sequence to construct a target detection sequence, and to perform zero-crossing detection on the target detection sequence to obtain candidate zero-crossing points; wherein, the target detection sequence contains a number of sampling points; The fault wavefront calibration module is used to extract a target signal segment from the discrete signal sequence with the candidate zero-crossing point as the center, construct a morphological feature spectrum based on the target signal segment, and calibrate the arrival time of the fault traveling wavefront of the target transmission line based on the morphological feature spectrum.

8. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the transmission line fault traveling wave head calibration method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the transmission line fault traveling wave head calibration method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions execute the transmission line fault traveling wave head calibration method as described in any one of claims 1 to 6.

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