Discharge identification method under overhead line

Abstract

The invention discloses a discharge identification method under an overhead line, and relates to the technical field of electrical signal processing, and the method comprises the steps: obtaining a zero-sequence voltage signal of the overhead line before forest fire discharge, and carrying out the low-pass filtering preprocessing of the zero-sequence voltage signal; performing continuous wavelet transform on the preprocessed zero-sequence voltage signal to obtain a time domain wavelet coefficient; performing phase analysis on the wavelet coefficient at each time position, and determining an instantaneous frequency by using the phase change of the wavelet coefficient; performing synchronous extrusion on the wavelet coefficient according to the instantaneous frequency, and redistributing the wavelet coefficient to a corresponding frequency position to obtain a time-frequency diagram of the zero-sequence voltage; extracting a main frequency component, a frequency bandwidth, energy distribution and a frequency spectrum peak position of a low-frequency band from the time-frequency diagram to construct characteristic parameters, and judging the type of a zero-sequence voltage signal so as to determine the cause of a forest fire accident; zero-sequence voltage signals are used as detection signals, so that investigators can judge causes of mountain fire accidents more conveniently.

Description

Technical Field

[0001] The invention relates to the technical field of electrical signal processing, in particular to a discharge identification method under an overhead line. Background Art

[0002] In recent years, there have been many 10kV line tripping accidents caused by forest fires due to social reasons and tree line faults in 10kV overhead lines. How to scientifically, quickly and accurately warn of forest fires, tree line faults and analyze and determine the causes of accidents has important technical value and social and economic benefits.

[0003] Synchronous squeezed wavelet transform (SSWT) is a common technique for signal time-frequency analysis and is widely used in feature extraction and pattern recognition of complex signals. Flame discharge and tree line discharge signals usually have different frequency characteristics and are often interfered by noise. Synchronous squeezed wavelet transform calculates the instantaneous frequency of the signal through continuous wavelet transform (CWT) and redistributes the wavelet coefficients in the frequency domain to generate a high-precision time-frequency graph, from which rich spectral information can be extracted. However, the recognition effect of SSWT depends largely on the selection of the mother wavelet and the accurate calculation of the instantaneous frequency. Morlet wavelet is often used in the signal analysis of flame discharge and tree line discharge due to its good time and frequency localization characteristics.

[0004] In the process of identifying wildfire flame discharge and treeline discharge, the zero-sequence voltage signal contains a large number of time-varying frequency components. Flame discharge signals often have a wide spectrum distribution and large energy fluctuations, while the spectrum distribution of treeline discharge signals is relatively concentrated. Through synchronous squeezing wavelet transform, the energy of the zero-sequence voltage signal can be redistributed on the time-frequency plane, so that the frequency components are concentrated on the corresponding instantaneous frequency, thereby improving the frequency resolution and achieving a clear representation of the signal characteristics. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a discharge identification method under an overhead line. The present invention is used for investigating the cause of a wildfire accident under a 10kV overhead line, and the present invention uses a zero-sequence voltage signal as a detection signal, which is more conducive to investigators determining the cause of the wildfire accident.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a discharge identification method under overhead lines, which is used to investigate the cause of wildfire accidents under overhead lines, wherein the cause of the accident includes wildfire discharge caused by social reasons and wildfire discharge caused by tree line faults, and the method comprises the following steps:

[0007] Step 1, obtaining a zero-sequence voltage signal of an overhead line before a mountain fire discharge, and performing low-pass filtering preprocessing on the zero-sequence voltage signal;

[0008] Step 2: Select Morlet wavelet as the mother wavelet function ψ(t), perform continuous wavelet transform on the preprocessed zero-sequence voltage signal, and obtain the time domain wavelet coefficient W x (a, b);

[0009] Step 3: Perform phase analysis on the wavelet coefficients at each time position b, and use the phase change of the wavelet coefficients to determine the instantaneous frequency ω x (a, b);

[0010] Step 4: synchronously squeeze the wavelet coefficients according to the instantaneous frequency, redistribute the wavelet coefficients to the corresponding frequency positions, and obtain the time-frequency diagram of the zero-sequence voltage;

[0011] Step 5: Extract the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low-frequency band from the time-frequency diagram to construct characteristic parameters, determine the type of zero-sequence voltage signal, and thus determine the cause of the wildfire accident.

[0012] As a further improvement of the present invention, in step 2, the mother wavelet function ψ(t) is specifically as follows:

[0013]

[0014] In the formula, is a normalization factor, exp(jω0t) is a complex exponential term, representing a sine wave with a frequency of ω0. is a Gaussian envelope term, which is used to confine the sine wave to a limited time range to produce a localized effect;

[0015] The continuous wavelet transform is as follows:

[0016]

[0017] Where a is the scale parameter, b is the time position, and is the translation parameter. Used for signal energy normalization, x(t) represents the zero-sequence voltage signal after preprocessing, represents the conjugate function of the mother wavelet function, W x (a, b) represents the energy intensity of the preprocessed zero-sequence voltage signal x(t) at scale parameter a and time position b, i.e., the wavelet coefficient.

[0018] As a further improvement of the present invention, in step 3, the instantaneous frequency is specifically as follows:

[0019]

[0020] In the formula, represents the partial derivative of the wavelet coefficient with respect to the time position b, i represents the imaginary part, Wx (a,b) are wavelet coefficients, ω x (a,b) represents the instantaneous frequency of the signal at scale parameter a and time position b.

[0021] As a further improvement of the present invention, in step 4, synchronous squeezing of the wavelet coefficients according to the instantaneous frequency is specifically as follows:

[0022]

[0023] Where, T x (f,b) represents the time-frequency distribution of the synchronous squeezing wavelet transform, δ(f-ω x (a,b)) is the Dirac function used to redistribute the wavelet coefficients W in the frequency domain x The energy of (a,b), Represents the normalization factor of the scale parameter a.

[0024] As a further improvement of the present invention, the step 5 is specifically as follows:

[0025] According to the main frequency component, frequency bandwidth, energy distribution and spectrum peak position, the corresponding feature vectors of wildfire discharge caused by social reasons and wildfire discharge caused by tree-line faults are constructed; the feature vectors corresponding to each feature parameter are input into the trained support vector machine, and if the output result meets the feature of wildfire discharge caused by social reasons, it is determined that the wildfire is caused by social reasons, and the cause of the accident is not related to the overhead lines; if the output result meets the feature of wildfire discharge caused by tree-line faults, it is determined that the wildfire is caused by tree-line faults, and the cause of the accident is related to the overhead lines.

[0026] As a further improvement of the present invention, in step 5, the main frequency component f min The details are as follows:

[0027]

[0028] Where, T x (f,b) represents the time-frequency of the synchronous squeezed wavelet transform, It means finding the frequency with the highest energy density.

[0029] As a further improvement of the present invention, in step 5, the energy distribution E(f) is obtained by calculating the energy density in different frequency bands:

[0030]

[0031] Where b1 and b2 are the starting and ending points of the interval in time position, and E(f) represents the total energy of the signal at frequency f. By normalizing the energy density of different frequency bands, the relative dispersion of energy distribution is obtained:

[0032]

[0033] In the formula, Represents the average frequency.

[0034] As a further improvement of the present invention, the overhead line is a 10kV overhead line.

[0035] The beneficial effects of the present invention are:

[0036] The present invention distinguishes between the two situations that the frequency of the zero-sequence voltage signal of the 10kV overhead line before the wildfire is caused by social reasons and that before the wildfire is caused by tree-line faults in the low-frequency area below 100Hz. By extracting the characteristic vectors corresponding to the characteristic parameters of the main frequency component, frequency bandwidth, energy distribution and spectrum peak of the zero-sequence voltage signal in the low-frequency band, the signal type is judged based on the trained vector machine, so as to determine the type of wildfire accident, which is convenient for rapid and accurate judgment of the cause of the accident after the wildfire occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Example

[0040] like Figure 1 As shown, a discharge identification method under an overhead line is used to investigate the cause of a wildfire accident under an overhead line, wherein the cause of the accident includes wildfire discharge caused by social reasons and wildfire discharge caused by tree line faults, and the method comprises the following steps:

[0041] Step 1: Obtain the zero-sequence voltage signal of the 10kV overhead line before the wildfire caused by social reasons and tree-line faults, and perform low-pass filtering preprocessing on the zero-sequence voltage signal, as follows:

[0042] The lowpass library function in MATLAB is used for low-pass filtering. After studying, it is found that the frequency of zero-sequence voltage in the low-frequency region below 100Hz when mountain fire flame discharge and tree line discharge occur under 10kV overhead lines has obvious differences. Therefore, the zero-sequence voltage signal outside 100Hz is filtered out to obtain the pre-processed zero-sequence voltage signal.

[0043] Step 2: Select Morlet wavelet as the mother wavelet function ψ(t), perform continuous wavelet transform on the preprocessed zero-sequence voltage signal, and obtain the time domain wavelet coefficient W x (a, b);

[0044] Step 3: Perform phase analysis on the wavelet coefficients at each time position b, and use the phase change of the wavelet coefficients to determine the instantaneous frequency ω x (a, b);

[0045] Step 4: synchronously squeeze the wavelet coefficients according to the instantaneous frequency, redistribute the wavelet coefficients to the corresponding frequency positions, and obtain the time-frequency of the zero-sequence voltage;

[0046] Step 5: extract the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low frequency band from the time-frequency diagram to construct characteristic parameters, judge the type of the zero-sequence voltage signal, and thus determine the cause of the wildfire accident;

[0047] In step 2, the Morlet wavelet is selected as the mother wavelet function ψ(t), and the pre-processed zero-sequence voltage signal is subjected to continuous wavelet transform to obtain the time domain wavelet coefficient W x (a,b), the formula for continuous wavelet transform is:

[0048]

[0049] In the formula, a is the scale parameter, take a>1, b is the translation parameter, Used for signal energy normalization, x(t) represents the zero-sequence voltage signal after preprocessing, represents the conjugate function of the mother wavelet function, W x (a, b) represents the energy intensity of the zero-sequence voltage signal x(t) at scale parameter a and time position b.

[0050] In step 3, the phase analysis is performed on the wavelet coefficients at each time position b, and the instantaneous frequency is determined by using the phase change of the wavelet coefficients. x The formula for (a,b) is:

[0051]

[0052] In the formula, represents the partial derivative of the wavelet coefficient with respect to time b, i represents the imaginary part, W x (a,b) are the wavelet coefficients obtained in step 2, ω x (a,b) represents the instantaneous frequency of the signal at scale parameter a and time position b.

[0053] In step 4, the wavelet coefficients are synchronously squeezed according to the instantaneous frequency, and the wavelet coefficients are redistributed to the corresponding frequency positions to obtain the time-frequency diagram of the zero-sequence voltage. The synchronous squeezing wavelet transform calculation formula is:

[0054]

[0055] Where, T x (f,b) represents the time-frequency distribution of the synchronous squeezing wavelet transform, δ(f-ω x (a,b)) is the Dirac function used to redistribute the wavelet coefficients W in the frequency domain x The energy of (a,b), Represents the normalization factor of scale a.

[0056] In step 5, the feature vectors corresponding to flame discharge and tree-line discharge are constructed according to the main frequency components, frequency bandwidth, energy distribution and spectrum peak position; the feature vectors corresponding to each feature parameter are input into the trained support vector machine, and if the output result meets the flame discharge characteristics, it is determined that the wildfire is caused by social reasons, and the cause of the accident is not related to the 10kV overhead line; if it meets the tree-line discharge characteristics, it is determined that the wildfire is caused by tree-line faults, and the cause of the accident is related to the 10kV overhead line.

[0057] In step 5, the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low frequency band are extracted from the time-frequency graph to construct characteristic parameters. On the time-frequency graph, for each time position b, the main frequency component f min The calculation formula is as follows:

[0058]

[0059] Where, T x (f,b) represents the time-frequency of the synchronous squeezed wavelet transform, It means finding the frequency with the highest energy density.

[0060] In step 5, the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low frequency band are extracted from the time-frequency diagram to construct characteristic parameters. At a given time position b, the energy distribution E(f) can be obtained by calculating the energy density in different frequency bands. The calculation formula of energy distribution E(f) is as follows:

[0061]

[0062] Where b1 and b2 are the starting and ending points of the time interval, and E(f) represents the total energy of the signal at frequency f. By normalizing the energy density of different frequency bands, the relative dispersion of energy distribution can be obtained. The calculation formula of relative dispersion is as follows:

[0063]

[0064] Where E(f) represents the total energy of the signal at frequency f. mean represents the average frequency and is defined as follows:

[0065]

[0066] Where E(f) represents the total energy of the signal at frequency f.

[0067] In this embodiment, the zero-sequence voltage signal of the 10kV overhead line before the wildfire caused by social reasons and the wildfire caused by the tree line fault is first obtained, and the zero-sequence voltage signal is pre-processed by low-pass filtering; Morlet wavelet is selected as the mother wavelet function ψ(t), and the pre-processed zero-sequence voltage signal is subjected to continuous wavelet transform to obtain the time domain wavelet coefficient W x (a, b); perform phase analysis on the wavelet coefficients at each time position b, and use the phase change of the wavelet coefficients to determine the instantaneous frequency; perform synchronous extrusion on the wavelet coefficients according to the instantaneous frequency, and redistribute the wavelet coefficients to the corresponding frequency positions to obtain the time-frequency diagram of the zero-sequence voltage; extract the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low-frequency band from the time-frequency diagram to construct feature parameters, and input the feature vectors corresponding to each feature parameter into the trained support vector machine to determine the type of zero-sequence voltage signal, so as to determine the cause of the wildfire accident. Improve the efficiency and accuracy of determining the cause of the accident after a wildfire occurs.

[0068] This embodiment has the following advantages: in actual situations, the evidence for investigating the cause of a wildfire is basically burned. The zero-sequence voltage signal, as one of the signals collected by power companies for a long time, has the advantages of stability and ease of acquisition. Feature parameters are constructed based on the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the zero-sequence voltage in the low-frequency band. The feature vectors corresponding to each feature parameter are input into a trained support vector machine to determine the type of the zero-sequence voltage signal. This can effectively avoid errors in human subjective judgment and is very convenient for determining the cause of an accident after a wildfire.

[0069] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for identifying discharge under overhead lines, characterized in that: The method is used for investigating the cause of a wildfire accident under an overhead line, wherein the cause of the accident includes wildfire discharge caused by social reasons and wildfire discharge caused by tree line faults, and the method comprises the following steps: Step 1, obtaining a zero-sequence voltage signal of an overhead line before a mountain fire discharge, and performing low-pass filtering preprocessing on the zero-sequence voltage signal; Step 2: Select Morlet wavelet as the mother wavelet function ψ(t), perform continuous wavelet transform on the preprocessed zero-sequence voltage signal, and obtain the time domain wavelet coefficient W x (a, b); Step 3: Perform phase analysis on the wavelet coefficients at each time position b, and use the phase change of the wavelet coefficients to determine the instantaneous frequency ω x (a, b); Step 4: synchronously squeeze the wavelet coefficients according to the instantaneous frequency, redistribute the wavelet coefficients to the corresponding frequency positions, and obtain the time-frequency diagram of the zero-sequence voltage; Step 5: Extract the main frequency component, frequency bandwidth, energy distribution and spectrum peak position of the low-frequency band from the time-frequency diagram to construct characteristic parameters, determine the type of zero-sequence voltage signal, and thus determine the cause of the wildfire accident.

2. The method for identifying discharge under overhead lines according to claim 1, characterized in that: In step 2, the mother wavelet function ψ(t) is specifically as follows: In the formula, is a normalization factor, exp(jω0t) is a complex exponential term, representing a sine wave with a frequency of ω0. is a Gaussian envelope term, which is used to confine the sine wave to a limited time range to produce a localized effect; The continuous wavelet transform is as follows: Where a is the scale parameter, b is the time position, and is the translation parameter. Used for signal energy normalization, x(t) represents the zero-sequence voltage signal after preprocessing, represents the conjugate function of the mother wavelet function, W x (a, b) represents the energy intensity of the preprocessed zero-sequence voltage signal x(t) at scale parameter a and time position b, i.e., the wavelet coefficient.

3. The method for identifying discharge under overhead lines according to claim 2, characterized in that: In step 3, the instantaneous frequency is as follows: In the formula, represents the partial derivative of the wavelet coefficient with respect to the time position b, i represents the imaginary part, W x (a,b) are wavelet coefficients, ω x (a,b) represents the instantaneous frequency of the signal at scale parameter a and time position b.

4. The method for identifying discharge under overhead lines according to claim 1, characterized in that: In step 4, the wavelet coefficients are synchronously squeezed according to the instantaneous frequency as follows: Where, T x (f,b) represents the time-frequency distribution of the synchronous squeezing wavelet transform, δ(f-ω x (a,b)) is the Dirac function used to redistribute the wavelet coefficients W in the frequency domain x The energy of (a,b), Represents the normalization factor of the scale parameter a.

5. The method for identifying discharge under overhead lines according to claim 1, characterized in that: The step 5 is specifically as follows: According to the main frequency component, frequency bandwidth, energy distribution and spectrum peak position, the feature vectors corresponding to the wildfire discharge caused by social reasons and the wildfire discharge caused by tree line faults are constructed; the feature vectors corresponding to each feature parameter are input into the trained support vector machine, and if the output result meets the wildfire discharge characteristics caused by social reasons, it is determined that the wildfire is caused by social reasons, and the cause of the accident is not related to the overhead line; If the output results meet the discharge characteristics of wildfires caused by tree-line faults, it is determined that the wildfire was caused by tree-line faults and the cause of the accident is related to overhead lines.

6. The method for identifying discharge under overhead lines according to claim 5, characterized in that: In step 5, the main frequency component f min The details are as follows: Where, T x (f,b) represents the time-frequency of the synchronous squeezed wavelet transform, It means finding the frequency with the highest energy density.

7. The method for identifying discharge under overhead lines according to claim 6, characterized in that: In step 5, the energy distribution E(f) is obtained by calculating the energy density in different frequency bands: Where b1 and b2 are the starting and ending points of the interval in time position, and E(f) represents the total energy of the signal at frequency f. By normalizing the energy density of different frequency bands, the relative dispersion of energy distribution is obtained: In the formula, Represents the average frequency.

8. The method for identifying discharge under overhead lines according to any one of claims 1 to 7, characterized in that: The overhead line is a 10kV overhead line.

Images