Tree-line Fault Characteristic Signal Detection Method Based on Zero-Sequence Voltage

Abstract

The present invention discloses a method for detecting characteristic signals of tree-line faults based on zero-sequence voltage, including: collecting the original discrete signals from the start of the increase in zero-sequence voltage to before the first stable decrease, smoothing the signals and calculating the effective value of the discrete zero-sequence voltage signal in each cycle; fitting the discrete zero-sequence voltage effective value signals to obtain its continuous function with respect to time; taking the second derivative function of the continuous function and judging the positive or negative of the value in the middle period of the second derivative function; calculating the second-order difference signal of the discrete zero-sequence voltage effective value; comparing the fluctuation amplitudes of the second-order difference signal in the first half of the time and the second half of the time; calculating the skewness of the second-order difference signal; comparing the Pearson correlation coefficients of the second-order difference signal in the first half of the time and the second half of the time; comprehensively analyzing whether a tree-line fault occurs; the present invention can be used to distinguish whether the distribution line fault is caused by tree-line contact, so as to achieve the purpose of quickly and effectively identifying faults.

Description

Technical Field

[0001] The present invention relates to the technical field of tree-line fault detection, and particularly to a method for detecting tree-line fault characteristic signals based on zero-sequence voltage. Background Art

[0002] Most of the distribution networks are neutral-point non-directly grounded systems. As the power transmission channel, the distribution lines are difficult to avoid crossing areas with many trees such as forests. Tree-line faults causing distribution line tripping and wildfires have become important problems affecting the safety of the power grid. The causes of tree-line faults are generally due to excessive growth of trees or contact with the distribution line under the action of wind deflection, resulting in tree-line discharge and ignition, thus causing faults in the distribution line. How to identify whether the line fault is a tree-line fault by detecting the change of the electrical signal of the distribution line is a major technical problem that urgently needs to be studied and solved. In the detection of distribution line faults, when the tree-line makes contact and discharges, the zero-sequence voltage changes violently, and the fluctuation degree of the second derivative of the zero-sequence voltage is much greater than that of the first derivative and the original data. By detecting and analyzing the zero-sequence voltage signal, it is possible to judge whether the line fault is a tree-line fault according to the different characteristics of the zero-sequence voltage signal during tree-line faults and other types of faults, providing a basis for analyzing the cause of the accident. Summary of the Invention

[0003] To solve the problems existing in the prior art, the object of the present invention is to provide a method for detecting tree-line fault characteristic signals based on zero-sequence voltage. The present invention can be used to distinguish whether the distribution line fault is caused by tree-line contact, achieving the purpose of quickly and effectively identifying the fault. Moreover, the present invention only needs to obtain the power frequency signal of the zero-sequence voltage of the line to effectively identify it, which is more conducive to the on-site operation and maintenance personnel to judge the fault type.

[0004] To achieve the above object, the technical solution adopted by the present invention is: a method for detecting tree-line fault characteristic signals based on zero-sequence voltage, including the following steps:

[0005] Step 1: Collect the original discrete signal from when the zero-sequence voltage starts to increase until before the first stable decrease, smooth the signal and calculate the effective value of the discrete zero-sequence voltage signal within each cycle;

[0006] Step 2: Fit the discrete zero-sequence voltage effective value signal to obtain its continuous function with respect to time;

[0007] Step 3: Take the second derivative of the continuous function and judge the positive or negative of the value in the middle period of the second derivative function;

[0008] Step 4: Calculate the second-order difference signal of the discrete zero-sequence voltage effective value;

[0009] Step 5: Compare the fluctuation amplitudes of the second-order difference signal in the first half of the time and the second half of the time;

[0010] Step 6: Calculate the skewness of the second-order difference signal;

[0011] Step 7: Compare the Pearson correlation coefficients of the second-order difference signal in the first half and the second half of the time;

[0012] Step 8: Comprehensively analyze whether a tree line fault has occurred.

[0013] As a further improvement of the present invention, the specific steps of step 1 are as follows:

[0014] Use the library function smooth in MATLAB to smooth the collected original signal, reduce the original signal noise, and then calculate the effective value of the discrete signal within each power frequency cycle;

[0015] The specific smoothing process is as follows:

[0016]

[0017] In the formula, A y [n] is the nth discrete signal after smoothing, N is the window size of smoothing, k is the window radius of smoothing, and y[i] is the ith discrete signal before smoothing;

[0018] The specific method for calculating the effective value of the discrete signal within each power frequency cycle is as follows:

[0019]

[0020] In the formula, U n is the effective value of the zero-sequence voltage within the nth power frequency cycle, M is the number of discrete signals within one power frequency cycle, and u x is the xth zero-sequence voltage discrete signal within one power frequency cycle.

[0021] As a further improvement of the present invention, the specific steps of step 2 are as follows:

[0022] Use the library functions polyfit and polyval in MATLAB to perform nth-order fitting on the discrete zero-sequence voltage effective value signal in step 1 to obtain its continuous function with respect to time, where n is a positive integer greater than 4.

[0023] As a further improvement of the present invention, the specific steps of step 3 are as follows:

[0024] Take the second derivative of the continuous function of the zero-sequence voltage effective value with respect to time obtained in step 2, and judge the positive and negative conditions of the second derivative at , where t max is the maximum value of time.

[0025] As a further improvement of the present invention, the specific steps of step 4 are as follows:

[0026] The effective value signal of the discrete zero-sequence voltage obtained in step 1 is second-order differenced with respect to time to obtain a quadratic approximation of the rate of change of the signal between adjacent points; wherein, the second-order differencing is specifically as follows:

[0027]

[0028] In the formula, F(n) is the nth value after second-order differencing, f(n + 2) is the (n + 2)th value of the effective value signal of the discrete zero-sequence voltage, f(n + 1) is the (n + 1)th value of the effective value signal of the discrete zero-sequence voltage, f(n) is the nth value of the effective value signal of the discrete zero-sequence voltage, and T is the power frequency period.

[0029] As a further improvement of the present invention, step 5 is specifically as follows:

[0030] Taking time as the scale, the effective value second-order difference signal of the discrete zero-sequence voltage obtained in step 4 is evenly divided into two segments, and the fluctuation amplitudes of these two segments of difference signals are compared;

[0031] The fluctuation amplitude is specifically as follows:

[0032]

[0033] In the formula, S1 is the fluctuation amplitude of the second-order difference signal in the first half of the time, S2 is the fluctuation amplitude of the second-order difference signal in the second half of the time, x i is the ith second-order difference signal, μ1 is the average amplitude of the second-order difference signal in the first half of the time, μ2 is the average amplitude of the second-order difference signal in the second half of the time, and A is the number of discrete second-order difference signals.

[0034] As a further improvement of the present invention, step 6 is specifically as follows:

[0035] Calculate the skewness of the effective value second-order difference signal of the discrete zero-sequence voltage obtained in step 4; the skewness is specifically as follows:

[0036]

[0037] In the formula, N is the number of discrete second-order difference signals, μ is the mean value of the discrete second-order difference signal, σ is the standard deviation of the discrete second-order difference signal, x i represents the ith discrete second-order difference signal, and γ is the skewness.

[0038] As a further improvement of the present invention, step 7 is specifically as follows:

[0039] Taking time as the scale, the effective value second-order difference signal of the discrete zero-sequence voltage obtained in step 4 is evenly divided into two segments, and the Pearson correlation coefficients of these two segments of difference signals are respectively calculated and compared;

[0040] The Pearson correlation coefficient is as follows:

[0041]

[0042] In the formula, r1 is the Pearson correlation coefficient of the second-order difference signal in the first half of the time, r2 is the Pearson correlation coefficient of the second-order difference signal in the second half of the time, t i is the time at the end of the i-th power frequency cycle, d i is the i-th second-order difference signal, is the mean value of the second-order difference signal in the first half of the time, is the mean value of the second-order difference signal in the second half of the time, is the mean value of the first half of the time, is the mean value of the second half of the time.

[0043] As a further improvement of the present invention, step 8 is specifically as follows:

[0044] In step 3, the second derivative function is always positive at ; in step 5, the fluctuation amplitude of the second-order difference signal of the discrete zero-sequence voltage effective value in the first half of the time is smaller than that in the second half of the time; in step 6, the absolute value of the skewness of the second-order difference signal of the discrete zero-sequence voltage effective value is less than 0.5; in step 7, the Pearson correlation coefficient of the second-order difference signal of the discrete zero-sequence voltage effective value in the first half is greater than 0, and the Pearson correlation coefficient in the second half is greater than that in the first half and greater than 0.3; among the above conditions, if three or more are satisfied, the probability that the line fault is a tree-line fault is relatively large, and if less than three are satisfied, the probability that the line fault is a tree-line fault is relatively small.

[0045] The present invention is used to identify whether the fault occurring in the line is a tree-line fault. The collected original discrete zero-sequence voltage signal is smoothed to reduce noise. At the same time, the second derivative function of the discrete zero-sequence voltage effective value signal and its fitting function, as well as the second-order difference signal of the discrete zero-sequence voltage effective value signal, are calculated; the fluctuation amplitude and Pearson correlation coefficient of the second-order difference signal in the first half of the time and the second half of the time are compared, and the skewness of the second-order difference signal is calculated; the four characteristic signals of the second derivative function, fluctuation amplitude, Pearson correlation coefficient, and skewness are comprehensively analyzed to determine whether a tree-line fault occurs, providing a technical basis for the identification of tree-line faults.

[0046] The beneficial effects of the present invention are:

[0047] Considering that the distribution line passes through the forest area, the tree-line contact failure may trigger other types of failures and forest fires, and other types of failures and forest fires may also cause failures in the distribution line passing through the forest area. Different types of failures are of great significance for the division of responsibility for fire accidents, and maintenance personnel also have different maintenance and treatment methods for different failure types, but it is very difficult to judge the occurrence sequence in engineering practice. The present invention smooths the collected original discrete zero-sequence voltage signals to reduce noise, and at the same time calculates the effective value signal of the discrete zero-sequence voltage and its second derivative function of the fitting function, as well as the second-order difference signal of the discrete zero-sequence voltage effective value signal. The fluctuation amplitudes and Pearson correlation coefficients of the first half and the second half of the second-order difference signal are compared, and the skewness of the second-order difference signal is calculated. The zero-sequence voltage characteristic signals of different failure types are not exactly the same. Therefore, analyzing the differences in the four characteristic signals of the second derivative function, fluctuation amplitude, Pearson correlation coefficient, and skewness of the zero-sequence voltage can facilitate maintenance personnel to judge whether a tree-line failure has occurred. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0050] Embodiment

[0051] As Figure 1 shown, a method for detecting the characteristic signal of the tree-line failure based on the zero-sequence voltage includes the following steps:

[0052] Step 1: Collect the original discrete signal from when the zero-sequence voltage starts to increase until the first stable decrease, smooth the signal, and calculate the effective value of the discrete zero-sequence voltage signal within each cycle;

[0053] The specific steps of Step 1 are as follows:

[0054] Use the library function smooth in MATLAB to smooth the collected original signal to reduce the original signal noise, and then calculate the effective value of the discrete signal within each power frequency cycle;

[0055] The specific smoothing process is as follows:

[0056]

[0057] In the formula, A y [n] is the nth discrete signal after smoothing, N is the window size of smoothing, k is the window radius of smoothing, and y[i] is the ith discrete signal before smoothing;

[0058] The specific method for calculating the effective value of the discrete signal within each power frequency period is as follows:

[0059]

[0060] In the formula, U n is the effective value of the zero-sequence voltage within the nth power frequency period, M is the number of discrete signals within one power frequency period, and u x is the xth discrete zero-sequence voltage signal within one power frequency period;

[0061] Step 2: Fit the discrete zero-sequence voltage effective value signal to obtain its continuous function with respect to time;

[0062] The specific method for Step 2 is as follows:

[0063] Use the library functions polyfit and polyval in MATLAB to perform an nth-order fit on the discrete zero-sequence voltage effective value signal obtained in Step 1 to obtain its continuous function with respect to time. To ensure the fitting accuracy, n is a positive integer greater than 4;

[0064] Step 3: Calculate the second derivative function of the continuous function and determine the sign of the value in the middle period of this second derivative function;

[0065] The specific method for Step 3 is as follows:

[0066] Calculate the second derivative function of the continuous function of the zero-sequence voltage effective value with respect to time obtained in Step 2, and determine the sign of this second derivative function at when, t max is the maximum value of time;

[0067] Step 4: Calculate the second-order difference signal of the discrete zero-sequence voltage effective value;

[0068] The specific method for Step 4 is as follows:

[0069] Perform a second-order difference on the discrete zero-sequence voltage effective value signal obtained in Step 1 with respect to time to obtain a quadratic approximation of the change rate of this signal between adjacent points;

[0070] The specific method for performing the second-order difference is as follows:

[0071]

[0072] In the formula, F(n) is the nth value after the second-order difference, f(n + 2) is the (n + 2)th value of the discrete zero-sequence voltage effective value signal, f(n + 1) is the (n + 1)th value of the discrete zero-sequence voltage effective value signal, f(n) is the nth value of the discrete zero-sequence voltage effective value signal, and T is the power frequency period;

[0073] Step 5: Compare the fluctuation amplitudes of the second-order difference signal in the first half and the second half of the time;

[0074] The specific steps of Step 5 are as follows:

[0075] Taking time as the scale, divide the discrete zero-sequence voltage effective value second-order difference signal obtained in Step 4 into two equal parts, and compare the fluctuation amplitudes of these two parts of the difference signals;

[0076] The specific fluctuation amplitude is as follows:

[0077]

[0078] In the formula: S1 is the fluctuation amplitude of the second-order difference signal in the first half of the time, S2 is the fluctuation amplitude of the second-order difference signal in the second half of the time, x i is the i-th second-order difference signal, μ1 is the average amplitude of the second-order difference signal in the first half of the time, μ2 is the average amplitude of the second-order difference signal in the second half of the time, and A is the number of discrete second-order difference signals;

[0079] Step 6: Calculate the skewness of the second-order difference signal;

[0080] The specific steps of Step 6 are as follows:

[0081] Calculate the skewness of the discrete zero-sequence voltage effective value second-order difference signal obtained in Step 4;

[0082] The specific skewness is as follows:

[0083]

[0084] In the formula, N is the number of discrete second-order difference signals, μ is the mean of the discrete second-order difference signals, σ is the standard deviation of the discrete second-order difference signals, x i represents the i-th discrete second-order difference signal, and γ is the skewness;

[0085] Step 7: Compare the Pearson correlation coefficients of the second-order difference signal in the first half and the second half of the time;

[0086] The specific steps of Step 7 are as follows:

[0087] Taking time as the scale, divide the discrete zero-sequence voltage effective value second-order difference signal obtained in Step 4 into two equal parts, and calculate and compare the Pearson correlation coefficients of these two parts of the difference signals respectively;

[0088] The specific Pearson correlation coefficient is as follows:

[0089]

[0090] Wherein, r1 is the Pearson correlation coefficient of the second-order difference signal in the first half of the time, r2 is the Pearson correlation coefficient of the second-order difference signal in the second half of the time, t i is the time at the end of the i-th power frequency cycle, d i is the i-th second-order difference signal, is the mean value of the second-order difference signal in the first half of the time, is the mean value of the second-order difference signal in the second half of the time, is the mean value in the first half of the time, is the mean value in the second half of the time;

[0091] Step 8: Comprehensively analyze whether a tree-line fault has occurred;

[0092] The specific steps of Step 8 are as follows:

[0093] In Step 3, the second derivative function is always positive when ; in Step 5, the fluctuation amplitude of the second-order difference signal of the discrete zero-sequence voltage effective value in the first half of the time is smaller than that in the second half of the time; in Step 6, the absolute value of the skewness of the second-order difference signal of the discrete zero-sequence voltage effective value is less than 0.5; in Step 7, the Pearson correlation coefficient of the second-order difference signal of the discrete zero-sequence voltage effective value in the first half is greater than 0, and the Pearson correlation coefficient in the second half is greater than that in the first half and greater than 0.3; among the above conditions, if three or more are satisfied, the probability that the line fault is a tree-line fault is relatively large, and if less than three are satisfied, the probability that the line fault is a tree-line fault is relatively small.

[0094] In this embodiment, first, the original discrete signal from when the zero-sequence voltage starts to increase until just before it first steadily decreases is collected. The library function smooth in MATLAB is used to smooth the collected original signal to reduce the original signal noise, and then the effective value of the discrete signal within each power frequency cycle is calculated. The library functions polyfit and polyval in MATLAB are used to perform an n-order fitting on the discrete zero-sequence voltage effective value signal to obtain its continuous function with respect to time. To ensure the fitting accuracy, n is a positive integer greater than 4. The second derivative function of the obtained continuous function of the zero-sequence voltage effective value with respect to time is calculated, and the positive or negative situation of this second derivative function during the intermediate period is judged. The discrete zero-sequence voltage effective value signal is second-order differenced with respect to time to obtain a quadratic approximation of the rate of change between adjacent points of this signal. Using time as a scale, the obtained discrete second-order differenced zero-sequence voltage effective value signal is evenly divided into two segments, and the fluctuation amplitudes of these two segments of differenced signals are compared. The skewness of the calculated discrete second-order differenced zero-sequence voltage effective value signal is calculated. Using time as a scale, the obtained discrete second-order differenced zero-sequence voltage effective value signal is evenly divided into two segments, and the Pearson correlation coefficients of these two segments of differenced signals are respectively calculated and compared. The second derivative function of the continuous function of the zero-sequence voltage effective value with respect to time is always positive during the intermediate period; the fluctuation amplitude of the discrete second-order differenced zero-sequence voltage effective value signal in the first half of the time is less than that in the second half of the time; the absolute value of the skewness of the discrete second-order differenced zero-sequence voltage effective value signal is less than 0.5; the Pearson correlation coefficient of the discrete second-order differenced zero-sequence voltage effective value signal in the first half is greater than 0, and the Pearson correlation coefficient in the second half is greater than that in the first half and greater than 0.3; among the above conditions, if three or more are satisfied, the probability that the line fault is a tree-line fault is relatively large, and if less than three are satisfied, the probability that the line fault is a tree-line fault is relatively small.

[0095] In this embodiment, smoothing the original discrete zero-sequence voltage data can reduce the noise interference of the original signal. Calculating the effective value within each power frequency cycle can highlight the characteristic quantity of the original discrete zero-sequence voltage signal within the power frequency. When a tree-line fault occurs, the zero-sequence voltage has high sensitivity and changes violently, and the fluctuation degree of its second derivative is greater than that of the first derivative and the original data. Therefore, analyzing using the second derivative of the zero-sequence voltage can better highlight the characteristic signal. When faults such as metal grounding, flame burning the wire, and broken wire non-tree grounding occur, the second derivative function of the zero-sequence voltage, as well as the fluctuation amplitude, Pearson correlation coefficient, and skewness of the second-order differenced zero-sequence voltage signal, are all different. Therefore, they can be regarded as the characteristic quantities of the zero-sequence voltage during line faults. Because when a tree-line fault occurs in a distribution system under different ground capacitance currents or residual currents, the change trend of the zero-sequence voltage is the same, and the characteristic quantities are all comparisons between the first half and the second half of the discrete zero-sequence voltage effective value or comparisons with a certain constant, so the magnitudes of the ground capacitance current and residual current of the distribution line do not need to be considered.

[0096] The above-described embodiments merely represent specific embodiments of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention.

Claims

1. A method for detecting the characteristic signal of tree-line fault based on zero-sequence voltage, characterized in that, It includes the following steps: Step 1: Collect the original discrete signal from when the zero-sequence voltage starts to increase until it first steadily decreases, smooth the signal, and calculate the effective value signal of the discrete zero-sequence voltage within each cycle; Step 2: Fit the effective value signal of the discrete zero-sequence voltage to obtain its continuous function with respect to time; Step 3: Take the second derivative of the continuous function and determine the sign of the value in the middle period of the second derivative function; Step 4: Calculate the second-order difference signal of the effective value signal of the discrete zero-sequence voltage; Step 5: Compare the fluctuation amplitudes of the second-order difference signal in the first half of the time and the second half of the time; Step 6: Calculate the skewness of the second-order difference signal; Step 7: Compare the Pearson correlation coefficients of the second-order difference signal in the first half of the time and the second half of the time; Step 8: Comprehensively analyze whether a tree-line fault has occurred; The specific content of Step 8 is as follows: In step 3, the second derivative function is always positive when , where t max is the maximum value of time; in step 5, the amplitude of the fluctuation of the second-order difference signal of the discrete zero-sequence voltage effective value signal in the first half of the time is smaller than that in the second half of the time; in step 6, the absolute value of the skewness of the second-order difference signal of the discrete zero-sequence voltage effective value signal is less than 0.5; in step 7, the Pearson correlation coefficient of the second-order difference signal of the discrete zero-sequence voltage effective value signal in the first half of the time is greater than 0, and the Pearson correlation coefficient in the second half of the time is greater than the Pearson correlation coefficient in the first half of the time and greater than 0.3; among the above conditions, if more than three are satisfied, the probability that the line fault is a tree-line fault is relatively large, otherwise, the probability that the line fault is a tree-line fault is relatively small.

2. The method for detecting the tree-line fault characteristic signal based on zero-sequence voltage according to claim 1, wherein The specific content of Step 1 is as follows: Use the library function smooth in MATLAB to smooth the collected original discrete signal, reduce the noise of the original discrete signal, and then calculate the effective value of the effective value signal of the discrete zero-sequence voltage within each power frequency cycle; The specific content of the smoothing process is as follows: where A y [n] is the nth discrete signal after smoothing, N is the window size of smoothing, k is the window radius of smoothing, and y[i] is the ith discrete signal before smoothing; The specific content of calculating the effective value of the effective value signal of the discrete zero-sequence voltage within each power frequency cycle is as follows: where U n is the effective value of the discrete zero-sequence voltage in the nth power-frequency cycle, M is the number of discrete signals in one power-frequency cycle, and u x is the xth discrete zero-sequence voltage signal in one power-frequency cycle.

3. The method for detecting the tree-line fault characteristic signal based on zero-sequence voltage according to claim 2, wherein The specific content of Step 2 is as follows: Use the library functions polyfit and polyval in MATLAB to perform an nth-order fit on the effective value signal of the discrete zero-sequence voltage in Step 1 to obtain its continuous function with respect to time, where n is a positive integer greater than 4.

4. The method for detecting the tree-line fault characteristic signal based on zero-sequence voltage according to claim 3, wherein The specific content of Step 3 is as follows: Take the second derivative of the continuous function of the effective value signal of the discrete zero-sequence voltage obtained in step 2 with respect to time, and judge the positive and negative conditions of the second derivative at when.

5. The method for detecting the tree-line fault characteristic signal based on the zero-sequence voltage according to claim 4, characterized in that The specific content of Step 4 is as follows: Perform a second-order difference on the time for the effective value signal of the discrete zero-sequence voltage obtained in Step 1 to obtain a quadratic approximation of the rate of change between adjacent points of the signal; specifically, the second-order difference is as follows: In the formula, F(n) is the nth value after the second-order difference, f(n + 2) is the (n + 2)th value of the effective value signal of the discrete zero-sequence voltage, f(n + 1) is the (n + 1)th value of the effective value signal of the discrete zero-sequence voltage, f(n) is the nth value of the effective value signal of the discrete zero-sequence voltage, and T is the power frequency cycle.

6. The method for detecting the tree-line fault characteristic signal based on the zero-sequence voltage according to claim 1, wherein The specific content of Step 5 is as follows: Taking time as the scale, evenly divide the second-order difference signal of the effective value signal of the discrete zero-sequence voltage obtained in Step 4 into two segments, and compare the fluctuation amplitudes of these two segments of difference signals; The specific content of the fluctuation amplitude is as follows: Wherein, S1 is the fluctuation amplitude of the second-order difference signal in the first half of the time, S2 is the fluctuation amplitude of the second-order difference signal in the second half of the time, x i is the i-th second-order difference signal, μ1 is the average amplitude of the second-order difference signal in the first half of the time, μ2 is the average amplitude of the second-order difference signal in the second half of the time, and A is the number of discrete second-order difference signals.

7. The method for detecting the tree-line fault characteristic signal based on zero-sequence voltage according to claim 1, wherein The specific content of Step 6 is as follows: Calculate the skewness of the second-order difference signal of the effective value signal of the discrete zero-sequence voltage obtained in Step 4; the specific content of the skewness is as follows: Where N is the number of discrete second-order difference signals, μ is the mean of the discrete second-order difference signals, σ is the standard deviation of the discrete second-order difference signals, and x i represents the i-th discrete second-order difference signal, and γ is the skewness.

8. The method for detecting the fault characteristic signal of the tree line based on the zero-sequence voltage according to claim 1, wherein The specific content of Step 7 is as follows: Taking time as the scale, evenly divide the second-order difference signal of the effective value signal of the discrete zero-sequence voltage obtained in Step 4 into two segments, and separately calculate and compare the Pearson correlation coefficients of these two segments of difference signals; The specific content of the Pearson correlation coefficient is as follows: Wherein, r1 is the Pearson correlation coefficient of the second-order difference signal in the first half of the time period, r2 is the Pearson correlation coefficient of the second-order difference signal in the second half of the time period, t i is the time at the end of the i-th power frequency cycle, d i is the i-th second-order difference signal, is the mean value of the second-order difference signal in the first half of the time period, is the mean value of the second-order difference signal in the second half of the time period, is the mean value in the first half of the time period, is the mean value in the second half of the time period.

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