Fault Detection Method for Distribution Networks Based on Generalized S-Transform with Variable Factor

Abstract

This invention relates to a distribution network fault detection method based on the generalized S-transform with variable factors, belonging to the field of distribution network fault detection technology. This invention obtains data such as time-frequency spectrum and transient energy through the generalized S-transform with variable factors, and constructs three line selection criteria: a comprehensive similarity coefficient of time-frequency spectrum, peak transient energy, and relative entropy of transient energy. Combining this with D-S evidence theory, it achieves multi-criteria fusion, enabling comprehensive analysis of line selection results based on multiple criteria, resulting in more accurate line selection. This invention can accurately select lines under different fault distances, fault lines, fault initial angles, fault grounding resistances, and noise environments, exhibiting good adaptability, sensitivity, and anti-interference capabilities. This invention converts the calculated transient energy characteristics into a confidence allocation function to construct evidence combinations, using different fault feature quantities to measure the confidence level and construct line selection criteria, thereby complementing and fusing them to obtain more accurate line selection results.

Description

Technical Field

[0001] This invention relates to the field of distribution network fault detection technology, specifically to a distribution network fault detection method based on generalized S-transform with variable factors. Background Technology

[0002] In a power system, the entire network consisting of substations of various voltage levels and transmission and distribution lines is called the power grid. It comprises three units: substations, transmission lines, and distribution lines. Among these, the distribution network, operating at voltage levels of 35kV and below, is a crucial link directly serving users and connecting substation units with power-consuming units, playing a vital role in power distribution. However, the distribution network is characterized by multiple voltage levels, complex network structure, diverse equipment types, numerous and widespread work sites, and a relatively poor safety environment, resulting in a relatively high number of safety risks. Statistics show that over 95% of power outages in the power grid are caused by faults in the distribution network.

[0003] The neutral point grounding methods in power distribution networks are divided into high-current neutral point grounding methods (i.e., high-current grounding methods) and low-current neutral point grounding methods (i.e., low-current grounding methods). In the case of a single-phase ground fault, the arc generated by a high-current grounding method cannot extinguish itself and must trip immediately. Conversely, in the case of a single-phase ground fault, the arc generated by a low-current grounding method can extinguish itself, and even if it does not trip immediately, it will not cause the same hazards as the high-current grounding method.

[0004] Single-phase grounding faults account for approximately 80% of all faults in low-current grounding systems. When a single-phase grounding fault occurs in a low-current grounding system, the capacitance voltage to ground of the two unaffected phases rises to the line voltage. The symmetry of the line voltage is not broken. When intermittent arcing occurs in the distribution network system, the process of arc ignition, extinction, and re-ignition can easily cause arcing overvoltage, damaging insulation and escalating the grounding fault into a phase-to-phase short circuit. Simultaneously, with the increasing scale of distribution networks and the growing number of feeders, the capacitive current is also constantly increasing, easily leading to a single-point fault escalating into a two-point or even multi-point fault, posing a significant threat to the safe and stable operation of the power system. According to the latest "Distribution Network Technical Guidelines," the technical principles for handling single-phase grounding faults in low-current grounding systems have been improved. After a single-phase grounding fault occurs, the previous "allowing the grid to continue operating for 2 hours" has been changed to "selective tripping." Therefore, according to the requirements of the latest guidelines, it is crucial to quickly locate the faulty line and fault point so that maintenance personnel can take timely and effective measures to handle the fault. Summary of the Invention

[0005] The present invention aims to solve the above-mentioned technical problems by providing a method for detecting faults in distribution networks based on generalized S-transform with variable factors.

[0006] To solve the above-mentioned technical problems, the technical solution provided by the present invention is as follows:

[0007] A method for fault detection in distribution networks based on generalized S-transform with variable factors includes the following steps:

[0008] Step 1:

[0009] First, monitor whether the zero-sequence voltage of the system exceeds 0.05-0.15 times the rated voltage of the busbar using voltage sensors on the busbar;

[0010] Step Two:

[0011] The transient zero-sequence current for 1 / 4 or 1 / 2 cycle after a fault is recorded and acquired by current sensors on each feeder.

[0012] Step 3:

[0013] By performing generalized S-transform with variable factors on the transient zero-sequence currents on each feeder, clear transient characteristic quantities in the time spectrum are obtained. Then, the transient signals in the time and frequency domain are subjected to data filtering and noise reduction processing based on generalized S-transform with variable factors, and three-phase synchronization error removal is applied.

[0014] Step Four:

[0015] The values ​​of the corresponding criterion feature quantities are calculated based on the time-frequency comprehensive similarity coefficient line selection method, the transient energy peak line selection method, and the transient energy relative entropy line selection method.

[0016] Step 5:

[0017] The corresponding fault criterion weight and bus criterion factor are calculated based on the characteristic values ​​of each criterion, thereby determining the mass function evidence value of each criterion. Then, the normalization constant K and the fused confidence function value are calculated based on the DS evidence theory.

[0018] Step Six:

[0019] The line corresponding to the maximum value of the merged trust function is identified as the faulty line, and the line selection process ends.

[0020] Preferably, the time-spectrum comprehensive similarity coefficient line selection method determines the faulty line by superimposing the similarity coefficient and the distance coefficient to form a comprehensive similarity coefficient.

[0021] Preferably, the formula for the similarity coefficient is as follows:

[0022]

[0023] Among them, |S ab The similarity coefficient Sab ≤ 1 is used to measure the similarity between two lines in the time-frequency domain. K represents the total number of frequency intervals and N represents the total number of time intervals. The smaller Sab is, the less similar the two samples are; the larger Sab is, the more similar they are. When Sab = 1, the two samples are basically the same. The similarity coefficient Sab includes both positive and negative numbers. The current direction of the faulty line is opposite to that of the non-faulty line. The similarity coefficient between the faulty line and the non-faulty line is often opposite in sign to that between the non-faulty lines.

[0024] Preferably, the formula for the distance coefficient is as follows:

[0025]

[0026] The distance coefficients are normalized according to the standard, as shown in the following formula:

[0027]

[0028] In the formula, R ab D represents the relative Euclidean distance. max The maximum Euclidean distance between samples can be expressed as:

[0029]

[0030] In the formula, GST max (k,n) and GST min (k,n) represent the samples of the larger and smaller overall values ​​in the generalized S matrix, respectively.

[0031] A more obvious similarity comparison between faulty and non-faulty lines in the time-frequency domain is performed, as shown in the following formula:

[0032]

[0033] The comprehensive similarity coefficient matrix is ​​normalized using the following formula:

[0034]

[0035] Where L represents the total number of lines. It represents the sum of the comprehensive similarity coefficients between line i and other lines, excluding line i itself.

[0036] Preferably, the formula for the transient energy peak line selection method is as follows:

[0037]

[0038] Preferably, the transient energy relative entropy line selection method involves summing the calculated transient energies of all lines at each frequency point to obtain the total transient energy of all lines at each frequency point, as shown in the following formula:

[0039]

[0040] The weights for the transient energy of all lines at each frequency point are calculated as follows:

[0041]

[0042] The relative entropy weights of all lines Combining equation (9) with equation (10), we can obtain the transient energy relative entropy between line i and line j as shown in equation (11):

[0043]

[0044] Summing the relative entropy between line i and all other lines yields the comprehensive relative entropy of line i's transient energy:

[0045]

[0046] Calculate the transient energy total relative entropy M for all lines. i (i = 1, 2, ..., L), then select the three lines with the largest transient energy comprehensive relative entropy, and sort the three transient energy comprehensive relative entropies from largest to smallest, recording the energy comprehensive relative entropies of the three lines as M. i1 M i2 M i3 The specific fault criteria are as follows:

[0047] (1) When the combined relative entropy of the transient energy corresponding to the three lines satisfies M i1 >M i2 +M i3 At that time, line i is the faulty line;

[0048] (2) If the above inequality is not satisfied, it is determined to be a bus fault.

[0049] By employing the above method, the present invention has the following advantages:

[0050] This invention obtains data such as time spectrum and transient energy through generalized S-transform with variable factors, and constructs three line selection criteria: time spectrum comprehensive similarity coefficient, transient energy peak value and transient energy relative entropy. Then, it combines DS evidence theory to achieve multi-criteria fusion, so that the line selection result of a single criterion can be comprehensively analyzed by multi-criteria fusion to obtain a more accurate line selection result.

[0051] This invention presents a multi-criteria fusion fault line selection method based on generalized S-transform with variable factors and DS evidence theory. It can accurately select the line under different fault distances, fault lines, fault initial angles, fault grounding resistances, and noise environments, and has good adaptability, sensitivity, and anti-interference.

[0052] This invention transforms time-frequency domain data obtained from generalized S-transform with variable factors and calculated transient energy characteristics into a confidence allocation function to construct evidence combination. Different fault feature quantities are used to measure the confidence level to construct line selection criteria, thereby complementing and integrating each other to obtain more accurate line selection results.

[0053] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the present invention;

[0056] Figure 2 This is an energy distribution diagram of the feeder of the present invention at various frequency points ((a) healthy line 133 feeder; (b) faulty line 134 feeder). Detailed Implementation

[0057] Specific embodiments of the invention will now be described in detail. Although the invention is described in conjunction with these specific embodiments, it should be understood that the invention is not intended to be limited to these specific embodiments. Rather, these embodiments are intended to cover alternative, modified, or equivalent embodiments that may be included within the spirit and scope of the invention as defined by the claims. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. The invention may be practiced without some or all of these specific details. In other instances, well-known processes have not been described in detail so as not to unnecessarily obscure the invention.

[0058] When used in conjunction with the terms "comprising," "method comprising," or similar language in this specification and appended claims, the singular forms "a," "some," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0059] The present invention will now be described in further detail with reference to the full text.

[0060] Combined with appendix Figure 1 and Figure 2 A method for fault detection in distribution networks based on generalized S-transform with variable factors includes the following steps:

[0061] Step 1:

[0062] First, the zero-sequence voltage of the system is monitored by voltage sensors on the bus to see if it exceeds 0.05-0.15 times the rated voltage of the bus. Specifically, data sampling can be performed at 0.5T-1.5T after a fault.

[0063] Step Two:

[0064] The transient zero-sequence current for 1 / 4 or 1 / 2 cycle after a fault is recorded and acquired by current sensors on each feeder.

[0065] Step 3:

[0066] By performing generalized S-transform with variable factors on the transient zero-sequence currents on each feeder, clear transient characteristic quantities in the time spectrum are obtained. Then, the transient signals in the time and frequency domain are subjected to data filtering and noise reduction processing based on generalized S-transform with variable factors, and three-phase synchronization error removal is applied.

[0067] Step Four:

[0068] The values ​​of the corresponding criterion feature quantities are calculated based on the time-frequency comprehensive similarity coefficient line selection method, the transient energy peak line selection method, and the transient energy relative entropy line selection method.

[0069] Step 5:

[0070] The corresponding fault criterion weight and bus criterion factor are calculated based on the characteristic values ​​of each criterion, thereby determining the mass function evidence value of each criterion. Then, the normalization constant K and the fused confidence function value are calculated based on the DS evidence theory.

[0071] Step Six:

[0072] The line corresponding to the maximum value of the merged trust function is identified as the faulty line, and the line selection process ends.

[0073] The aforementioned time-spectrum comprehensive similarity coefficient line selection method uses the superposition of similarity coefficients and distance coefficients to form a comprehensive similarity coefficient to determine faulty lines.

[0074] The formula for the similarity coefficient is as follows:

[0075]

[0076] Among them, |S ab The similarity coefficient Sab ≤ 1 is used to measure the similarity between two lines in the time-frequency domain. K represents the total number of frequency intervals and N represents the total number of time intervals. The smaller Sab is, the less similar the two samples are; the larger Sab is, the more similar they are. When Sab = 1, the two samples are basically the same. The similarity coefficient Sab includes both positive and negative numbers. The current direction of the faulty line is opposite to that of the non-faulty line. The similarity coefficient between the faulty line and the non-faulty line is often opposite in sign to that between the non-faulty lines.

[0077] The formula for the distance coefficient is as follows:

[0078]

[0079] The distance coefficients are normalized according to the standard, as shown in the following formula:

[0080]

[0081] In the formula, R ab D represents the relative Euclidean distance. max The maximum Euclidean distance between samples can be expressed as:

[0082]

[0083] In the formula, GST max (k,n) and GST min (k,n) represent the samples of the larger and smaller overall values ​​in the generalized S matrix, respectively.

[0084] A more obvious similarity comparison between faulty and non-faulty lines in the time-frequency domain is performed, as shown in the following formula:

[0085]

[0086] The comprehensive similarity coefficient matrix is ​​normalized using the following formula:

[0087]

[0088] Where L represents the total number of lines. It represents the sum of the comprehensive similarity coefficients between line i and other lines, excluding line i itself.

[0089] The formula for the transient energy peak line selection method is as follows:

[0090]

[0091] The transient energy relative entropy line selection method involves summing the transient energies of all lines at each frequency point obtained from the calculations of each line, to obtain the total transient energy of all lines at each frequency point, as shown in the following formula:

[0092]

[0093] The weights for the transient energy of all lines at each frequency point are calculated as follows:

[0094]

[0095] The relative entropy weights of all lines Combining equation (9) with equation (10), we can obtain the transient energy relative entropy between line i and line j as shown in equation (11):

[0096]

[0097] Summing the relative entropy between line i and all other lines yields the comprehensive relative entropy of line i's transient energy:

[0098]

[0099] Calculate the transient energy total relative entropy M for all lines. i (i = 1, 2, ..., L), then select the three lines with the largest transient energy comprehensive relative entropy, and sort the three transient energy comprehensive relative entropies from largest to smallest, recording the energy comprehensive relative entropies of the three lines as M. i1 M i2 M i3 The specific fault criteria are as follows:

[0100] (1) When the combined relative entropy of the transient energy corresponding to the three lines satisfies M i1 >M i2 +M i3 At that time, line i is the faulty line;

[0101] (2) If the above inequality is not satisfied, it is determined to be a bus fault.

[0102] In specific implementation of this invention, the time-spectrum comprehensive similarity coefficient line selection method is as follows:

[0103] Similarity measurement is a measure of the degree of similarity between two samples. The more similar the samples, the greater the similarity; the more dissimilar the samples, the smaller the similarity. Common similarity criteria include correlation coefficient, similarity coefficient, distance coefficient (mainly Euclidean distance), and similarity deviation. The correlation coefficient primarily describes the morphological similarity between two samples, while the similarity coefficient primarily describes the numerical similarity, including the linear correlation and the differences in positive and negative characteristics between the samples. Here, calculating the similarity coefficient mainly highlights the numerical similarity of the samples, and combining it with the intuitive advantage of the distance coefficient, a comprehensive similarity coefficient is formed to compare the similarity of multiple lines, thereby filtering out faulty lines.

[0104] Here, we introduce the generalized S-transformation matrix GST(k,n) with variable factors to introduce the similarity coefficient, distance coefficient, and comprehensive similarity coefficient respectively. Let GST... a (k,n) and GST b (k,n) are the time-frequency spectra of the transient zero-sequence currents of lines a and b obtained by the generalized S-transformation.

[0105]

[0106] In the formula, A(k,n) is the magnitude matrix obtained after calculating the modulus of GST, denoted as the modulus matrix of GST. The phase matrix of the GST matrix shows that it effectively represents the time-frequency characteristics of the signal. Using a time-frequency characteristic map for representation is more intuitive and easier to understand compared to wavelet transform. Here, the actual frequency f is specifically noted. n Represented as f n =(f s / N)·n, where f s Let f be the sampling frequency, and n be the corresponding frequency sampling point. When n = 0, f n =0, then it is the DC component of GST.

[0107] Similarity coefficient:

[0108]

[0109] Among them, |S ab |≤1, used to measure the similarity between two lines in the time-frequency domain, where K represents the total number of frequency intervals and N represents the total number of time intervals. S ab The smaller the value of S, the less similar the two samples are. ab The larger the value of S, the greater the similarity. ab When the similarity coefficient S = 1, the two samples are essentially identical. abIt includes both positive and negative numbers. Since the current direction of the faulty line is opposite to that of the non-faulty line, the similarity coefficient between the faulty line and the non-faulty line is often opposite in sign to that between the non-faulty lines.

[0110] Distance coefficient (Euclidean distance):

[0111] Euclidean distance refers to the true distance between two points in n-dimensional space, or the natural length of a vector. This distance coefficient can intuitively and clearly represent the differences between samples.

[0112]

[0113] (3) Comprehensive similarity coefficient

[0114] To combine the distance coefficient with the similarity coefficient to form a comprehensive similarity coefficient, the distance coefficient is normalized, resulting in the following expression for the relative Euclidean distance:

[0115]

[0116] In the formula, R ab D represents the relative Euclidean distance. max The maximum Euclidean distance between samples can be expressed as:

[0117]

[0118] In the formula, GST max (k,n) and GST min (k,n) represent samples of the largest and smallest values ​​in the generalized S matrix, respectively. R ab The larger the value of R, the greater the distance between samples and the smaller the similarity. ab The smaller the value of R, the smaller the distance between samples. ab When = 0, the sample points are basically consistent.

[0119] Finally, define the comprehensive similarity coefficient C. ab The similarity coefficient S ab Similarity coefficient R ab The combined values ​​of the similarity coefficient and the distance coefficient are used to make a more obvious similarity comparison between faulty and non-faulty lines in the time and frequency domain.

[0120]

[0121] As can be seen from the above formula, due to the difference in R between the faulty line and the non-faulty line... ab It is very large, and S ab Since the number is negative, we subtract the two to maximize the fault characteristic of the faulty line. Cab The larger the value, the greater the degree of difference between samples. ab The smaller the value, the smaller the difference between samples. The transient zero-sequence current is transformed to the time-frequency domain using a generalized S-transform with variable factors. The comprehensive similarity coefficient matrix of the generalized S-transform matrix is ​​then calculated using comprehensive similarity coefficients. This allows for the acquisition of frequency information and phase-amplitude relationships of various signals within the time domain, thereby enabling the acquisition of richer fault characteristic information in the time-frequency domain and completing the time-frequency domain analysis and processing of the transient zero-sequence current.

[0122] To construct a more intuitive and clear line selection criterion, the comprehensive similarity coefficient matrix is ​​normalized here:

[0123]

[0124] The comprehensive similarity coefficient k of each route is obtained. i In the formula, L represents the total number of lines. It represents the sum of the comprehensive similarity coefficients between line i and other lines, excluding line i itself.

[0125] As can be seen from the above series of formulas, the comprehensive similarity coefficient matrix value between faulty lines and non-faulty lines will be significantly larger than the comprehensive similarity coefficient matrix value between other non-faulty lines, that is, the comprehensive similarity coefficient k of the faulty line. i It will be significantly larger than the comprehensive similarity coefficient k of other non-faulty lines. i To facilitate fault line selection, a threshold-based line selection criterion is constructed here, as follows:

[0126] (1) When k i ≥k set When (i = 1, 2, ..., L), line i is the faulty line;

[0127] (2) When k satisfies k for all lines i i <k set When (i = 1, 2, ..., L), it is determined to be a bus fault.

[0128] The calculation method for the transient energy peak line selection method is as follows:

[0129] At a single-phase ground fault point, there is a slow-decaying, small-amplitude transient inductive current and a fast-decaying, large-amplitude transient capacitive current. Therefore, the fault current of a small-current ground fault mainly consists of transient capacitive current. Thus, the fault line can be judged by the energy peak value of the transient zero-sequence current of the fault line.

[0130] According to the principles of physical electrical energy, the traditional formula for transient energy is:

[0131]

[0132] In the formula, W i (t) represents the transient energy time-domain function, u0(t) represents the bus zero-sequence voltage time-domain function, and i 0i (t) represents the time-domain function of the zero-sequence current of each feeder, where i = 1, 2, ..., L, and represents the feeder number. Since the zero-sequence voltage of the bus of each feeder is the same, the voltage can be omitted during integration. Combining this with the definition of the generalized S-transform mentioned above, the discrete zero-sequence current signal is discretized using the generalized S-transform, and the transient energy of each line at each frequency point can be obtained as follows:

[0133]

[0134] Based on the above principle, the fault transient zero-sequence current obtained through fault simulation is used to calculate the GST matrix through a generalized S-transform with variable factors. This matrix is ​​then substituted into equation (7) to obtain the transient energy distribution of each line at each frequency point. The corresponding transient energy distribution diagram is then plotted. Taking feeder 133 and feeder 134 as examples, for instance... Figure 2 As shown.

[0135] Based on the above energy distribution Figure 2 It can be seen that the peak transient energy of the faulty feeder 134 is about 50 times that of the healthy feeder 133. Therefore, the peak transient energy can be used as a characteristic criterion for fault line selection, and the difference is very obvious, which can effectively distinguish the faulty line from other healthy lines. Here, the peak transient energy E of all lines at all frequency points is calculated. imax (i = 1, 2, ..., L), then select the three lines with the largest transient energy peaks, sort the energy peaks from largest to smallest, and record the energy peaks of the three lines as E. i1max E i2max E i3max The specific fault criteria are as follows:

[0136] (1) When the transient energy peak values ​​corresponding to the three lines satisfy |E i1max |>|E i2max |+|E i3max When |, line i is the faulty line;

[0137] (2) If the above inequality is not satisfied, it is determined to be a bus fault.

[0138] The specific calculation method of the transient energy relative entropy line selection method is as follows:

[0139] Information entropy is a measure of the degree of uncertainty in the amount of information, while relative entropy, also known as Kullback-Leibler divergence or information divergence, is used to measure the degree of difference between the information of two samples. Suppose we have two samples P = {P1, P2, ..., P...} n} and Q = {Q1, Q2, ..., Q} n}, then the relative entropy between the two is:

[0140]

[0141] When the relative entropy D KL When (P||Q)=0, it means that the difference in information entropy between sample P and sample Q is 0, i.e., they overlap. As the relative entropy value increases, the difference between the two becomes greater. Therefore, the faulty line can be found by comparing the relative entropy of transient energy between lines.

[0142] First, sum the transient energies of all lines at each frequency point calculated in the previous section to obtain the total transient energy of all lines at each frequency point:

[0143]

[0144] Then, the weights of the transient energy of all lines at each frequency point are calculated as follows:

[0145]

[0146] As can be seen from the formula, the relative entropy weights of all lines are... Combining equation (9) and equation (10), we can obtain the transient relative entropy of energy between line i and line j as follows:

[0147]

[0148] Finally, summing the relative entropies between line i and all other lines (excluding line i) yields the transient energy comprehensive relative entropy of line i:

[0149]

[0150] Similarly, the transient energy comprehensive relative entropy M for all lines is also calculated here. i (i = 1, 2, ..., L), then select the three lines with the largest transient energy comprehensive relative entropy, and sort the three transient energy comprehensive relative entropies from largest to smallest, recording the energy comprehensive relative entropies of the three lines as M. i1 M i2 M i3 The specific fault criteria are as follows:

[0151] (1) When the combined relative entropy of the transient energy corresponding to the three lines satisfies M i1 >M i2 +M i3 At that time, line i is the faulty line;

[0152] (2) If the above inequality is not satisfied, it is determined to be a bus fault.

[0153] The multi-criteria fusion algorithm based on DS evidence theory is as follows:

[0154] DS Evidence Theory:

[0155] The Dempster-Simpson (DS) algorithm originates from the field of information fusion. In information processing, much information is uncertain, and the DS algorithm is a theoretical tool for intelligently processing and fusing uncertain information. It introduces the concept of a trust function, thus constructing a complete strategy for handling uncertain reasoning problems from the perspectives of "evidence" and "combination." Based on the three fault selection criteria proposed in this invention, the identification framework is confirmed, and the Basic Probability Assignment (BPA) function for all lines in each criterion is calculated, resulting in different BPA values ​​corresponding to different evidence theories. Then, the three mass functions are combined according to the Dempster synthesis rule to achieve the fusion of the results of the three fault selection criteria, obtaining the fused trust function value for fault line determination. Here, we will specifically introduce the basic principles of the DS evidence theory. The identification framework defined in the DS evidence theory can also be called the hypothesis space, represented as a non-empty set containing multiple mutually exclusive events, and all elements constitute a set containing all possibilities. For the fault selection problem of the small current grounding system studied in this invention, the corresponding identification framework can be defined as:

[0156] Θ={F0,F1,F2,…,F L} (15);

[0157] In the formula, F i (i = 1, 2, ..., L) indicates that line i has a fault, and F0 indicates that the bus has a fault.

[0158] In the recognition framework Θ, the BPA function is a 2 Θ →mass function in [0,1], and satisfying:

[0159]

[0160] In the formula, m(F) refers to the trust level assigned to all sets F in the recognition frame Θ. The sum of the trust levels of all sets in the recognition frame Θ is 1, while the sum of the trust levels of sets not in the recognition frame Θ is... Trust level is 0.

[0161] for The Dempster synthesis rule for the three mass functions, i.e., the three fault line selection criteria m1, m2, and m3, on the identification framework Θ is as follows:

[0162]

[0163] In the formula, K is the normalization constant, which is calculated as follows:

[0164]

[0165] Substituting equation (17) into equation (18) yields the confidence function values ​​of the bus and each line, which can then be used as the criterion for judging the faulty line after fusion.

[0166] Multi-criteria fusion-based fault route selection method:

[0167] When a single-phase ground fault occurs in a low-current grounding system, the three fault selection criteria will be calculated simultaneously to obtain the corresponding criterion values, and then the fault selection process based on the DS evidence theory multi-criterion fusion will begin. Here, we introduce the specific construction of the mass function of the three criteria used in this invention for evidence theory. First, the fault weights constructed from each criterion are as follows:

[0168]

[0169] In the formula, P1(F i P2(F) represents the weight of the comprehensive similarity coefficient for line i. i P3(F) represents the peak transient energy weight of line i. i Let represent the relative entropy weight of the transient energy of line i. To more clearly distinguish between line faults and bus faults, the following criterion factor for bus faults is introduced:

[0170]

[0171] In the formula, P jmax This represents the maximum fault weight among all lines for criterion j. The bus criterion factor is multiplied by the fault weight of each criterion to obtain the final mass function value for each criterion for all lines and the bus, as follows:

[0172]

[0173] In the formula, m j (F0) represents the evidence value of the busbar under criterion j, m j (F i ) represents the evidence value of line i under criterion j.

[0174] The present invention first monitors whether the zero-sequence voltage of the system exceeds the limit by using a voltage sensor on the bus. If the zero-sequence voltage of the system exceeds 0.05-0.15 times the rated voltage of the bus, the fault selection process begins.

[0175] (2) Record and acquire transient zero-sequence current for 1 / 4 or 1 / 2 cycle after a fault using current sensors on each feeder.

[0176] (3) By performing generalized S-transform with variable factors on the transient zero-sequence current on each feeder, clear transient characteristic quantities in the time spectrum are obtained. Then, the transient signal in the time and frequency domain is subjected to data filtering and noise reduction processing based on generalized S-transform with variable factors and the application of three-phase synchronization error removal.

[0177] (4) Calculate the values ​​of the corresponding criterion feature quantities based on the time-frequency comprehensive similarity coefficient line selection method, the transient energy peak line selection method, and the transient energy relative entropy line selection method;

[0178] (5) Calculate the corresponding fault criterion weight and bus criterion factor based on the characteristic values ​​of each criterion, thereby determining the mass function evidence value of each criterion, and then calculate the normalization constant K and the fused confidence function value based on the DS evidence theory.

[0179] (6) The line with the maximum value of BPA after fusion is the faulty line, and the line selection is completed.

[0180] This invention obtains data such as time spectrum and transient energy through generalized S-transform with variable factors, and constructs three line selection criteria: time spectrum comprehensive similarity coefficient, transient energy peak value and transient energy relative entropy. Then, it combines DS evidence theory to achieve multi-criteria fusion, so that the line selection result of a single criterion can be comprehensively analyzed by multi-criteria fusion to obtain a more accurate line selection result.

[0181] The present invention and its embodiments have been described above. This description is not restrictive, and the embodiments shown throughout are only one of the embodiments of the present invention. The actual structure is not limited to this. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A power distribution network fault detection method based on generalized S-transform with variable factors, characterized in that, Includes the following steps: Step 1: First, monitor whether the zero-sequence voltage of the system exceeds 0.05-0.15 times the rated voltage of the busbar using voltage sensors on the busbar; Step Two: The transient zero-sequence current for 1 / 4 or 1 / 2 cycle after a fault is recorded and acquired by current sensors on each feeder. Step 3: By performing generalized S-transform with variable factors on the transient zero-sequence current of each feeder, clear transient characteristic quantities in the time spectrum are obtained. Then, data filtering and noise reduction processing based on generalized S-transform with variable factors and three-phase synchronization error removal are performed on the transient signal in the time and frequency domain. Step Four: The values ​​of the corresponding criterion feature quantities are calculated based on the time-frequency comprehensive similarity coefficient line selection method, the transient energy peak line selection method, and the transient energy relative entropy line selection method. The aforementioned time-spectrum comprehensive similarity coefficient line selection method uses a comprehensive similarity coefficient formed by superimposing similarity coefficients and distance coefficients to determine faulty lines; Step 5: The corresponding fault criterion weight and bus criterion factor are calculated based on the characteristic values ​​of each criterion, thereby determining the mass function evidence value of each criterion. Then, the normalization constant K and the fused confidence function value are calculated based on the DS evidence theory. Step Six: The line corresponding to the maximum value of the merged trust function is identified as the faulty line, and the line selection process ends.

2. The generalized S-transform based power distribution network fault detection method with variable factor according to claim 1, characterized in that: The formula for the similarity coefficient is as follows: （1）； in, This is used to measure the similarity between two lines in the time-frequency domain. K represents the total number of frequency intervals, N represents the total number of time intervals, and S... ab The smaller the value of S, the less similar the two samples are. ab The larger the value of S, the greater the similarity. ab When the similarity coefficient S = 1, the two samples are basically the same, and the similarity coefficient S ab It includes positive and negative numbers. The current direction of the faulty line is opposite to that of the non-faulty line. The similarity coefficient between the faulty line and the non-faulty line is often opposite in sign to that between the non-faulty lines. GST a ( k , n ) and GST b ( k , n ) are the time-frequency spectra of the transient zero-sequence currents of lines a and b obtained by the generalized S-transformation.

3. The distribution network fault detection method based on generalized S-transform with variable factors according to claim 2, characterized in that: The formula for the distance coefficient is as follows: （2）。 4. The distribution network fault detection method based on generalized S-transform with variable factors according to claim 3, characterized in that: The distance coefficients are normalized according to the standard, as shown in the following formula: （3）； In the formula, R ab Indicates relative Euclidean distance. D max The maximum Euclidean distance between samples can be expressed as: （4）； In the formula, GST max ( k , n ) and GST min ( k , n ) represent the samples of the largest and smallest values ​​in the generalized S matrix, respectively; A more obvious similarity comparison between faulty and non-faulty lines in the time-frequency domain is performed, as shown in the following formula: （5）； The comprehensive similarity coefficient matrix is ​​normalized using the following formula: （6）； Where L represents the total number of lines. It represents the sum of the comprehensive similarity coefficients between line i and other lines, excluding line i itself.

5. The distribution network fault detection method based on generalized S-transform with variable factors according to claim 4, characterized in that: The formula for the transient energy peak line selection method is as follows: （7）。 6. The distribution network fault detection method based on generalized S-transform with variable factors according to claim 5, characterized in that: The transient energy relative entropy line selection method involves summing the transient energies of all lines at each frequency point obtained from the calculations of each line, to obtain the total transient energy of all lines at each frequency point, as shown in the following formula: （8）； The weights for the transient energy of all lines at each frequency point are calculated as follows: （9） The relative entropy weights of all lines Combining equation (9) and equation (10), we can obtain the circuit. i and lines j The transient relative entropy of energy between them is shown in equation (11): （10）； （11）； Line i The line can be obtained by summing its relative entropy with all other lines. i Transient energy comprehensive relative entropy: （12）； Calculate the transient energy total relative entropy for all lines. M i ( i =1, 2, …, L Then, the three lines with the largest transient energy comprehensive relative entropy were selected, and the three transient energy comprehensive relative entropies were sorted from largest to smallest. The energy comprehensive relative entropies of the three lines were recorded as follows: M 1. M 2. M 3. The specific fault criteria are as follows: (1) When the transient energy of the three lines is combined with the relative entropy, the total relative entropy is satisfied. M 1> M 2+ M At 3 o'clock, the line i This is the faulty circuit; (2) If the above inequality is not satisfied, it is determined to be a bus fault.

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