High-resistance grounding fault detection method and device, storage medium and electronic equipment

Abstract

The invention discloses a high-resistance grounding fault detection method and device, a storage medium and electronic equipment. The method relates to the technical field of fault detection, and comprises the following steps: carrying out continuous wavelet transform processing on a line mode voltage component of a power distribution network monitoring point in a preset time length to obtain a traveling wave full-wave signal representing a time-frequency characteristic; performing calculation processing based on the traveling wave full-wave signal to obtain initial energy of the traveling wave full-wave signal of the power distribution network monitoring point in the preset duration; when the initial energy is greater than or equal to a first preset threshold value, time-frequency feature extraction is carried out on the traveling wave full-wave signal to obtain a feature quantity representing an energy distribution feature, and the feature quantity comprises a target energy ratio and a sparse factor; and performing high-resistance grounding fault fusion detection based on the target energy ratio and the sparse factor to obtain a high-resistance grounding fault detection result of the power distribution network monitoring point within a preset time period. According to the method, the high-resistance fault detection accuracy can be improved, and the detection cost is saved.

Description

Technical Field

[0001] This invention relates to the field of fault detection technology, and in particular to a method, apparatus, storage medium, and electronic device for detecting high-resistance grounding faults. Background Technology

[0002] High-resistance faults are characterized by weak features and are often accompanied by electric arcs, making their characteristics complex. Therefore, high-resistance fault detection has always been a difficult point in fault selection for distribution networks. The characteristics of high-resistance grounding faults can be summarized as follows: (1) Weak fault current: When a high-resistance grounding fault occurs in a distribution network, its grounding medium is usually asphalt, sand, concrete, etc., and the corresponding grounding transition resistance is relatively high. The amplitude of the grounding point current is very small, usually less than 10% of the normal load current of the system. Foreign research experimental data show that the typical high-resistance grounding fault current ranges from 0 to 75A when grounded through different media. (2) Asymmetrical fault waveform: The waveform of the positive and negative half cycles of the high-resistance grounding fault current is asymmetrical. (3) Long fault duration: The high-resistance grounding fault current value is lower than the setting current value of conventional protection. Conventional protection is difficult to detect and isolate high-resistance faults. The fault can last for several hours or even several days. In addition, the grounding point is prone to dangerous accidents such as fires due to the continuous input of electrical energy from the distribution network. (4) The fault waveform is highly random. When a high-resistance grounding fault occurs, the grounding arc will cycle between burning and extinguishing. Furthermore, when the transmission line touches the grounding medium, the grounding arc will become even more unstable if there are windy or other weather conditions. These factors will cause the grounding circuit to intermittently conduct and disconnect, and the grounding fault current amplitude will also change suddenly at certain times. The fault current waveform is irregular and highly random, so it is not possible to detect high-resistance grounding faults by simply setting the current threshold. (5) The fault waveform is nonlinearly distorted. Since the equivalent resistance of the grounding medium is usually nonlinear, the harmonic content in the fault current is relatively high, resulting in nonlinear distortion of its waveform. In addition, the high-resistance fault current contains a lot of transient capacitive current, and its grounding resistance is high, causing the fault current waveform to have non-periodic characteristics. (6) High harmonic content and abundant high-frequency components: The unstable arc caused by a high-resistance grounding fault will result in a high proportion of harmonics and interharmonics in the fault current. Studies have shown that, compared with other frequency bands, the high-frequency components in the 2-10kHz frequency band account for a relatively high proportion in the typical arc grounding fault current, which can achieve better identification of high-resistance grounding faults. Currently, commonly used high-resistance arc fault models include the Mayr model, the Cassie model, the cybernetics model, and the nonlinear model. However, traditional high-resistance fault models cannot reflect the characteristics of arc faults and have poor detection effects. Summary of the Invention

[0003] In view of this, the present invention provides a method, device, storage medium and electronic device for detecting high-resistance grounding faults, the main purpose of which is to solve the problem of inaccurate detection of high-resistance faults.

[0004] To address the above problems, this application provides a method for detecting high-resistance grounding faults, comprising:

[0005] The line-mode voltage components of the distribution network monitoring points for a preset duration are subjected to continuous wavelet transform processing to obtain a traveling full-wave signal that characterizes the time-frequency features;

[0006] The initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration is obtained by performing calculations based on the traveling wave full-wave signal.

[0007] When the initial energy is greater than or equal to the first preset threshold, time-frequency features are extracted from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor.

[0008] Based on the target energy ratio and the sparsity factor, high-resistance grounding fault fusion detection is performed to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0009] Optionally, before performing continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration, the method further includes:

[0010] Real-time voltage signals from the power distribution network monitoring points are collected to obtain three-phase voltage traveling wave signals of a preset duration.

[0011] The three-phase voltage traveling wave signal is processed by phase-mode transformation using the Kelenberger transform method to obtain the line-mode voltage component.

[0012] Optionally, the step of performing continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal characterizing time-frequency features specifically includes:

[0013] Based on the voltage characteristics of the line-mode voltage components at the current distribution network monitoring points, a target wavelet basis function matching the voltage characteristics is determined;

[0014] Determine the analysis time window and scale range;

[0015] Based on the target wavelet basis function, the analysis time window, and the scale range, a predetermined continuous wavelet transform function is used to perform continuous wavelet transform processing on the line mode voltage component to obtain the traveling wave full-wave signal characterizing the time-frequency features.

[0016] Optionally, the step of calculating and processing the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the distribution network monitoring point for the preset duration specifically includes:

[0017] The initial energy is obtained by performing coefficient modulus integration on the traveling wave full-wave signal using a preset traveling wave full-wave total energy function.

[0018] Optionally, the step of extracting time-frequency features from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution features specifically includes:

[0019] Obtain the energy value of the subsequent scheduled number of time windows for the current time window;

[0020] Each energy value is divided by the initial energy to obtain the energy ratio.

[0021] The energy ratios are filtered to obtain the target energy ratios that characterize the energy distribution.

[0022] Optionally, the step of extracting time-frequency features from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution further includes:

[0023] The traveling wave full-wave signal is divided into blocks in both the time and frequency dimensions to obtain multiple time-frequency blocks;

[0024] The energy of each time-frequency block is calculated to obtain the time-frequency energy spectrum matrix;

[0025] Based on the time-frequency energy spectrum matrix, characteristic quantity calculations are performed to obtain the sparsity factor characterizing the energy distribution.

[0026] Optionally, the step of performing high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period specifically includes:

[0027] When the target energy ratio is less than or equal to the second preset threshold and the sparsity factor is greater than or equal to the third preset threshold, the detection result is determined to be a high-resistance grounding fault, and an early warning is issued.

[0028] When the target energy ratio is greater than the second preset threshold and / or the sparsity factor is less than the third preset threshold, the detection result is determined to be a normal transient disturbance.

[0029] To address the aforementioned problems, this application provides a high-resistance grounding fault detection device, comprising:

[0030] The continuous wavelet transform module is used to perform continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal that characterizes the time-frequency features.

[0031] The calculation module is used to perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration.

[0032] The feature extraction module is used to extract time-frequency features from the traveling wave full-wave signal when the initial energy is greater than or equal to a first preset threshold, and obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor.

[0033] The fusion detection module is used to perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor, and obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0034] To address the aforementioned problems, this application provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the high-resistance grounding fault detection method described above.

[0035] To address the aforementioned problems, this application provides an electronic device, comprising at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the high-resistance grounding fault detection method described above.

[0036] The beneficial effects of this application are as follows: This application utilizes continuous wavelet transform to obtain the full waveform of the traveling wave within a time window, establishes a time-frequency energy spectrum matrix to characterize the frequency-dependent energy variation of the traveling wave; constructs a start-up criterion based on the energy mutation of the full waveform of the traveling wave; uses wavelet energy entropy quantification to describe the energy distribution characteristics of high-resistivity grounding faults and normal transient disturbances in the time-frequency domain, and uses a sparse factor to reflect the uniformity of the time-frequency energy distribution of high-resistivity grounding faults and normal transient disturbances, constructing a detection criterion to improve the detection sensitivity of high-resistivity grounding fault traveling wave signals and reliably identify high-resistivity grounding faults.

[0037] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 A flowchart illustrating a high-resistance grounding fault detection method provided in an embodiment of this application is shown.

[0040] Figure 2 A flowchart illustrating a high-resistance grounding fault detection method provided in an embodiment of this application is shown.

[0041] Figure 3 A structural block diagram of a high-resistance grounding fault detection device provided in an embodiment of this application is shown. Detailed Implementation

[0042] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0043] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0044] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0045] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0046] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0047] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0048] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0049] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0050] This application provides a method for detecting high-resistance grounding faults, such as... Figure 1 As shown, it includes:

[0051] Step S101: Perform continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal characterizing the time-frequency features;

[0052] In the specific implementation process of this step, based on the voltage characteristics of the line-mode voltage components at the current distribution network monitoring point, a target wavelet basis function matching the voltage characteristics is determined; an analysis time window and scale range are determined; and a predetermined continuous wavelet transform function is used to perform continuous wavelet transform processing on the line-mode voltage components according to the target wavelet basis function, the analysis time window, and the scale range to obtain the traveling wave full-wave signal characterizing the time-frequency characteristics.

[0053] Step S102: Perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration;

[0054] In the specific implementation process of this step, the coefficient modulus of the coefficients are calculated by integrating the total energy function of the traveling wave full waveform based on the traveling wave full wave signal to obtain the initial energy.

[0055] Step S103: When the initial energy is greater than or equal to the first preset threshold, time-frequency features are extracted from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor.

[0056] In this step, the energy values ​​of a predetermined number of subsequent time windows of the current time window are obtained; each energy value is divided by the initial energy to obtain energy ratios; these energy ratios are then filtered to obtain the target energy ratios that characterize the energy distribution. The traveling wave full-wave signal is divided into blocks in both the time and frequency dimensions to obtain multiple time-frequency blocks; the energy of each time-frequency block is calculated to obtain a time-frequency energy spectrum matrix; and feature calculations are performed on the time-frequency energy spectrum matrix to obtain a sparsity factor characterizing the energy distribution.

[0057] Step S104: Perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0058] In the specific implementation process of this step, when the target energy ratio is less than or equal to the second preset threshold and the sparsity factor is greater than or equal to the third preset threshold, the detection result is determined to be a high-resistance grounding fault, and an early warning is issued; when the target energy ratio is greater than the second preset threshold and / or the sparsity factor is less than the third preset threshold, the detection result is determined to be a normal transient disturbance.

[0059] This application utilizes continuous wavelet transform to obtain the full waveform of a traveling wave within a time window, establishes a time-frequency energy spectrum matrix to characterize the frequency-dependent energy variation of the traveling wave, constructs a start-up criterion based on the energy mutation of the full waveform of the traveling wave, and uses wavelet energy entropy quantification to describe the energy distribution characteristics of high-resistivity grounding faults and normal transient disturbances in the time-frequency domain. A sparse factor is used to reflect the uniformity of the time-frequency energy distribution of high-resistivity grounding faults and normal transient disturbances, constructing a detection criterion to improve the detection sensitivity of high-resistivity grounding fault traveling wave signals and reliably identify high-resistivity grounding faults.

[0060] Another embodiment of this application provides a different method for detecting high-resistance grounding faults, such as... Figure 2 As shown, it includes:

[0061] Step S201: Collect the real-time voltage signal of the power distribution network monitoring point to obtain a three-phase voltage traveling wave signal of a preset duration;

[0062] In the specific implementation of this step, the power distribution network monitoring points include substation outgoing line nodes, key branch points, and other locations; wideband voltage sensors, such as high-bandwidth Rogowski coils, can be used to acquire the three-phase voltage traveling wave signal u. A u B u C .

[0063] Step S202: The three-phase voltage traveling wave signal is processed by phase mode transformation using the Kelenberger transform method to obtain the line mode voltage component;

[0064] In the specific implementation process of this step, the Kelvin transform is used to decouple the three-phase voltage signal into independent modal components to eliminate inter-phase coupling interference and focus on the line modal component containing fault information. The mathematical expression is shown in the following mathematical formula (1):

[0065]

[0066] Among them, u0, u α and u β These are the line-mode voltage components corresponding to the three-phase voltage signals, used for subsequent analysis, as they are less affected by the grounding method and contain rich high-frequency transient information about faults.

[0067] Step S203: Perform continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal characterizing the time-frequency features;

[0068] In the specific implementation process of this step, based on the voltage characteristics of the line-mode voltage component of the current distribution network monitoring point, the target wavelet basis function matching the voltage characteristics is determined; according to the characteristics of the fault traveling wave signal (abrupt pulse signal), a wavelet similar to the signal shape should be selected to better capture its characteristics. The Daubechies (dbN) series, such as db4, db5 wavelets or the Symlet (symN) series wavelets can be used as wavelet basis functions. They have tight support, approximate symmetry and vanishing moment characteristics, which are very suitable for detecting the singularity and transient changes of the signal. Determine the range of values ​​of the analysis time window T and the scale factor a, i.e. the scale range; according to the target wavelet basis function, the analysis time window and the scale range, the predetermined continuous wavelet transform function is used to perform continuous wavelet transform processing on the line-mode voltage component to obtain the traveling wave full wave signal that characterizes the time-frequency characteristics. The mathematical expression of the predetermined continuous wavelet transform function is as follows:

[0069]

[0070] Where a is the scaling factor; b is the translation factor; f(t) represents the line-mode voltage component function; ψ(t) represents the target wavelet basis function; This is the energy normalization factor, ensuring consistent wavelet energy across different scales. In continuous wavelet transform, a and b are continuously changing, and the time-frequency window also moves continuously across the time-frequency surface, thus leading to information redundancy. Furthermore, in practical applications, especially computer information processing, continuous wavelets must be discretized. It's worth noting that although wavelet discretization analyzes continuous-time signals, it doesn't discretize time t, but rather the scale parameter a and translation parameter b. Typically, the discretization formulas for scale parameter a and translation parameter b are taken as... The mathematical expression for the corresponding discrete wavelet function is shown in the following mathematical formula (3):

[0071]

[0072] Where, ψ j,k (t) is the target wavelet basis function.

[0073] The mathematical expression for the discretized wavelet transform coefficients is shown in the following mathematical formula (4):

[0074]

[0075] in Represents ψ j,k (t) performs complex conjugate operation;

[0076] Its reconstructed mathematical expression is shown in the following mathematical formula (5):

[0077]

[0078] A larger scale factor corresponds to a lower frequency range, and a smaller scale factor corresponds to a higher frequency range. The translation factor determines the position of the wavelet function on the time axis; by changing the translation factor, the signal can be analyzed at different time points. By selecting appropriate wavelet basis functions and scale parameters, high-frequency components (corresponding to fault wavefronts) in traveling wave signals can be highlighted, thus providing accurate time information for fault location. Commonly used wavelet basis functions include Haar wavelets, Daubechies wavelets, and Symlet wavelets. Different wavelet basis functions have different characteristics and are suitable for different types of signal analysis. When selecting a wavelet basis function, factors such as signal characteristics, analysis objectives, and computational efficiency need to be considered.

[0079] Step S204: Perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration;

[0080] In this step, the initial energy is obtained by performing coefficient modulus integration calculation based on the traveling wave full-wave signal using a preset traveling wave full-wave total energy function. The initial energy of the traveling wave full-wave signal at the distribution network monitoring point for a preset duration is calculated. This energy is the integral of the continuous wavelet transform coefficient modulus, reflecting the overall intensity of the transient disturbance within that time period. The mathematical expression of the preset traveling wave full-wave total energy function is shown in the following formula (6):

[0081]

[0082] Where E1 represents the initial energy; M represents the number of scales (frequency); N represents the number of time points; |CWT(a i b j | Represents a scale i Time is b j The modulus of the wavelet coefficients at that point.

[0083] Step S205: When the initial energy is greater than or equal to the first preset threshold, obtain the energy values ​​of the subsequent predetermined number of time windows of the current time window;

[0084] In this step, when the initial energy is greater than or equal to a first preset threshold, the energy values ​​of the subsequent predetermined number of time windows of the current time window are obtained; when the initial energy is less than the first preset threshold, it is determined that the system is operating normally and there is no high-resistance grounding fault, and the detection process ends. When the initial energy is greater than or equal to the first preset threshold, it is determined that a disturbance has occurred, the high-resistance fault detection program is started, and the next step of in-depth analysis is initiated. The first line-mode voltage component of at least one distribution network monitoring point with a subsequent preset duration is obtained; continuous wavelet transform processing is performed on at least one of the first line-mode voltage components to obtain a first traveling wave full-wave signal characterizing the time-frequency features; based on at least one of the first traveling wave full-wave signals, calculation processing is performed to obtain the first initial energy E2, E3…E of the traveling wave full-wave signal of at least one distribution network monitoring point with the subsequent preset duration. n , where n is a positive integer representing the number of initial energies. The first preset threshold can be set according to actual needs.

[0085] Step S206: Perform division operations between each energy value and the initial energy to obtain each energy ratio;

[0086] In this step, each energy value is divided by the initial energy to obtain the energy ratio. For example, if the initial energies for the subsequent time windows (e.g., three) are E2, E3, and E4, the energy ratios are calculated as follows: as well as

[0087] Step S207: Filter each of the energy ratios to obtain the target energy ratio that characterizes the energy distribution features;

[0088] In the specific implementation process of this step, the energy ratios are screened to obtain the target energy ratio that characterizes the energy distribution. When there are n energy ratios, the minimum value is selected from the n energy ratios to obtain the target energy ratio.

[0089] Step S208: Divide the traveling wave full-wave signal into blocks in both the time and frequency dimensions to obtain multiple time-frequency blocks;

[0090] In the specific implementation of this step, when a disturbance occurs at any point in the power grid, the traveling wave waveform measured at the detection point can be represented by a time-frequency diagram, characterizing the differences in the time and frequency domains of the traveling wave signal under different operating conditions. The entire traveling wave waveform is decomposed into M frequency bands, and the continuous wavelet coefficients of each frequency band are divided into N time periods to obtain multiple time-frequency blocks. The mathematical expression for the energy of the time-frequency block of frequency band j in time period i is defined as shown in the following formula (7):

[0091]

[0092] Where, x j (k) represents the continuous wavelet coefficients corresponding to sampling point k within frequency band j; i1 and i N These are the sampling points at the beginning and end of the i-th time period.

[0093] Step S209: Calculate the energy of each time-frequency block to obtain the time-frequency energy spectrum matrix;

[0094] In this step, the energy of each time-frequency block is calculated and processed to obtain the time-frequency energy spectrum matrix E. M×N The mathematical expression for the time-frequency energy spectrum matrix can be expressed by the following formula (8):

[0095]

[0096] Step S210: Perform characteristic quantity calculation processing based on the time-frequency energy spectrum matrix to obtain the sparsity factor characterizing the energy distribution characteristics;

[0097] In the specific implementation process of this step, there is a significant difference in the time-frequency energy distribution between high-resistance grounding faults and normal transient disturbances. By calculating the energy distribution of the entire traveling wave waveform, a detection criterion can be constructed, which can efficiently, sensitively, and reliably identify high-resistance grounding faults. For high-resistance grounding faults, the energy distribution of the entire traveling wave waveform is uniform, and the effective data content in the characteristic energy matrix is ​​relatively large; for normal transient disturbances, the energy distribution of the entire traveling wave waveform is concentrated, and many elements of the characteristic energy matrix are close to 0, with less effective data content. Therefore, the concept of sparsity factor is introduced to measure the sparsity of the characteristic energy matrix, thereby reflecting the uniformity of the energy distribution of the entire traveling wave waveform. Since the elements in the characteristic energy matrix are not strictly equal to 0, a certain threshold needs to be given for differentiation. Based on the threshold, the effective data in the characteristic energy matrix is ​​filtered to realize the detection of high-resistance grounding faults. The mathematical expression of the sparsity factor can be expressed by the following formula (9):

[0098]

[0099] Here, S represents the sparsity factor, which indicates the uniformity of the time-frequency energy distribution. η is the energy threshold, and T represents the number of time-frequency blocks in the time-frequency energy spectrum matrix whose energy is greater than the energy threshold; the numerator represents the total number of time-frequency blocks in the time-frequency energy spectrum matrix. For high-resistivity grounding faults, the time-frequency energy distribution is uniform, the characteristic energy matrix contains a large amount of effective data, and the matrix has low sparsity. For normal transient disturbances, the time-frequency energy distribution is concentrated, many elements in the characteristic energy matrix are less than the energy threshold η, the effective data content is low, and the matrix has high sparsity. By reflecting the difference between high-resistivity grounding faults and normal transient disturbances through the uniformity of the energy distribution of the entire traveling wave waveform, accurate detection of high-resistivity grounding faults can be achieved.

[0100] Step S211: Perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0101] In this step, when the target energy ratio is less than or equal to the second preset threshold and the sparsity factor is greater than or equal to the third preset threshold, the detection result is determined to be a high-resistance grounding fault, and an early warning is issued; when the target energy ratio is greater than the second preset threshold and / or the sparsity factor is less than the third preset threshold, the detection result is determined to be a normal transient disturbance. The first and second preset thresholds can be set according to actual needs.

[0102] This application acquires real-time voltage signals from the distribution network monitoring points to obtain three-phase voltage traveling wave signals of a preset duration; performs phase-mode transformation processing on the three-phase voltage traveling wave signals using the Kelenberger transform method to obtain line-mode voltage components; performs continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points of the preset duration to obtain a traveling wave full-wave signal characterizing time-frequency features; performs calculation processing based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal of the distribution network monitoring points of the preset duration; when the initial energy is greater than or equal to a first preset threshold, the current time is obtained. The energy values ​​of subsequent predetermined number of time windows are calculated; each energy value is divided by the initial energy to obtain an energy ratio; the energy ratios are then filtered to obtain a target energy ratio characterizing the energy distribution; the traveling wave full-wave signal is divided into blocks in both the time and frequency dimensions to obtain multiple time-frequency blocks; the energy of each time-frequency block is calculated to obtain a time-frequency energy spectrum matrix; high-resistance grounding fault fusion detection is performed based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period. This application uses a dual-criteria fusion method to detect power grid fault states through both energy attenuation ratio criteria and time-frequency distribution pattern criteria, improving the system's safety and detection accuracy. It can detect extremely weak fault currents with transition resistances as high as several thousand ohms, solving the industry problem of high-resistance faults being difficult to detect due to their weak characteristics. Through time-frequency analysis and energy integration, it can detect extremely high transition resistance faults that cannot be identified by traditional methods. By abandoning wavefront identification, the limitations imposed by the complex structure of distribution networks on the traveling wave method are overcome, resulting in greater adaptability. Through time-frequency feature extraction and fusion, intelligent protection criteria are achieved, representing an advanced direction in technological development.

[0103] Another embodiment of this application provides a high-resistance grounding fault detection device, such as... Figure 3 As shown, it includes:

[0104] The continuous wavelet transform module 1 is used to perform continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal that characterizes the time-frequency features.

[0105] Calculation module 2 is used to perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration;

[0106] Feature extraction module 3 is used to extract time-frequency features from the traveling wave full-wave signal when the initial energy is greater than or equal to a first preset threshold, and obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include target energy ratio and sparsity factor.

[0107] The fusion detection module 4 is used to perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor, and obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0108] In the specific implementation process, the device further includes: an acquisition module, used to acquire the real-time voltage signal of the power distribution network monitoring point to obtain a three-phase voltage traveling wave signal of preset duration; and a phase mode transformation module, used to perform phase mode transformation processing on the three-phase voltage traveling wave signal using the Kelvin transform method to obtain the line mode voltage component.

[0109] In the specific implementation process, the continuous wavelet transform module 1 is specifically used to: determine the target wavelet basis function that matches the voltage characteristics of the line-mode voltage component of the current distribution network monitoring point; determine the analysis time window and scale range; and perform continuous wavelet transform processing on the line-mode voltage component using a predetermined continuous wavelet transform function according to the target wavelet basis function, the analysis time window and the scale range to obtain the traveling wave full-wave signal that characterizes the time-frequency characteristics.

[0110] In the specific implementation process, the calculation module 2 is specifically used to: perform coefficient modulus integral calculation based on the traveling wave full wave signal using a preset traveling wave full waveform total energy function to obtain the initial energy.

[0111] In the specific implementation process, the feature extraction module 3 is specifically used to: obtain the energy values ​​of a predetermined number of subsequent time windows of the current time window; perform division operations between each energy value and the initial energy to obtain each energy ratio; and perform filtering processing on each energy ratio to obtain the target energy ratio that characterizes the energy distribution features.

[0112] In the specific implementation process, the feature extraction module 3 is also used to: divide the traveling wave full-wave signal into blocks in the time dimension and frequency dimension respectively to obtain multiple time-frequency blocks; calculate and process the energy of each time-frequency block to obtain a time-frequency energy spectrum matrix; and perform feature quantity calculation and processing based on the time-frequency energy spectrum matrix to obtain a sparsity factor characterizing the energy distribution characteristics.

[0113] In specific implementation, the fusion detection module 4 is specifically used to: determine the detection result as a high-resistance grounding fault and issue an early warning when the target energy ratio is less than or equal to the second preset threshold and the sparsity factor is greater than or equal to the third preset threshold; and determine the detection result as a normal transient disturbance when the target energy ratio is greater than the second preset threshold and / or the sparsity factor is less than the third preset threshold.

[0114] This application utilizes continuous wavelet transform to obtain the full waveform of a traveling wave within a time window, establishes a time-frequency energy spectrum matrix to characterize the frequency-dependent energy variation of the traveling wave, constructs a start-up criterion based on the energy mutation of the full waveform of the traveling wave, and uses wavelet energy entropy quantification to describe the energy distribution characteristics of high-resistivity grounding faults and normal transient disturbances in the time-frequency domain. A sparse factor is used to reflect the uniformity of the time-frequency energy distribution of high-resistivity grounding faults and normal transient disturbances, constructing a detection criterion to improve the detection sensitivity of high-resistivity grounding fault traveling wave signals and reliably identify high-resistivity grounding faults.

[0115] Another embodiment of this application provides a storage medium storing a computer program, which, when executed by a processor, implements the following method steps:

[0116] Step 1: Perform continuous wavelet transform on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling full-wave signal that characterizes the time-frequency features;

[0117] Step 2: Perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration;

[0118] Step 3: When the initial energy is greater than or equal to the first preset threshold, time-frequency features are extracted from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor.

[0119] Step 4: Perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0120] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0122] The specific implementation process of the above method steps can be found in the embodiments of the above arbitrary high-resistance grounding fault detection method, which will not be repeated here.

[0123] This application utilizes continuous wavelet transform to obtain the full waveform of a traveling wave within a time window, establishes a time-frequency energy spectrum matrix to characterize the frequency-dependent energy variation of the traveling wave, constructs a start-up criterion based on the energy mutation of the full waveform of the traveling wave, and uses wavelet energy entropy quantification to describe the energy distribution characteristics of high-resistivity grounding faults and normal transient disturbances in the time-frequency domain. A sparse factor is used to reflect the uniformity of the time-frequency energy distribution of high-resistivity grounding faults and normal transient disturbances, constructing a detection criterion to improve the detection sensitivity of high-resistivity grounding fault traveling wave signals and reliably identify high-resistivity grounding faults.

[0124] Another embodiment of this application provides an electronic device, which can be a server. The electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. The program executed by the processor can implement the functions or steps of a high-resistance grounding fault detection method on the server side.

[0125] In one embodiment, an electronic device is provided, which can be a client. The electronic device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. The program of the electronic device, executed by the processor, can implement the functions or steps of a high-resistance grounding fault detection method on the client side.

[0126] Another embodiment of this application provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, performs the following method steps:

[0127] Step 1: Perform continuous wavelet transform on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling full-wave signal that characterizes the time-frequency features;

[0128] Step 2: Perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration;

[0129] Step 3: When the initial energy is greater than or equal to the first preset threshold, time-frequency features are extracted from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor.

[0130] Step 4: Perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

[0131] The specific implementation process of the above method steps can be found in the embodiments of the above arbitrary high-resistance grounding fault detection method, which will not be repeated here.

[0132] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for detecting high-resistance grounding faults, characterized in that, include: The line-mode voltage components of the distribution network monitoring points for a preset duration are subjected to continuous wavelet transform processing to obtain a traveling full-wave signal that characterizes the time-frequency features; The initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration is obtained by performing calculations based on the traveling wave full-wave signal. When the initial energy is greater than or equal to the first preset threshold, time-frequency features are extracted from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor. Based on the target energy ratio and the sparsity factor, high-resistance grounding fault fusion detection is performed to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

2. The method as described in claim 1, characterized in that, Before performing continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration, the method further includes: Real-time voltage signals from the power distribution network monitoring points are collected to obtain three-phase voltage traveling wave signals of a preset duration. The three-phase voltage traveling wave signal is processed by phase-mode transformation using the Kelenberger transform method to obtain the line-mode voltage component.

3. The method as described in claim 1, characterized in that, The continuous wavelet transform processing of the line-mode voltage components at the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal characterizing time-frequency features specifically includes: Based on the voltage characteristics of the line-mode voltage components at the current distribution network monitoring points, a target wavelet basis function matching the voltage characteristics is determined; Determine the analysis time window and scale range; Based on the target wavelet basis function, the analysis time window, and the scale range, a predetermined continuous wavelet transform function is used to perform continuous wavelet transform processing on the line mode voltage component to obtain the traveling wave full-wave signal characterizing the time-frequency features.

4. The method as described in claim 1, characterized in that, The calculation and processing based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the distribution network monitoring point for the preset duration specifically includes: The initial energy is obtained by performing coefficient modulus integration on the traveling wave full-wave signal using a preset traveling wave full-wave total energy function.

5. The method as described in claim 1, characterized in that, The step of extracting time-frequency features from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution features specifically includes: Obtain the energy value of the subsequent scheduled number of time windows for the current time window; Each energy value is divided by the initial energy to obtain the energy ratio. The energy ratios are filtered to obtain the target energy ratios that characterize the energy distribution.

6. The method as described in claim 1, characterized in that, The step of extracting time-frequency features from the traveling wave full-wave signal to obtain feature quantities characterizing the energy distribution characteristics further includes: The traveling wave full-wave signal is divided into blocks in both the time and frequency dimensions to obtain multiple time-frequency blocks; The energy of each time-frequency block is calculated to obtain the time-frequency energy spectrum matrix; Based on the time-frequency energy spectrum matrix, characteristic quantity calculations are performed to obtain the sparsity factor characterizing the energy distribution.

7. The method as described in claim 1, characterized in that, The high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor, to obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period, specifically includes: When the target energy ratio is less than or equal to the second preset threshold and the sparsity factor is greater than or equal to the third preset threshold, the detection result is determined to be a high-resistance grounding fault, and an early warning is issued. When the target energy ratio is greater than the second preset threshold and / or the sparsity factor is less than the third preset threshold, the detection result is determined to be a normal transient disturbance.

8. A high-resistance grounding fault detection device, characterized in that, include: The continuous wavelet transform module is used to perform continuous wavelet transform processing on the line-mode voltage components of the distribution network monitoring points for a preset duration to obtain a traveling wave full-wave signal that characterizes the time-frequency features. The calculation module is used to perform calculations based on the traveling wave full-wave signal to obtain the initial energy of the traveling wave full-wave signal at the power distribution network monitoring point for the preset duration. The feature extraction module is used to extract time-frequency features from the traveling wave full-wave signal when the initial energy is greater than or equal to a first preset threshold, and obtain feature quantities characterizing the energy distribution characteristics. The feature quantities include the target energy ratio and the sparsity factor. The fusion detection module is used to perform high-resistance grounding fault fusion detection based on the target energy ratio and the sparsity factor, and obtain the high-resistance grounding fault detection results of the distribution network monitoring points within a preset time period.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the high-resistance grounding fault detection method according to any one of claims 1-7.

10. An electronic device, characterized in that, It includes at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the high-resistance grounding fault detection method according to any one of claims 1-7.

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