A method, system, device, medium and product for identifying a dominant traveling wave of a multi-branch power distribution network

Abstract

The application discloses a kind of leading traveling wave identification method, system, equipment, medium and product of multi-branch power distribution network, it is related to traveling wave identification technical field, using preset difference step to carry out mutation detection to target traveling wave signal, obtain multiple candidate traveling wave segments, based on preset spectrum centroid function to each candidate traveling wave segment is distributed power interference elimination processing, obtain intermediate candidate traveling wave segment, based on power distribution network topology to each intermediate candidate traveling wave segment is screened, obtain target candidate traveling wave segment and propagation time consistency index, according to the adjacent measuring point traveling wave arrival time set and each propagation time consistency index that is obtained in advance to each target candidate traveling wave segment is carried out multi-measuring point collaborative identification, obtain leading traveling wave.The technical problem that it is difficult to directly distinguish effective component and interference component in the multi-branch power distribution network scene is solved, which is solved that traditional leading traveling wave discrimination method is more dependent on the first arrival time of single-point signal, amplitude mutation or fixed threshold judgment.

Description

Technical Field

[0001] This invention relates to the field of traveling wave identification technology, and in particular to a method, system, device, medium and product for identifying dominant traveling waves in a multi-dominated power grid. Background Technology

[0002] The distribution network is the core component of the power system supplying power to users. With the rapid development of new power systems, the distribution network is gradually transforming into a complex structure with multiple branches, multiple interconnections, and high distributed power source penetration, which places higher demands on rapid fault detection, accurate fault location, and reliable isolation. The traveling wave principle, due to its advantages such as obvious transient characteristics, fast response speed, and high location accuracy, has become a key technical approach in the field of distribution network fault diagnosis and relay protection, playing an important role in engineering applications such as fault location, section identification, and fault type determination.

[0003] Currently, traditional methods for identifying dominant traveling waves mostly rely on the arrival time, amplitude abrupt change, or fixed threshold of a single measurement point signal. However, in multi-branch power grid scenarios, after traveling waves propagate, reflect, and transmit through multiple branch lines, they form severe aliasing at the measurement point, resulting in blurred wavefront characteristics and overlapping arrival times. This makes it difficult to directly distinguish between effective components and interference components, thus reducing the identification accuracy of dominant traveling waves. Summary of the Invention

[0004] This invention provides a method, system, device, medium, and product for identifying dominant traveling waves in multi-branch-dominated power grids. It solves the technical problem that traditional dominant traveling wave identification methods rely on the arrival time, amplitude abrupt change, or fixed threshold of a single measurement point signal. However, in multi-branch-dominated power grid scenarios, after the traveling wave propagates, reflects, and is transmitted through multiple branch lines, it forms severe aliasing at the measurement point, with blurred wavefront characteristics and overlapping arrival times, making it difficult to directly distinguish between effective components and interference components, thus reducing the identification accuracy of dominant traveling waves.

[0005] The first aspect of this invention provides a method for identifying the dominant traveling wave in a multi-dominated power grid, comprising:

[0006] Acquire the aliased traveling wave signal and distribution network topology of the multi-control power grid, preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal;

[0007] The target traveling wave signal is subjected to abrupt change detection using a preset differential step size to obtain multiple candidate traveling wave segments;

[0008] Based on the preset spectral centroid function, distributed power source interference removal processing is performed on each candidate traveling wave segment to obtain the corresponding intermediate candidate traveling wave segment.

[0009] Based on the power distribution network topology, the propagation time consistency of each intermediate candidate traveling wave segment is screened to obtain the corresponding target candidate traveling wave segment and propagation time consistency index.

[0010] Based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, multi-measurement point collaborative identification is performed on each of the target candidate traveling wave segments to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

[0011] Optionally, the step of preprocessing the aliased traveling wave signal to obtain the corresponding target traveling wave signal includes:

[0012] The aliased traveling wave signal is subjected to baseline correction processing based on a preset sliding window length to obtain the corresponding baseline correction signal;

[0013] The baseline correction signal is subjected to bandpass filtering to obtain the corresponding bandpass filtered signal;

[0014] The bandpass filtered signal is normalized to obtain the corresponding target traveling wave signal.

[0015] Optionally, the step of performing abrupt change detection on the target traveling wave signal using a preset differential step size to obtain multiple candidate traveling wave segments includes:

[0016] The target traveling wave signal is subjected to first-order difference operation based on a preset difference step size to obtain multiple first-order difference values.

[0017] Based on the differential step size, local energy difference calculation is performed on the target traveling wave signal to obtain multiple local energy difference values;

[0018] Based on the preset mutation weights, the absolute values ​​of each first-order difference value and the absolute values ​​of the corresponding local energy difference values ​​are weighted and calculated to obtain multiple mutation detection indicators.

[0019] Based on the preset median absolute deviation function, the corresponding adaptive threshold is determined according to each of the mutation detection indicators.

[0020] Based on the adaptive threshold and the preset interval constraint, each mutation detection index is screened to obtain the corresponding candidate wavefront time set.

[0021] Asymmetric local time windows are constructed with each candidate wavefront time in the candidate wavefront time set as the center, and the target traveling wave signal is truncated using each of the asymmetric local time windows to obtain multiple candidate traveling wave segments.

[0022] Optionally, the step of performing distributed power source interference removal processing on each of the candidate traveling wave segments based on a preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segments includes:

[0023] Each candidate traveling wave segment is input into a preset spectral centroid function to obtain multiple spectral centroids;

[0024] Each candidate traveling wave segment is subjected to Hilbert transform to obtain multiple instantaneous amplitude envelopes, and the attenuation rate of each instantaneous amplitude envelope is determined as the corresponding attenuation envelope feature;

[0025] When the attenuation envelope feature corresponding to the candidate traveling wave segment is within the preset distributed power injection component feature range, it is determined whether the spectral centroid corresponding to the candidate traveling wave segment is within the distributed power injection component feature range.

[0026] When the attenuation envelope feature corresponding to the candidate traveling wave segment is not within the feature range of the distributed power injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0027] When the spectral centroid corresponding to the candidate traveling wave segment is not located in the characteristic range of the distributed power source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0028] Optionally, the step of performing propagation time consistency screening on each of the intermediate candidate traveling wave segments based on the distribution network topology to obtain the corresponding target candidate traveling wave segments and propagation time consistency index includes:

[0029] A depth-first search was performed based on the power distribution network topology to obtain multiple candidate propagation paths;

[0030] The theoretical propagation time for each candidate propagation path is determined by a preset theoretical propagation time function.

[0031] Based on a preset time consistency function, the propagation time consistency index corresponding to each intermediate candidate traveling wave segment is determined according to each theoretical propagation time.

[0032] When the propagation time consistency index corresponding to the intermediate candidate traveling wave segment is greater than or equal to the preset consistency threshold, the intermediate candidate traveling wave segment is determined as the target candidate traveling wave segment.

[0033] Optionally, the step of performing multi-point collaborative identification of each target candidate traveling wave segment based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, to obtain the dominant traveling wave corresponding to the aliased traveling wave signal, includes:

[0034] The arrival time features, local energy features, wavefront steepness features, and template matching degree features of each of the target candidate traveling wave segments are extracted respectively.

[0035] The arrival time feature, local energy feature, wavefront steepness feature, template matching degree feature and propagation time consistency index corresponding to each of the target candidate traveling wave segments are normalized to obtain multiple multidimensional local features;

[0036] Based on a preset Gaussian attenuation function, the collaborative confidence level of each target candidate traveling wave segment is determined according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the distribution network topology.

[0037] Based on the preset collaborative confidence weight, the collaborative confidence of the measurement points corresponding to each of the target candidate traveling wave segments is weighted and averaged to obtain multiple multi-measurement point collaborative confidences.

[0038] Based on the preset evaluation weights, the multi-point collaborative confidence and multi-dimensional local features corresponding to each of the target candidate traveling wave segments are weighted and calculated to obtain multiple evaluation scores;

[0039] The target candidate traveling wave segment corresponding to the maximum value among the evaluation scores is selected as the dominant traveling wave corresponding to the aliased traveling wave signal.

[0040] A second aspect of the present invention provides a dominant traveling wave identification system for a multi-dominated power grid, comprising:

[0041] The preprocessing module is used to acquire the aliased traveling wave signal and distribution network topology of the multi-distribution power grid, and to preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal.

[0042] The mutation detection module is used to perform mutation detection on the target traveling wave signal using a preset differential step size to obtain multiple candidate traveling wave segments;

[0043] The interference removal module is used to perform distributed power interference removal processing on each candidate traveling wave segment based on a preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segment.

[0044] The filtering module is used to filter the propagation time consistency of each intermediate candidate traveling wave segment based on the distribution network topology, and obtain the corresponding target candidate traveling wave segment and propagation time consistency index.

[0045] The identification module is used to perform multi-point collaborative identification of each target candidate traveling wave segment based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, so as to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

[0046] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the dominant traveling wave identification method for a multi-dominated power grid as described above.

[0047] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the dominant traveling wave identification method for a multi-dominated power grid as described above.

[0048] The fifth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the dominant traveling wave identification method for a multi-dominated power grid as described above.

[0049] As can be seen from the above technical solutions, the present invention has the following advantages:

[0050] This invention acquires aliased traveling wave (TWT) signals and distribution network topology from a multi-dominated power grid. It preprocesses the TWT signals to obtain corresponding target TWT signals, performs abrupt change detection on the target TWT signals using a preset differential step size, and obtains multiple candidate TWT segments. Based on a preset spectral centroid function, it performs distributed source interference (DPI) elimination processing on each candidate TWT segment to obtain corresponding intermediate candidate TWT segments. Based on the distribution network topology, it performs propagation time consistency screening on each intermediate candidate TWT segment to obtain corresponding target candidate TWT segments and propagation time consistency indices. Based on a pre-acquired set of arrival times of TWTs at adjacent measurement points and each propagation time consistency index, it performs multi-measurement point collaborative identification of each target candidate TWT segment to obtain the dominant TWT corresponding to the aliased TWT signal. This overcomes the technical problem that traditional dominant TWT discrimination methods rely heavily on the first arrival time, amplitude abrupt changes, or fixed thresholds of single-measurement point signals. However, in multi-dominated power grid scenarios, after the TWT propagates, reflects, and transmits through multiple branch lines, severe aliasing occurs at the measurement points, resulting in blurred wavefront characteristics, overlapping arrival times, and difficulty in directly distinguishing effective components from interference components. Compared with traditional methods for identifying dominant traveling waves, this invention first preprocesses the aliased traveling wave signal to improve signal quality, then combines abrupt change detection to quickly extract candidate traveling wave segments, and accurately eliminates pseudo wavefront interference injected by distributed power sources based on the spectral centroid function. This effectively reduces the impact of interference components on subsequent identification from the source. At the same time, it performs propagation time consistency screening based on the distribution network topology, making full use of the physical propagation laws to constrain the rationality of candidate segments. Then, it combines the arrival times of adjacent measurement points to complete multi-measurement point collaborative identification, achieving dual verification of single-measurement point signal characteristics and multi-measurement point spatial propagation consistency. In complex distribution network scenarios with multiple branches and high distributed power source penetration, it can significantly improve the decoupling effect of aliased traveling waves and the accuracy of dominant traveling wave identification. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of the steps of a dominant traveling wave identification method for a multi-dominated power grid provided in Embodiment 1 of the present invention;

[0053] Figure 2 This is a flowchart illustrating the steps of a dominant traveling wave identification method for a multi-dominated power grid according to Embodiment 2 of the present invention.

[0054] Figure 3 This is a schematic diagram of the target traveling wave signal provided in Embodiment 2 of the present invention;

[0055] Figure 4 This is a schematic diagram of the candidate wavefront timing within the target traveling wave signal provided in Embodiment 2 of the present invention;

[0056] Figure 5 This is a schematic diagram of the results of intermediate candidate traveling wave segment identification provided in Embodiment 2 of the present invention;

[0057] Figure 6 This is a schematic diagram of the curve showing the change of the measurement point collaborative confidence level with the deviation between the measured time difference and the theoretical time difference, as provided in Embodiment 2 of the present invention.

[0058] Figure 7 This is a schematic diagram comparing the evaluation scores of each target candidate traveling wave segment provided in Embodiment 2 of the present invention;

[0059] Figure 8 This is a structural block diagram of a dominant traveling wave identification system for a multi-dominated power grid provided in Embodiment 3 of the present invention;

[0060] Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0061] This invention provides a method, system, device, medium, and product for identifying dominant traveling waves in multi-branch-dominated power grids. It addresses the technical problem that traditional dominant traveling wave identification methods rely heavily on the arrival time, amplitude abrupt changes, or fixed thresholds of signals from single measurement points. However, in multi-branch-dominated power grid scenarios, after traveling waves propagate, reflect, and transmit through multiple branch lines, severe aliasing occurs at the measurement points, resulting in blurred wavefront characteristics and overlapping arrival times. This makes it difficult to directly distinguish between effective and interference components, thus reducing the identification accuracy of dominant traveling waves.

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0063] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for identifying the dominant traveling wave in a multi-dominated power grid, as provided in Embodiment 1 of the present invention.

[0064] This invention provides a method for identifying the dominant traveling wave in a multi-dominated power grid, comprising:

[0065] Step 101: Obtain the aliased traveling wave signal and distribution network topology of the multi-control power grid, preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal.

[0066] A multi-branch power grid refers to a complex power distribution network with multiple branch lines, tie switches, and the ability to connect to distributed power sources, where traveling wave propagation exhibits multi-path characteristics.

[0067] Altered traveling wave signals refer to transient voltage or current signals formed by the superposition of traveling wave components propagating along different paths in a multi-polar power grid at the measurement point. The wavefront characteristics are blurred and easily confused.

[0068] Distribution network topology refers to the connection relationships and physical structure of components such as lines, nodes, measuring points, and distributed power sources in a distribution network.

[0069] The target traveling wave signal refers to a preprocessed traveling wave signal that has no baseline offset, no invalid noise, and has a normal amplitude.

[0070] In this embodiment of the invention, the aliased traveling wave signal and the distribution network topology of the multi-control power grid are obtained, and the aliased traveling wave signal is sequentially subjected to baseline correction, bandpass filtering and normalization to obtain the corresponding target traveling wave signal.

[0071] Step 102: Use a preset differential step size to perform abrupt change detection on the target traveling wave signal to obtain multiple candidate traveling wave segments.

[0072] Differential step size refers to the pre-set interval between signal sampling points, which is used to control the sensitivity and noise immunity of differential operations.

[0073] In this embodiment of the invention, first-order difference operations and local energy difference operations are performed on the target traveling wave signal based on a preset difference step size to obtain multiple first-order difference values ​​and multiple local energy difference values. Based on a preset mutation weight, the absolute value of each first-order difference value and the absolute value of the corresponding local energy difference value are weighted to obtain multiple mutation detection indicators. Each mutation detection indicator is input point by point into a preset median absolute deviation function to obtain the corresponding adaptive threshold. Based on the adaptive threshold and a preset interval constraint, each mutation detection indicator is screened to obtain a corresponding set of candidate wavefront times. Asymmetric local time windows are constructed with each candidate wavefront time in the set as the center, and each asymmetric local time window is used to truncate the target traveling wave signal to obtain multiple candidate traveling wave segments.

[0074] Step 103: Perform distributed power source interference removal processing on each candidate traveling wave segment based on the preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segments.

[0075] Intermediate candidate traveling wave segments refer to the valid candidate segments retained after pre-removal of distributed source interference.

[0076] In this embodiment of the invention, each candidate traveling wave segment is input into a preset spectral centroid function to obtain multiple spectral centroids. Each candidate traveling wave segment is then subjected to a Hilbert transform to obtain multiple instantaneous amplitude envelopes, and the attenuation rate of each instantaneous amplitude envelope is determined as the corresponding attenuation envelope feature. If both the attenuation envelope feature and the spectral centroid of a candidate traveling wave segment are within a preset distributed source injection component feature range, the candidate traveling wave segment is discarded. If either the attenuation envelope feature or the spectral centroid of a candidate traveling wave segment is within the distributed source injection component feature range, a suspicious flag is added to the candidate traveling wave segment, and the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment. If neither the attenuation envelope feature nor the spectral centroid of a candidate traveling wave segment is within the preset distributed source injection component feature range, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0077] It should be noted that the spectral centroid function is specifically:

[0078] ;

[0079] in, For the spectral centroid, For frequency index, denoted as the discrete Fourier transform coefficients of the candidate traveling wave segment.

[0080] Step 104: Based on the distribution network topology, perform propagation time consistency screening on each intermediate candidate traveling wave segment to obtain the corresponding target candidate traveling wave segment and propagation time consistency index.

[0081] The target candidate traveling wave segment refers to the valid candidate traveling wave segment retained after being screened for consistency of propagation time.

[0082] The propagation time consistency index refers to a continuous quantitative value that measures the degree of matching between the candidate wavefront time and the theoretical propagation time.

[0083] In this embodiment of the invention, the network is abstracted into a graph structure based on the distribution network topology, and all candidate propagation paths from potential fault sources to measurement points are generated through depth-first search. The theoretical propagation time is calculated based on the line length and propagation speed of each candidate propagation path. Based on a preset time consistency function, a propagation time consistency index is determined for each intermediate candidate traveling wave segment according to the theoretical propagation time. When the propagation time consistency index of an intermediate candidate traveling wave segment is greater than or equal to a preset consistency threshold, the intermediate candidate traveling wave segment is determined as the target candidate traveling wave segment.

[0084] Step 105: Based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, perform multi-measurement point collaborative identification of each target candidate traveling wave segment to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

[0085] In this embodiment of the invention, the arrival time features, local energy features, wavefront steepness features, and template matching degree features of each target candidate traveling wave segment are extracted. The arrival time features, local energy features, wavefront steepness features, template matching degree features, and propagation time consistency index corresponding to each target candidate traveling wave segment are normalized to obtain multiple multi-dimensional local features. Based on a preset Gaussian attenuation function, the collaborative confidence level of each target candidate traveling wave segment is determined according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the distribution network topology. Based on preset collaborative confidence level weights, a weighted average calculation is performed on the collaborative confidence levels of each target candidate traveling wave segment to obtain multiple multi-measurement point collaborative confidence levels. Based on preset evaluation weights, a weighted summation is performed on the multi-measurement point collaborative confidence levels and multi-dimensional local features corresponding to each target candidate traveling wave segment to obtain multiple evaluation scores. The target candidate traveling wave segment with the highest evaluation score and passing the consistency verification is selected as the dominant traveling wave corresponding to the aliased traveling wave signal.

[0086] It should be noted that after determining the candidate traveling wave segment with the highest evaluation score as the preliminary dominant traveling wave, the consistency check uses two key conditions to determine the reliability of the identification result: 1. Whether the difference between the evaluation score of the preliminary dominant traveling wave and the second highest evaluation score is less than a preset score difference threshold. 2. Whether the template matching degree feature of the preliminary dominant traveling wave is less than a preset template matching degree threshold. If either key condition is met, it indicates that there is uncertainty in the preliminary dominant traveling wave, and the threshold coefficient of the median absolute deviation function, the parameters of the asymmetric local time window, and the mutation weight need to be adjusted, and step 102 needs to be adjusted. This process continues until the number of jumps is greater than 3 or neither key condition is met.

[0087] In this embodiment of the invention, by acquiring the aliased traveling wave signal and distribution network topology of a multi-dominated power grid, the aliased traveling wave signal is preprocessed to obtain the corresponding target traveling wave signal. A pre-set differential step size is used to detect abrupt changes in the target traveling wave signal, resulting in multiple candidate traveling wave segments. Distributed power source interference is eliminated from each candidate traveling wave segment based on a pre-set spectral centroid function, resulting in corresponding intermediate candidate traveling wave segments. Based on the distribution network topology, propagation time consistency is screened for each intermediate candidate traveling wave segment, resulting in the corresponding target candidate traveling wave segment and propagation time consistency index. Multi-measurement point collaborative identification is performed on each target candidate traveling wave segment according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and each propagation time consistency index, thus obtaining the dominant traveling wave corresponding to the aliased traveling wave signal. This overcomes the technical problem that traditional dominant traveling wave discrimination methods often rely on the first arrival time, amplitude abrupt changes, or fixed threshold judgments of single-measurement point signals. However, in multi-dominated power grid scenarios, after the traveling wave propagates, reflects, and transmits through multiple branch lines, severe aliasing occurs at the measurement points, resulting in blurred wavefront characteristics and overlapping arrival times, making it difficult to directly distinguish between effective components and interference components. Compared with traditional methods for identifying dominant traveling waves, this invention first preprocesses the aliased traveling wave signal to improve signal quality, then combines abrupt change detection to quickly extract candidate traveling wave segments, and accurately eliminates pseudo wavefront interference injected by distributed power sources based on the spectral centroid function. This effectively reduces the impact of interference components on subsequent identification from the source. At the same time, it performs propagation time consistency screening based on the distribution network topology, making full use of the physical propagation laws to constrain the rationality of candidate segments. Then, it combines the arrival times of adjacent measurement points to complete multi-measurement point collaborative identification, achieving dual verification of single-measurement point signal characteristics and multi-measurement point spatial propagation consistency. In complex distribution network scenarios with multiple branches and high distributed power source penetration, it can significantly improve the decoupling effect of aliased traveling waves and the accuracy of dominant traveling wave identification.

[0088] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a dominant traveling wave identification method for a multi-dominated power grid, as provided in Embodiment 2 of the present invention.

[0089] This invention provides a method for identifying the dominant traveling wave in a multi-dominated power grid, comprising:

[0090] Step 201: Obtain the aliased traveling wave signal and distribution network topology of the multi-control power grid, preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal.

[0091] Further, step 201 includes the following sub-steps:

[0092] S11. Based on the preset sliding window length, perform baseline correction processing on the aliased traveling wave signal to obtain the corresponding baseline correction signal.

[0093] Baseline-corrected signal refers to a traveling wave signal that has been baseline-corrected to remove DC and baseline drift, resulting in a more regular waveform.

[0094] The sliding window length refers to the pre-set time window length used to calculate the local mean, with a value between 0.8 milliseconds and 1.5 milliseconds.

[0095] In this embodiment of the invention, the local mean of the aliased traveling wave signal is calculated point by point based on a preset sliding window length, thereby estimating the baseline drift and DC offset components of the signal. The corresponding local mean is subtracted from the aliased traveling wave signal to complete the DC removal process, effectively eliminating the slowly changing baseline interference and DC components in the signal, and obtaining a baseline correction signal with a purer waveform and no baseline offset.

[0096] It should be noted that the specific expression for DC removal is:

[0097] ;

[0098] in, The sampled value of the baseline correction signal at time t. The sampled value of the aliased traveling wave signal at time t. Calculate the local mean for the sliding window, where t is the time index.

[0099] S12. Perform bandpass filtering on the baseline correction signal to obtain the corresponding bandpass filtered signal.

[0100] In this embodiment of the invention, the baseline correction signal is bandpass filtered to retain the effective frequency band where the traveling wave transient component is located, and to filter out irrelevant components such as low-frequency power frequency drift, high-frequency sampling noise and equipment interference, so that the signal contains only the core traveling wave component suitable for wavefront detection, resulting in a bandpass filtered signal with clean amplitude and regular frequency band.

[0101] It should be noted that the specific expression for the bandpass filtered signal is:

[0102] ;

[0103] in, Let be the sampled value of the bandpass filtered signal at time t. This is a bandpass filter operator.

[0104] It should be noted that the effective frequency band refers to the frequency range that can truly reflect the characteristics of the traveling wave front of a fault, typically from 2kHz to 500kHz.

[0105] S13. Normalize the bandpass filtered signal to obtain the corresponding target traveling wave signal.

[0106] In this embodiment of the invention, the bandpass filtered signal is normalized by dividing the signal amplitude by the maximum value of its absolute value and introducing a small constant to avoid the denominator being zero. The signal amplitude is uniformly mapped to a standard range, eliminating the differences in signal amplitude under different measurement points and different fault scenarios, making the waveform characteristics consistent and comparable, thereby obtaining the target traveling wave signal that meets the requirements of subsequent mutation detection and decoupling identification.

[0107] It should be noted that the specific expression for the target traveling wave signal is:

[0108] ;

[0109] in, The sampled value of the target traveling wave signal at time t. To prevent the use of tiny constants with a denominator of zero, the value is taken as 10^{-8} to 10^{-6}.

[0110] See Figure 3 As shown, the target traveling wave signal contains a pseudo wavefront formed by distributed power injection at approximately 100 microseconds and a real fault traveling wave at approximately 200 microseconds.

[0111] Step 202: Use a preset differential step size to perform abrupt change detection on the target traveling wave signal to obtain multiple candidate traveling wave segments.

[0112] Further, step 202 includes the following sub-steps:

[0113] S21. Perform first-order difference operation on the target traveling wave signal based on the preset difference step size to obtain multiple first-order difference values.

[0114] The differential step size refers to the pre-set interval between adjacent sampling points, which is used to control the sensitivity and noise immunity of the first-order differential.

[0115] The first-order difference value refers to the point-by-point difference result obtained after the first-order difference operation, which is used to characterize the rate of change of the signal at the corresponding time.

[0116] In this embodiment of the invention, a first-order differential operation is performed on the target traveling wave signal based on a preset differential step size. By calculating the amplitude difference between adjacent sampling points, the fast abrupt change edges in the signal are highlighted and the change characteristics of the traveling wave front location are enhanced, resulting in multiple first-order differential values ​​that can accurately reflect the degree of waveform abrupt change.

[0117] It should be noted that the expression for the first-order difference value is as follows:

[0118] ;

[0119] in, The first-order difference response at time t For the difference step size, For the target traveling wave signal The sampled value at time.

[0120] S22. Perform local energy difference calculation on the target traveling wave signal based on the difference step size to obtain multiple local energy difference values.

[0121] The local energy difference value refers to the point-by-point result of the local energy difference operation, which is used to characterize the intensity of the energy mutation of the traveling wave.

[0122] In this embodiment of the invention, the target traveling wave signal and the differential step size are input into a preset local energy difference function to obtain multiple local energy difference values.

[0123] It should be noted that the local energy difference function is specifically as follows:

[0124] ;

[0125] in, Let be the local energy value at time t. The length of the local energy window. The sampled value of the target traveling wave signal at time k. for The local energy value, where k is the time index within the local energy window. Let be the local energy difference value at time t.

[0126] S23. Based on the preset mutation weights, the absolute values ​​of each first-order difference value and the absolute values ​​of the corresponding local energy difference values ​​are weighted and calculated to obtain multiple mutation detection indicators.

[0127] The mutation weight refers to the pre-set weighting coefficient of the first-order difference and the local energy difference, the sum of which is 1, used to balance sensitivity and noise immunity.

[0128] The mutation detection index refers to the point-by-point quantization value obtained after weighted fusion. The larger the value, the more significant the signal mutation at that point, and the more likely it is to be a traveling wave head.

[0129] In this embodiment of the invention, based on a preset mutation weight, the absolute values ​​of the first-order difference value and the absolute values ​​of the local energy difference value at each time point are weighted and summed respectively, and the amplitude mutation feature and the energy mutation feature are integrated into a unified quantitative index, which effectively takes into account both wavefront detection sensitivity and anti-interference stability, and obtains multiple mutation detection indicators that can comprehensively reflect the degree of signal mutation.

[0130] It should be noted that the specific expression for the mutation detection index is as follows:

[0131] ;

[0132] in, Let t be the mutation detection index. This is the first mutation weighting coefficient, with a value ranging from 0.4 to 0.7. This is the second mutation weighting coefficient, with a value ranging from 0.3 to 0.6.

[0133] It is worth mentioning that when the wavefront is steep and the noise is low, the first abrupt change weighting coefficient can be appropriately increased. When the noise is strong or the wavefront is weak, the second abrupt change weighting coefficient can be appropriately increased.

[0134] S24. Based on the preset median absolute deviation function, determine the corresponding adaptive threshold according to each mutation detection index.

[0135] In this embodiment of the invention, the mutation detection indicators are sorted from largest to smallest to obtain a mutation detection indicator sequence. The mutation detection indicator sequence is then input into a preset median absolute deviation function to obtain the corresponding adaptive threshold.

[0136] It should be noted that the expression for the median absolute deviation function is as follows:

[0137] ;

[0138] in, For adaptive threshold, This is a sequence of mutation detection indicators. This is the threshold coefficient, typically ranging from 2.5 to 6. This represents the median of the mutation detection index sequence. This represents the absolute deviation of the median.

[0139] S25. Based on the adaptive threshold and the preset interval constraint, each mutation detection index is screened to obtain the corresponding candidate wavefront time set.

[0140] Interval constraint refers to a pre-set minimum wavefront time interval, used to prevent the same wavefront from being detected repeatedly at multiple sampling points.

[0141] The candidate wavefront time set refers to the set of valid suspected traveling wavefront arrival times that are retained after threshold filtering and interval deduplication.

[0142] In the embodiments of the present invention, see Figure 4As shown, when the mutation detection index is greater than or equal to the adaptive threshold, the time corresponding to the mutation detection index is marked as the potential wavefront position (i.e., the maximum point greater than or equal to the adaptive threshold). Each wavefront position is deduplicated according to a preset interval constraint (i.e., the time interval between two adjacent potential wavefront positions is calculated; if the time interval between two adjacent potential wavefront positions is less than the interval constraint, the potential wavefront position with the smaller mutation detection index is removed to avoid multiple misjudgments of the same wavefront), resulting in a set of candidate wavefront times without duplication or noise interference.

[0143] S26. Construct asymmetric local time windows centered on each candidate wavefront time in the candidate wavefront time set, and use each asymmetric local time window to extract the target traveling wave signal to obtain multiple candidate traveling wave segments.

[0144] Asymmetric local time windows refer to truncation windows centered on the candidate wavefront moment with varying durations before and after it, being shorter before and longer after, thus taking into account both baseline and complete waveform information.

[0145] A candidate traveling wave segment refers to a local waveform captured centered on a candidate wavefront, which includes the suspected wavefront and the attenuation process.

[0146] In this embodiment of the invention, an asymmetric local time window is constructed with each candidate wavefront time in the candidate wavefront time set as the center. The asymmetric local time window has a shorter duration on the front side of the wavefront to retain baseline information and a longer duration on the back side of the wavefront to fully include the traveling wave attenuation waveform. Then, the target traveling wave signal is truncated using each asymmetric local time window to obtain multiple candidate traveling wave segments.

[0147] It should be noted that the specific expression for the asymmetric local time window is as follows:

[0148] ;

[0149] in, For the i1th candidate wavefront time, the asymmetric local time window is used. For candidate wavehead moments, The wavefront retention time is typically set between 10 μs and 80 μs. i1 is the retention time after the wavefront, which is usually set to 50μs to 300μs, and i1 is the candidate wavefront time index.

[0150] Step 203: Perform distributed power source interference removal processing on each candidate traveling wave segment based on the preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segments.

[0151] Furthermore, step 203 includes the following sub-steps:

[0152] S31. Input each candidate traveling wave segment into the preset spectral centroid function to obtain multiple spectral centroids.

[0153] The spectral centroid refers to the weighted average frequency calculated using the spectral amplitude as the weight, representing the main concentrated frequency band of signal energy.

[0154] In this embodiment of the invention, each candidate traveling wave segment is input into a preset spectral centroid function. The spectral distribution of the candidate traveling wave segment is obtained by performing a fast Fourier transform on the candidate traveling wave segment. Then, the weighted average frequency of each frequency component is calculated with the spectral amplitude as the weight, and the spectral centroid corresponding to each candidate traveling wave segment is obtained.

[0155] S32. Perform Hilbert transform on each candidate traveling wave segment to obtain multiple instantaneous amplitude envelopes, and determine the attenuation rate of each instantaneous amplitude envelope as the corresponding attenuation envelope feature.

[0156] The decay rate refers to a parameter obtained by fitting the instantaneous amplitude envelope, which characterizes how quickly the amplitude decays over time.

[0157] In this embodiment of the invention, each candidate traveling wave segment is subjected to Hilbert transform, and multiple instantaneous amplitude envelopes are obtained by constructing an analytical signal and extracting its magnitude. Then, each instantaneous amplitude envelope is subjected to linear fitting or exponential fitting to calculate the attenuation rate of the amplitude over time, and each attenuation rate is determined as the corresponding attenuation envelope feature.

[0158] It should be noted that an analytic signal refers to a complex signal composed of a candidate traveling wave segment and its Hilbert transform result, where the real part is the candidate traveling wave segment and the imaginary part is the Hilbert transform result.

[0159] S33. When the attenuation envelope feature corresponding to the candidate traveling wave segment is within the preset distributed power injection component feature range, it is determined whether the spectral centroid corresponding to the candidate traveling wave segment is within the distributed power injection component feature range.

[0160] The characteristic range of the injected components of distributed generation refers to the pre-established typical numerical range that includes the attenuation envelope characteristics and spectral centroid of the high-frequency injected components of inverter-type distributed generation.

[0161] In this embodiment of the invention, when the attenuation envelope feature corresponding to the candidate traveling wave segment is within the preset distributed power injection component feature range, it is further determined whether the spectral centroid corresponding to the candidate traveling wave segment is also within the distributed power injection component feature range.

[0162] S34. When the attenuation envelope feature corresponding to the candidate traveling wave segment is not within the characteristic range of the distributed source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0163] In this embodiment of the invention, when the attenuation envelope feature corresponding to the candidate traveling wave segment is not within the characteristic range of the distributed power injection component, the candidate traveling wave segment is taken as an intermediate candidate traveling wave segment.

[0164] S35. When the spectral centroid corresponding to a candidate traveling wave segment is not located in the characteristic range of the distributed source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0165] In this embodiment of the invention, when the spectral centroid corresponding to a candidate traveling wave segment is not located in the characteristic range of the distributed power injection component, the candidate traveling wave segment is used as an intermediate candidate traveling wave segment.

[0166] It is worth mentioning that, see Figure 5 As shown, the candidate traveling wave segment at approximately 100 microseconds (marked in red) is marked as a distributed source pseudo-wavehead and removed because its spectral centroid and attenuation envelope characteristics both fall within the characteristic range of the distributed source injection component. The candidate traveling wave segments at approximately 200 microseconds (marked in green) and approximately 400 microseconds (marked in green) are retained as intermediate candidate traveling wave segments and proceed to step 204.

[0167] Step 204: Perform a depth-first search based on the distribution network topology to obtain multiple candidate propagation paths.

[0168] Depth-first search is a graph traversal algorithm that prioritizes deeper exploration. It starts from the starting point, follows a path to the end, backtracks, and then explores other branches. It is suitable for finding all possible paths between two points.

[0169] Candidate propagation paths refer to all possible topological paths from the fault point to the current measurement point obtained through depth-first search.

[0170] In this embodiment of the invention, a depth-first search is performed based on the distribution network topology. Starting from the line where the fault is located and ending at the current measurement point, each node is recursively visited according to the rule of "prioritizing traversal in the depth direction". Topological elements such as lines, switches, and branch nodes are traversed in sequence and the access order is recorded. During the traversal, nodes that are visited repeatedly are pruned to avoid invalid paths, thereby obtaining multiple candidate propagation paths connecting the fault point and the current measurement point.

[0171] Step 205: Determine the theoretical propagation time corresponding to each candidate propagation path through a preset theoretical propagation time function.

[0172] In this embodiment of the invention, the theoretical propagation time corresponding to each candidate propagation path is obtained by uniformly calculating the propagation time based on the propagation speed of the line and the total topological length of each candidate propagation path into a preset theoretical propagation time function.

[0173] It should be noted that the theoretical propagation time function is specifically as follows:

[0174] ;

[0175] in, Let j be the theoretical propagation time corresponding to the j-th candidate propagation path. Let m be the length of the line segment. Let m be the propagation speed of the m-th segment of the line, where m is the index of the line. Let j be the j-th candidate propagation path, where j is the index of the candidate propagation path.

[0176] For overhead lines: =1.8×10 8 ~2.95×10 8 m / s.

[0177] For cable lines: =1.2×10 8 ~2.0×10 8 m / s.

[0178] Step 206: Based on the preset time consistency function, determine the propagation time consistency index corresponding to each intermediate candidate traveling wave segment according to each theoretical propagation time.

[0179] In this embodiment of the invention, based on a preset time consistency function, the theoretical propagation time corresponding to each candidate propagation path and the actual wavefront arrival time extracted from the intermediate candidate traveling wave segment are subjected to difference calculation and normalization processing. The degree of matching is quantified according to the size of the time deviation to obtain the propagation time consistency index corresponding to each intermediate candidate traveling wave segment.

[0180] It should be noted that the time consistency function is specifically as follows:

[0181] ;

[0182] in, Let be the propagation time consistency index corresponding to the i2th intermediate candidate traveling wave segment. To accommodate transmission time tolerance, Let i2 be the candidate wavefront time corresponding to the i2th intermediate candidate traveling wave segment, where i2 is the index of the intermediate candidate traveling wave segment.

[0183] Step 207: When the propagation time consistency index corresponding to the intermediate candidate traveling wave segment is greater than or equal to the preset consistency threshold, the intermediate candidate traveling wave segment is determined as the target candidate traveling wave segment.

[0184] The consistency threshold refers to a pre-set critical value used to determine whether the propagation time consistency index meets the standard. The value is determined based on the accuracy of the distribution network topology and the detection requirements, and the criteria for judging the time consistency are determined.

[0185] In this embodiment of the invention, when the propagation time consistency index corresponding to the intermediate candidate traveling wave segment is greater than or equal to the preset consistency threshold, it indicates that the measured wavefront arrival time of the intermediate candidate traveling wave segment is highly consistent with the theoretical propagation time of the corresponding candidate propagation path, and the intermediate candidate traveling wave segment is determined as the target candidate traveling wave segment.

[0186] Step 208: Based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, perform multi-measurement point collaborative identification of each target candidate traveling wave segment to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

[0187] Furthermore, step 208 includes the following sub-steps:

[0188] S41. Extract the arrival time features, local energy features, wavefront steepness features, and template matching degree features of each target candidate traveling wave segment.

[0189] The arrival time characteristic refers to the precise time parameter at which the wavefront of a traveling wave in a candidate traveling wave segment arrives at the detection point, and is used to characterize the temporal characteristics of traveling wave propagation.

[0190] Local energy characteristics refer to the cumulative signal energy of a target candidate traveling wave segment within the wavefront neighborhood window, reflecting the strength level of fault transient energy.

[0191] Wavefront steepness refers to the rate of change of the rising edge of a traveling wave's wavefront, characterized by first-order difference operations, reflecting the steepness and intensity of the wavefront abrupt change.

[0192] The specific expression for the wavefront steepness characteristic is as follows:

[0193] ;

[0194] in, Let be the wavefront steepness feature of the i-th target candidate traveling wave segment. Let be the signal amplitude of the i-th target candidate traveling wave segment at the k1-th sampling point. Let be the signal amplitude of the i-th target candidate traveling wave segment at the k1-1 sampling point, where i is the index of the target candidate traveling wave segment.

[0195] Template matching feature refers to the quantized value obtained by calculating the similarity between the target candidate traveling wave segment and the preset standard fault traveling wave template. The higher the value, the better the waveform similarity.

[0196] The specific expression for the template matching degree feature is as follows:

[0197] ;

[0198] in, The template matching degree feature of the i-th target candidate traveling wave segment is... For the i-th target candidate traveling wave segment, This is a standard traveling wave template set.

[0199] In this embodiment of the invention, the arrival time features, local energy features, wavefront steepness features, and template matching degree features of each target candidate traveling wave segment are extracted respectively, and the feature parameters are quantified from four dimensions: arrival time in the time domain, degree of local energy accumulation, degree of wavefront abrupt steepness, and similarity to the standard fault traveling wave template.

[0200] S42. Normalize the arrival time features, local energy features, wavefront steepness features, template matching degree features, and propagation time consistency index corresponding to each target candidate traveling wave segment to obtain multiple multidimensional local features.

[0201] Multidimensional local features refer to a set of features composed of arrival time features, local energy features, wavefront steepness features, template matching degree features, and propagation time consistency index, which are used to characterize the effectiveness of candidate traveling wave segments from multiple dimensions.

[0202] In this embodiment of the invention, the arrival time feature, local energy feature, wavefront steepness feature, template matching degree feature and propagation time consistency index corresponding to each target candidate traveling wave segment are normalized, and all feature values ​​are mapped to a unified [0,1] interval to eliminate the scoring bias caused by the difference in the scale and numerical magnitude between different features, thereby obtaining multiple multidimensional local features.

[0203] It should be noted that the normalization process is as follows:

[0204] ;

[0205] in, The original features after normalization. Original features The maximum value among all original features. It is the minimum value among all original features.

[0206] S43. Based on the preset Gaussian attenuation function, determine the collaborative confidence level of each target candidate traveling wave segment according to the pre-acquired set of arrival times of traveling waves at adjacent measuring points and the distribution network topology.

[0207] The set of arrival times of traveling waves at adjacent measuring points refers to the set of arrival times of all traveling waves detected by multiple measuring points adjacent to the current measuring point in the same transient event.

[0208] Measurement point co-confidence refers to a quantitative index used to measure whether a candidate traveling wave segment and its adjacent measurement points meet the physical propagation consistency. The higher the value, the higher the confidence that the candidate traveling wave is a real fault traveling wave.

[0209] In this embodiment of the invention, the theoretical time difference is determined based on the pre-acquired set of arrival times of traveling waves at adjacent measuring points and the distribution network topology. Each theoretical time difference, along with the corresponding target candidate traveling wave segment and the arrival times of traveling waves at adjacent measuring points, is input into a preset Gaussian attenuation function to obtain the measurement point collaborative confidence level corresponding to each target candidate traveling wave segment.

[0210] It should be noted that the Gaussian decay function is specifically as follows:

[0211] ;

[0212] in, Let be the collaborative confidence level of the measurement points for the i-th target candidate traveling wave segment. Let be the measured time difference between the i-th target candidate traveling wave segment and the j1-th arrival time of the adjacent measuring point. This is the time difference tolerance parameter. The theoretical time difference between the i-th target candidate traveling wave segment and the j-1-th arrival time of the adjacent measuring point is calculated based on the distribution network topology and line propagation speed. Let be the arrival time of the i-th target candidate traveling wave segment. It represents the arrival time of the j1th adjacent measuring point.

[0213] For pure overhead line sections: the time difference tolerance parameter is set to 5~10μs.

[0214] For pure cable line sections: the time difference tolerance parameter is set to 8~15μs.

[0215] Overhead-cable hybrid section: The time difference tolerance parameter is 10~20μs.

[0216] Includes distributed power supply access nodes: the time difference tolerance parameter is set to 15~20μs.

[0217] It is worth mentioning that, see Figure 6 As shown, the deviation of the target candidate traveling wave segment C1 is about 3 microseconds, and the confidence level of the measurement point collaboration is close to 1. The deviations of the near propagation path component C4, the reflected wave C3, and the far propagation path component C5 increase in sequence, and the confidence level of the measurement point collaboration decreases in sequence.

[0218] S44. Based on the preset collaborative confidence weights, perform a weighted average calculation on the collaborative confidence of the measurement points corresponding to each target candidate traveling wave segment to obtain multiple multi-measurement point collaborative confidences.

[0219] Multi-point collaborative confidence refers to the comprehensive confidence index obtained after weighted fusion of multiple measurement points, which is used to measure the physical consistency and authenticity of candidate traveling waves throughout the entire distribution network space.

[0220] Collaborative confidence weight refers to a pre-set weighting coefficient based on factors such as line type, measurement point reliability, and topological distance, used to distinguish the degree of contribution of different adjacent measurement points to collaborative confidence.

[0221] Weighted average calculation refers to a calculation method that sums and normalizes the confidence scores of multiple measurement points according to preset weights, making the results more consistent with actual propagation patterns and engineering credibility.

[0222] In this embodiment of the invention, based on the preset collaborative confidence weight, the collaborative confidence of each target candidate traveling wave segment under different combinations of adjacent measurement points is calculated by weighted averaging, which fully integrates the spatial verification information of multiple measurement points, eliminates the disturbance caused by abnormal deviations, and obtains multiple collaborative confidence of multiple measurement points that can comprehensively reflect the consistency of spatial propagation.

[0223] S45. Based on the preset evaluation weights, the multi-point collaborative confidence and multi-dimensional local features corresponding to each target candidate traveling wave segment are weighted and calculated to obtain multiple evaluation scores.

[0224] Evaluation weights refer to weighting coefficients pre-set based on the engineering application scenario and the importance of features, used to control the contribution ratio of each dimension of features in the comprehensive evaluation.

[0225] The evaluation score refers to a quantitative value obtained by integrating features from various dimensions and spatial collaborative information. The higher the score, the greater the likelihood that the candidate traveling wave is the dominant real traveling wave.

[0226] In this embodiment of the invention, based on preset evaluation weights, the multi-point collaborative confidence and multi-dimensional local features corresponding to each target candidate traveling wave segment are weighted and calculated to obtain multiple evaluation scores that can comprehensively reflect the authenticity and physical consistency of the traveling wave. For example, the evaluation score = first evaluation weight coefficient * arrival time feature + second evaluation weight coefficient * local energy feature + third evaluation weight coefficient * wavefront steepness feature + fourth evaluation weight coefficient * template matching degree feature + fifth evaluation weight coefficient * propagation time consistency index + sixth evaluation weight coefficient * multi-point collaborative confidence. Wherein, the first evaluation weight coefficient (usually 0.15~0.35) + the second evaluation weight coefficient (usually 0.10~0.30) + the third evaluation weight coefficient (usually 0.10~0.25) + the fourth evaluation weight coefficient (usually 0.10~0.25) + the fifth evaluation weight coefficient (usually 0.15~0.35) + the sixth evaluation weight coefficient (usually 0.15~0.25) = 1.

[0227] It is worth mentioning that for scenarios with high levels of branching and accurate topology information, the weight coefficient of the fifth evaluation can be appropriately increased. For scenarios with high noise levels, the weight coefficients of the second and fourth evaluations can be appropriately increased.

[0228] S46. Select the target candidate traveling wave segment corresponding to the maximum value among the evaluation scores as the dominant traveling wave corresponding to the aliased traveling wave signal.

[0229] In this embodiment of the invention, all evaluation scores are traversed and the item with the largest value is selected. The target candidate traveling wave segment corresponding to the highest score is determined as the dominant traveling wave corresponding to the current aliasing traveling wave signal.

[0230] See Figure 7 As shown, the target candidate traveling wave segment C1 scored highly in six aspects: arrival time, local energy, wavefront steepness, template matching degree, propagation time consistency, and multi-measurement point collaborative confidence. Moreover, its evaluation score was significantly higher than that of the other target candidate traveling wave segments, and it was selected as the dominant traveling wave output.

[0231] In this embodiment of the invention, by acquiring the aliased traveling wave signal and distribution network topology of a multi-dominated power grid, the aliased traveling wave signal is preprocessed to obtain the corresponding target traveling wave signal. A pre-set differential step size is used to detect abrupt changes in the target traveling wave signal, resulting in multiple candidate traveling wave segments. Distributed power source interference is eliminated from each candidate traveling wave segment based on a pre-set spectral centroid function, resulting in corresponding intermediate candidate traveling wave segments. Based on the distribution network topology, propagation time consistency is screened for each intermediate candidate traveling wave segment, resulting in the corresponding target candidate traveling wave segment and propagation time consistency index. Multi-measurement point collaborative identification is performed on each target candidate traveling wave segment according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and each propagation time consistency index, thus obtaining the dominant traveling wave corresponding to the aliased traveling wave signal. This overcomes the technical problem that traditional dominant traveling wave discrimination methods often rely on the first arrival time, amplitude abrupt changes, or fixed threshold judgments of single-measurement point signals. However, in multi-dominated power grid scenarios, after the traveling wave propagates, reflects, and transmits through multiple branch lines, severe aliasing occurs at the measurement points, resulting in blurred wavefront characteristics and overlapping arrival times, making it difficult to directly distinguish between effective components and interference components. Compared with traditional methods for identifying dominant traveling waves, this invention first preprocesses the aliased traveling wave signal to improve signal quality, then combines abrupt change detection to quickly extract candidate traveling wave segments, and accurately eliminates pseudo wavefront interference injected by distributed power sources based on the spectral centroid function. This effectively reduces the impact of interference components on subsequent identification from the source. At the same time, it performs propagation time consistency screening based on the distribution network topology, making full use of the physical propagation laws to constrain the rationality of candidate segments. Then, it combines the arrival times of adjacent measurement points to complete multi-measurement point collaborative identification, achieving dual verification of single-measurement point signal characteristics and multi-measurement point spatial propagation consistency. In complex distribution network scenarios with multiple branches and high distributed power source penetration, it can significantly improve the decoupling effect of aliased traveling waves and the accuracy of dominant traveling wave identification.

[0232] Please see Figure 8 , Figure 8 This is a structural block diagram of a dominant traveling wave identification system for a multi-dominated power grid provided in Embodiment 3 of the present invention.

[0233] This invention provides a dominant traveling wave identification system for a multi-dominated power grid, comprising:

[0234] Preprocessing module 301 is used to acquire the aliased traveling wave signal and distribution network topology of the multi-distribution power grid, preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal;

[0235] The mutation detection module 302 is used to perform mutation detection on the target traveling wave signal using a preset differential step size to obtain multiple candidate traveling wave segments;

[0236] The interference removal module 303 is used to perform distributed power interference removal processing on each candidate traveling wave segment based on a preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segment.

[0237] The filtering module 304 is used to perform propagation time consistency filtering on each intermediate candidate traveling wave segment based on the distribution network topology, and obtain the corresponding target candidate traveling wave segment and propagation time consistency index.

[0238] The identification module 305 is used to perform multi-point collaborative identification of each target candidate traveling wave segment based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, so as to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

[0239] Furthermore, the preprocessing module 301 includes:

[0240] The baseline correction submodule is used to perform baseline correction processing on the aliased traveling wave signal based on a preset sliding window length to obtain the corresponding baseline correction signal.

[0241] The bandpass filter submodule is used to perform bandpass filtering on the baseline correction signal to obtain the corresponding bandpass filtered signal;

[0242] The normalization submodule is used to normalize the bandpass filtered signal to obtain the corresponding target traveling wave signal.

[0243] Furthermore, the mutation detection module 302 includes:

[0244] The first-order difference numerator module is used to perform first-order difference operations on the target traveling wave signal based on a preset difference step size to obtain multiple first-order difference values.

[0245] The local energy difference module is used to perform local energy difference calculations on the target traveling wave signal based on the difference step size, and obtain multiple local energy difference values.

[0246] The weighting submodule is used to perform weighted calculations on the absolute value of each first-order difference value and the absolute value of the corresponding local energy difference value based on preset mutation weights, so as to obtain multiple mutation detection indicators.

[0247] The median absolute deviation submodule is used to determine the corresponding adaptive threshold based on the preset median absolute deviation function and various mutation detection indicators.

[0248] The first screening submodule is used to screen various mutation detection indicators based on adaptive thresholds and preset interval constraints to obtain the corresponding candidate wavefront time set.

[0249] The interception submodule is used to construct asymmetric local time windows centered on each candidate wavefront time in the candidate wavefront time set, and to intercept the target traveling wave signal using each asymmetric local time window to obtain multiple candidate traveling wave segments.

[0250] Furthermore, the interference removal module 303 includes:

[0251] The spectral centroid submodule is used to input each candidate traveling wave segment into a preset spectral centroid function to obtain multiple spectral centroids;

[0252] The attenuation envelope submodule is used to perform Hilbert transform on each candidate traveling wave segment to obtain multiple instantaneous amplitude envelopes, and to determine the attenuation rate of each instantaneous amplitude envelope as the corresponding attenuation envelope feature.

[0253] The second screening submodule is used to determine whether the spectral centroid of the candidate traveling wave segment is within the distributed power injection component characteristic range when the attenuation envelope feature of the candidate traveling wave segment is within the preset distributed power injection component characteristic range.

[0254] When the attenuation envelope feature corresponding to the candidate traveling wave segment is not within the characteristic range of the distributed source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0255] When the spectral centroid of a candidate traveling wave segment is not located in the characteristic range of the distributed source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

[0256] Furthermore, the filtering module 304 includes:

[0257] The search submodule is used to perform a depth-first search based on the distribution network topology to obtain multiple candidate propagation paths;

[0258] The theoretical propagation time calculation submodule is used to determine the theoretical propagation time corresponding to each candidate propagation path through a preset theoretical propagation time function;

[0259] The propagation time consistency calculation submodule is used to determine the propagation time consistency index corresponding to each intermediate candidate traveling wave segment based on the preset time consistency function and the theoretical propagation time.

[0260] The third screening submodule is used to determine the intermediate candidate traveling wave segment as the target candidate traveling wave segment when the propagation time consistency index corresponding to the intermediate candidate traveling wave segment is greater than or equal to the preset consistency threshold.

[0261] Furthermore, the identification module 305 includes:

[0262] The extraction submodule is used to extract the arrival time features, local energy features, wavefront steepness features, and template matching degree features of each target candidate traveling wave segment.

[0263] The multidimensional local feature submodule is used to normalize the arrival time feature, local energy feature, wavefront steepness feature, template matching degree feature and propagation time consistency index corresponding to each target candidate traveling wave segment, and obtain multiple multidimensional local features.

[0264] The single-point collaborative confidence submodule is used to determine the collaborative confidence level of each target candidate traveling wave segment based on a preset Gaussian attenuation function, according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the distribution network topology.

[0265] The weighted average submodule is used to perform a weighted average calculation on the collaborative confidence of each target candidate traveling wave segment corresponding to the measurement point based on the preset collaborative confidence weight, so as to obtain multiple multi-measurement point collaborative confidence.

[0266] The evaluation submodule is used to perform weighted calculations on the multi-point collaborative confidence and multi-dimensional local features corresponding to each target candidate traveling wave segment based on preset evaluation weights, and obtain multiple evaluation scores.

[0267] The dominant traveling wave identification submodule is used to select the target candidate traveling wave segment corresponding to the maximum value among the various evaluation scores as the dominant traveling wave corresponding to the aliased traveling wave signal.

[0268] Please see Figure 9 , Figure 9 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0269] An electronic device according to an embodiment of the present invention includes: a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 performs the dominant traveling wave identification method for a multi-dominated power grid as described in any of the above embodiments.

[0270] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When these codes are run by a computing processing device, the device causes it to perform the various steps in the dominant traveling wave identification method for a multi-dominated power grid described above.

[0271] Embodiment 5 of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the dominant traveling wave identification method for a multi-dominated power grid as described in any of the above embodiments.

[0272] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the dominant traveling wave identification method for a multi-dominated power grid as described in any of the above embodiments.

[0273] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0274] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0275] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0276] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0277] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0278] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the dominant traveling wave in a multi-dominated power grid, characterized in that, include: Acquire the aliased traveling wave signal and distribution network topology of the multi-control power grid, preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal; The target traveling wave signal is subjected to abrupt change detection using a preset differential step size to obtain multiple candidate traveling wave segments; Based on the preset spectral centroid function, distributed power source interference removal processing is performed on each candidate traveling wave segment to obtain the corresponding intermediate candidate traveling wave segment. Based on the power distribution network topology, the propagation time consistency of each intermediate candidate traveling wave segment is screened to obtain the corresponding target candidate traveling wave segment and propagation time consistency index. Based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, multi-measurement point collaborative identification is performed on each of the target candidate traveling wave segments to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

2. The dominant traveling wave identification method for a multi-dominated power grid according to claim 1, characterized in that, The step of preprocessing the aliased traveling wave signal to obtain the corresponding target traveling wave signal includes: The aliased traveling wave signal is subjected to baseline correction processing based on a preset sliding window length to obtain the corresponding baseline correction signal; The baseline correction signal is subjected to bandpass filtering to obtain the corresponding bandpass filtered signal; The bandpass filtered signal is normalized to obtain the corresponding target traveling wave signal.

3. The dominant traveling wave identification method for a multi-dominated power grid according to claim 1, characterized in that, The step of performing abrupt change detection on the target traveling wave signal using a preset differential step size to obtain multiple candidate traveling wave segments includes: The target traveling wave signal is subjected to first-order difference operation based on a preset difference step size to obtain multiple first-order difference values. Based on the differential step size, local energy difference calculation is performed on the target traveling wave signal to obtain multiple local energy difference values; Based on the preset mutation weights, the absolute values ​​of each first-order difference value and the absolute values ​​of the corresponding local energy difference values ​​are weighted and calculated to obtain multiple mutation detection indicators. Based on the preset median absolute deviation function, the corresponding adaptive threshold is determined according to each of the mutation detection indicators. Based on the adaptive threshold and the preset interval constraint, each mutation detection index is screened to obtain the corresponding candidate wavefront time set. Asymmetric local time windows are constructed with each candidate wavefront time in the candidate wavefront time set as the center, and the target traveling wave signal is truncated using each of the asymmetric local time windows to obtain multiple candidate traveling wave segments.

4. The dominant traveling wave identification method for a multi-dominated power grid according to claim 1, characterized in that, The step of performing distributed power source interference removal processing on each candidate traveling wave segment based on a preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segment includes: Each candidate traveling wave segment is input into a preset spectral centroid function to obtain multiple spectral centroids; Each candidate traveling wave segment is subjected to Hilbert transform to obtain multiple instantaneous amplitude envelopes, and the attenuation rate of each instantaneous amplitude envelope is determined as the corresponding attenuation envelope feature; When the attenuation envelope feature corresponding to the candidate traveling wave segment is within the preset distributed power injection component feature range, it is determined whether the spectral centroid corresponding to the candidate traveling wave segment is within the distributed power injection component feature range. When the attenuation envelope feature corresponding to the candidate traveling wave segment is not within the feature range of the distributed power injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment. When the spectral centroid corresponding to the candidate traveling wave segment is not located in the characteristic range of the distributed power source injection component, the candidate traveling wave segment is determined as an intermediate candidate traveling wave segment.

5. The dominant traveling wave identification method for a multi-dominated power grid according to claim 1, characterized in that, The step of filtering the intermediate candidate traveling wave segments based on the distribution network topology to obtain the corresponding target candidate traveling wave segments and propagation time consistency index includes: A depth-first search was performed based on the power distribution network topology to obtain multiple candidate propagation paths; The theoretical propagation time for each candidate propagation path is determined by a preset theoretical propagation time function. Based on a preset time consistency function, the propagation time consistency index corresponding to each intermediate candidate traveling wave segment is determined according to each theoretical propagation time. When the propagation time consistency index corresponding to the intermediate candidate traveling wave segment is greater than or equal to the preset consistency threshold, the intermediate candidate traveling wave segment is determined as the target candidate traveling wave segment.

6. The dominant traveling wave identification method for a multi-dominated power grid according to claim 1, characterized in that, The step of performing multi-point collaborative identification of each target candidate traveling wave segment based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, to obtain the dominant traveling wave corresponding to the aliased traveling wave signal, includes: The arrival time features, local energy features, wavefront steepness features, and template matching degree features of each of the target candidate traveling wave segments are extracted respectively. The arrival time feature, local energy feature, wavefront steepness feature, template matching degree feature and propagation time consistency index corresponding to each of the target candidate traveling wave segments are normalized to obtain multiple multidimensional local features; Based on a preset Gaussian attenuation function, the collaborative confidence level of each target candidate traveling wave segment is determined according to the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the distribution network topology. Based on the preset collaborative confidence weight, the collaborative confidence of the measurement points corresponding to each of the target candidate traveling wave segments is weighted and averaged to obtain multiple multi-measurement point collaborative confidences. Based on the preset evaluation weights, the multi-point collaborative confidence and multi-dimensional local features corresponding to each of the target candidate traveling wave segments are weighted and calculated to obtain multiple evaluation scores; The target candidate traveling wave segment corresponding to the maximum value among the evaluation scores is selected as the dominant traveling wave corresponding to the aliased traveling wave signal.

7. A dominant traveling wave identification system for a multi-dominated power grid, characterized in that, include: The preprocessing module is used to acquire the aliased traveling wave signal and distribution network topology of the multi-distribution power grid, and to preprocess the aliased traveling wave signal to obtain the corresponding target traveling wave signal. The mutation detection module is used to perform mutation detection on the target traveling wave signal using a preset differential step size to obtain multiple candidate traveling wave segments; The interference removal module is used to perform distributed power interference removal processing on each candidate traveling wave segment based on a preset spectral centroid function to obtain the corresponding intermediate candidate traveling wave segment. The filtering module is used to filter the propagation time consistency of each intermediate candidate traveling wave segment based on the distribution network topology, and obtain the corresponding target candidate traveling wave segment and propagation time consistency index. The identification module is used to perform multi-point collaborative identification of each target candidate traveling wave segment based on the pre-acquired set of arrival times of traveling waves at adjacent measurement points and the consistency index of each propagation time, so as to obtain the dominant traveling wave corresponding to the aliased traveling wave signal.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the dominant traveling wave identification method for a multi-dominated power grid as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the dominant traveling wave identification method for a multi-dominated power grid as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the dominant traveling wave identification method for a multi-dominated power grid as described in any one of claims 1-6.

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