Typical traveling wave characteristic parameter extraction method based on time-frequency analysis

Abstract

The invention discloses a typical traveling wave characteristic parameter extraction method based on time-frequency analysis, and the method comprises the following steps: collecting a traveling wave signal, obtaining the position information of a collection point and the position information of a fault point of the traveling wave signal, and taking the traveling wave signal as a first signal; performing interval sampling on the first signal based on a time domain sequence to generate a second signal, and selecting the second signal based on a frequency domain interval to generate a third signal; carrying out discrete sampling on the third signal and then recombining the third signal to form a fourth signal; and performing feature extraction on the fourth signal to obtain a waveform feature, and obtaining an association relationship between the waveform feature and the acquisition point position information and the fault point position information. According to the method, the waveform features of the traveling wave signals can be extracted under the condition that the sample size is small, the capacity of recognizing the traveling wave signals is improved, and the traveling wave signals and the fault types are recognized by calculating the correlation degree in the sampling information and recognizing the traveling wave signals according to the correlation degree interval where the correlation integral is located.

Description

Technical Field

[0001] The invention belongs to the technical field of power equipment fault analysis, and in particular relates to a method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis. Background Art

[0002] At present, the traveling wave fault location method has a high positioning accuracy in theory, which makes it widely studied and applied. The moment and position of the fault traveling wave signal mutation point represent the specific fault information. Therefore, accurately detecting the mutation point of the fault traveling wave signal becomes the key to traveling wave fault location. Based on wavelet analysis, Hilbert Huang transform and other analysis methods, it has been widely used in traveling wave identification and has achieved good fault location effect. However, the attenuation of the traveling wave transmission process weakens the singularity of the traveling wave; the identification of lightning interference when the line is not faulty, and the precise positioning of the flashover point when the lightning point and the flashover point are inconsistent in the lightning fault require the identification and detection of the second traveling wave head (the reflected wave of the fault point) on the lightning side; in the single-end traveling wave fault location method, the reflected wave of the fault point and the reflected wave of the opposite busbar are accurately identified; because the useful signal cannot be effectively extracted due to the interference of noise during the transmission and sampling of the traveling wave signal, the existing traveling wave identification method based on the extraction of time domain or time-frequency domain feature quantity is difficult to guarantee its reliability and accuracy. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a typical traveling wave characteristic parameter extraction method based on time-frequency analysis which can improve the recognition ability of traveling wave signals when the sample size is small and can ensure reliability and accuracy.

[0004] The technical solution of the present invention is:

[0005] A typical traveling wave characteristic parameter extraction method based on time-frequency analysis comprises the following steps:

[0006] Step S1: collecting a traveling wave signal, obtaining the location information of a collection point and a fault point of the traveling wave signal, and using the traveling wave signal as a first signal;

[0007] Step S2: performing interval sampling on the first signal based on a time domain sequence to generate a second signal, and selecting the second signal based on a frequency domain interval to generate a third signal;

[0008] Step S3: discretely sampling the third signal and recombining the sample to form a fourth signal;

[0009] Step S4, extracting features from the fourth signal to obtain waveform features, and obtaining correlations between the waveform features and the acquisition point location information and the fault point location information.

[0010] Furthermore, the step S1 includes the following steps:

[0011] Step S11: setting a collection point of a traveling wave signal and a fault simulation point on the transmission line, and setting a plurality of collection points of the traveling wave signal, and recording the location information of the collection point of the traveling wave signal at each point;

[0012] Step S12: simulating various types of power transmission line faults, and recording the fault simulation points as fault point location information;

[0013] Step S13: After the collection point of the fault traveling wave signal detects the traveling wave signal, the traveling wave signal is used as the first signal.

[0014] Furthermore, the calculation formula of the fault point position is:

[0015]

[0016] L f =L 1 +d f or L f =L 2 -d f ,

[0017] Among them, d f Indicates the distance between the fault point and the acquisition point, T 1f Indicates the time when the fault traveling wave signal arrives at acquisition point 1, T 2f represents the time when the fault traveling wave signal reaches the acquisition point 2, v represents the propagation speed of the traveling wave, L 1 Indicates the location of collection point 1, L 2 Indicates the location of collection point 2, L f Indicates the location of the fault point.

[0018] Furthermore, the step S2 comprises the following steps:

[0019] Step S21: performing interval sampling on the first signal based on a time domain sequence to generate a second signal;

[0020] Step S22: performing interval sampling on the second signal based on the frequency domain interval to obtain sampling information;

[0021] Step S23: splicing the sampling information obtained by the interval sampling in time sequence to obtain a third signal;

[0022] Step S24: by changing the distance between the frequency intervals, different frequency intervals are sampled at intervals, sampling information of the different frequency intervals is acquired, and a plurality of third signals are obtained.

[0023] Furthermore, the expression for generating the second signal by performing interval sampling on the first signal based on the time domain sequence is:

[0024]

[0025] Among them, r i (t) represents the signal received in the i-th time interval, i.e., the second signal, A represents the amplitude of the second signal, and f 0 represents the center frequency of the second signal, θ 0 represents the initial phase of the second signal, j represents the imaginary unit, w(t) represents the noise part of the second signal, i represents the index of the time segment, that is, the current signal is located in the i-th time interval, t represents the time variable, T 0 Indicates the length of the time segment, that is, the duration of each interval;

[0026] The expression for obtaining sampling information by performing interval sampling on the second signal based on the frequency domain interval is:

[0027]

[0028] Wherein, X(k) represents the sampling information, that is, the sampling value of a finite number of spectra in the second signal, and x(n) represents the nth discrete sampling value of the original signal. represents the sampling interval, N represents the total number of sampling points of the signal, k represents the frequency index, n represents the time index, and i represents the imaginary unit;

[0029] The expression of the third signal is:

[0030]

[0031] x k，m (n) = x(t k，m +nT k，m ), n=0,1,2,...,N k，m -1,

[0032] x third,k (n), k = 1, 2, ..., K,

[0033] Among them, x(t) represents the original signal, x k,m (n) represents the sampled signal obtained by sampling from the kth frequency interval and the mth time interval, t k,m Indicates the starting time point of sampling, T k,m represents the sampling interval, k represents the frequency interval index, m represents the segment number when the kth frequency interval is sampled, and N k,m Indicates the length of the mth sampling signal in the kth frequency interval, x third,k(n) represents the third signal, K represents the total number of frequency intervals, N k,j It represents the number of points of the jth sampling signal in the kth frequency interval, and n represents the sampling point index of the discrete signal.

[0034] Further, the step S3 is specifically: discretely sample each of the third signals and then recombine them, splice them together to form a fourth signal, and obtain a fourth signal group; the expression of the fourth signal is:

[0035]

[0036] Among them, r i (n) represents the received signal at the nth sampling point in the i-th time period, i.e., the fourth signal, A represents the amplitude of the fourth signal, and f 0 represents the center frequency of the fourth signal, Δt represents the sampling interval, represents the initial phase of the fourth signal, w(n) represents the noise part of the fourth signal, i represents the index of the time segment, N 0 Represents the number of sampling points in each time segment, and n represents the number of signal sampling points.

[0037] Furthermore, the step S4 comprises the following steps:

[0038] Step S41: Establishing a correlation vector metric model;

[0039] Step S42: Calculate the distance between each acquisition point location information and the fault point location information in the sampling information, give an observation threshold, and when the relative distance is less than the observation threshold, it is called an associated vector pair, and then count the associated vector pairs to obtain the relative statistical distance;

[0040] Step S43: According to the actual distance between the acquisition point location information and the fault point location information, the waveform feature is matched with the actual distance between the acquisition point location information and the fault point location information, and the correlation interval is used as a correlation relationship. According to the correlation relationship, the traveling wave signal is identified and the fault point position is located.

[0041] Furthermore, the expression of the association vector metric model is:

[0042]

[0043] Where C(q, r) represents the correlation vector metric, q represents the singular measure, r represents the observation system of the correlation integral or the delay or observation threshold of the time series, N represents the total number of samples, x represents the global signal eigenvalue, a represents the offset, n represents the amplification factor, and x represents the i represents the value of the i-th sample point in the sample set, x jrepresents the value of the jth sample point in the sample set, and H(x) represents the step function.

[0044] Furthermore, the relative distance in step S42 is the distance between the acquisition point and the fault point, and the expression of the relative distance is:

[0045] D ij =|L i -L f |,

[0046] Among them, D ij Indicates the distance between the acquisition point i and the fault point j, L i represents the location information of the collection point i, L f Indicates the location information of the fault point f;

[0047] When j When <r, the acquisition point i and the fault point f form an associated vector pair, and the counting formula is:

[0048]

[0049] Among them, Npair represents the number of association vector pairs, r represents the observation threshold, and δ represents the decision function;

[0050] The calculation formula of the observation threshold r is:

[0051] C(q,r)=r D ,

[0052] Where D represents the multifractal dimension and C(q, r) represents the correlation vector metric.

[0053] Furthermore, the expression of the association relationship in step S43 is:

[0054] R(L i ,L f )=f(D ij ,φ),

[0055]

[0056] Among them, R(L i ,L f ) represents the acquisition point Li and the fault point L f The relationship between Di j represents the distance between the acquisition point and the fault point, φ represents the waveform characteristics, α and β represent the adjustment coefficients, v represents the traveling wave propagation speed, T 1f Indicates the time when the fault traveling wave signal arrives at acquisition point 1, T 2f Indicates the time when the fault traveling wave signal arrives at the acquisition point 2, L i Indicates the location of the known collection point, Lf Indicates the location of the fault point.

[0057] Beneficial effects of the present invention:

[0058] 1. High-precision fault location: By combining time-frequency analysis with the correlation vector metric model, the present invention can more accurately determine the location of the fault point, especially in complex environments (such as multipath propagation, noise interference). The use of the traveling wave propagation time difference formula combined with the correlation relationship can effectively reduce the measurement error;

[0059] 2. Full-process feature extraction and analysis: From time domain signals to frequency domain sampling, and then to the construction and analysis of correlation relationships, the steps are clear and progressive, ensuring that the key features of the traveling wave signal can be fully extracted. Multi-level signal processing (first signal, second signal, third signal, fourth signal) can gradually filter out noise and highlight key features, providing a reliable data basis for subsequent positioning;

[0060] 3. Multi-scale correlation characteristics analysis: The correlation vector metric model, combined with the flexible adjustment of the observation threshold r and the singular measure q, can analyze the multi-scale correlation characteristics between the fault point and the acquisition point. By adjusting the parameters r and q, it can take into account both local correlation and global distribution characteristics to adapt to different application scenarios;

[0061] 4. Effective anti-interference capability: The present invention adopts time-frequency analysis and interval sampling technology to effectively isolate the influence of noise interference on fault location. It uses the decision function and observation threshold to only count the vector pairs with high correlation, which further improves the anti-noise performance.

[0062] 5. Comprehensive utilization of multi-band information: Through the interval sampling and splicing technology of the frequency domain interval, the comprehensive information of different time periods and frequency intervals is obtained, which helps to fully capture the fault characteristics. The splicing of multiple third signals can further enhance the ability to describe complex fault characteristics;

[0063] 6. Adaptive optimization of observation threshold: The observation threshold is automatically calculated by the correlation vector metric and the multifractal dimension, and can be adaptively adjusted to suit different system sizes and complexities;

[0064] 7. Wide application scope: The present invention is applicable to various transmission lines (such as overhead lines, cable lines) and different types of faults (such as single-phase grounding, phase-to-phase short circuit, line break, etc.), and also has good performance in complex power networks (such as multi-branch networks);

[0065] 8. Better fault diagnosis capability: The present invention can better distinguish different fault types and locations through correlation analysis between waveform features and actual distances, which helps to improve the accuracy and robustness of fault diagnosis. Feature extraction can identify significant patterns related to faults and avoid interference from excessive irrelevant data.

[0066] 9. Multi-frequency interval characteristic capture: Frequency domain interval sampling and multi-frequency interval signal splicing can capture the characteristics of traveling wave signals in different frequency bands, making fault location more comprehensive and accurate, especially suitable for complex transmission lines with significant multi-frequency characteristics;

[0067] In summary, the present invention integrates technologies such as time-frequency analysis, interval sampling, multi-band processing and correlation vector analysis. Through complete process design and flexible parameter adjustment, it has significant advantages in accuracy, robustness, real-time and applicability. It is suitable for the rapid positioning and feature extraction of various transmission line faults in power systems, and provides important technical support for improving the operating stability and safety of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the process steps of a typical traveling wave characteristic parameter extraction method based on time-frequency analysis of the present invention. DETAILED DESCRIPTION

[0069] like Figure 1 As shown, a typical traveling wave characteristic parameter extraction method based on time-frequency analysis includes the following steps:

[0070] Step S1: collecting a traveling wave signal, obtaining the location information of a collection point and a fault point of the traveling wave signal, and using the traveling wave signal as a first signal;

[0071] Step S2: performing interval sampling on the first signal based on a time domain sequence to generate a second signal, and selecting the second signal based on a frequency domain interval to generate a third signal;

[0072] Step S3: discretely sampling the third signal and recombining the sample to form a fourth signal;

[0073] Step S4, extracting features from the fourth signal to obtain waveform features, and obtaining correlations between the waveform features and the acquisition point location information and the fault point location information.

[0074] Preferably, step S1 comprises the following steps:

[0075] Step S11: setting a collection point of a traveling wave signal and a fault simulation point on the transmission line, and setting a plurality of collection points of the traveling wave signal, and recording the location information of the collection point of the traveling wave signal at each point;

[0076] Step S12: simulating various types of power transmission line faults, and recording the fault simulation points as fault point location information;

[0077] Step S13: After the collection point of the fault traveling wave signal detects the traveling wave signal, the traveling wave signal is used as the first signal.

[0078] More preferably, the calculation formula for the fault point location is:

[0079]

[0080] L f =L 1 +d f or L f =L 2 -d f ,

[0081] Among them, d f Indicates the distance between the fault point and the acquisition point, T 1f Indicates the time when the fault traveling wave signal arrives at acquisition point 1, T 2f represents the time when the fault traveling wave signal reaches the acquisition point 2, v represents the propagation speed of the traveling wave, L 1 Indicates the location of collection point 1, L 2 Indicates the location of collection point 2, L f Indicates the location of the fault point.

[0082] Preferably, step S2 comprises the following steps:

[0083] Step S21: performing interval sampling on the first signal based on a time domain sequence to generate a second signal;

[0084] Step S22: performing interval sampling on the second signal based on the frequency domain interval to obtain sampling information;

[0085] Step S23: splicing the sampling information obtained by the interval sampling in time sequence to obtain a third signal;

[0086] Step S24: by changing the distance between the frequency intervals, different frequency intervals are sampled at intervals, sampling information of the different frequency intervals is acquired, and a plurality of third signals are obtained.

[0087] More preferably, the expression for generating the second signal by performing interval sampling on the first signal based on the time domain sequence is:

[0088]

[0089] Where ri(t) represents the signal received in the i-th time interval, that is, the second signal, A represents the amplitude of the second signal, and f 0 represents the center frequency of the second signal, θ 0represents the initial phase of the second signal, j represents the imaginary unit, w(t) represents the noise part of the second signal, i represents the index of the time segment, that is, the current signal is located in the i-th time interval, t represents the time variable, T 0 Indicates the length of the time segment, that is, the duration of each interval;

[0090] The expression for obtaining sampling information by performing interval sampling on the second signal based on the frequency domain interval is:

[0091]

[0092] Wherein, X(k) represents the sampling information, that is, the sampling value of a finite number of spectra in the second signal, and x(n) represents the nth discrete sampling value of the original signal. represents the sampling interval, N represents the total number of sampling points of the signal, k represents the frequency index, n represents the time index, and i represents the imaginary unit;

[0093] The expression of the third signal is:

[0094]

[0095] x k，m (n) = x(t k，m +nT k，m ), n=0,1,2,...,N k，m-1 ,

[0096] x third,k (n), k = 1, 2, ..., K,

[0097] Among them, x(t) represents the original signal, x k,m (n) represents the sampled signal obtained by sampling from the kth frequency interval and the mth time interval, t k,m Indicates the starting time point of sampling, T k,m represents the sampling interval, k represents the frequency interval index, m represents the segment number when the kth frequency interval is sampled, and N k,m Indicates the length of the mth sampling signal in the kth frequency interval, x third,k (n) represents the third signal, K represents the total number of frequency intervals, N k,j It represents the number of points of the jth sampling signal in the kth frequency interval, and n represents the sampling point index of the discrete signal.

[0098] Preferably, the step S3 specifically comprises: recombining each of the third signals after discrete sampling, splicing them together to form a fourth signal, and obtaining a fourth signal group; the expression of the fourth signal is:

[0099]

[0100] Among them, r i (n) represents the received signal at the nth sampling point in the i-th time period, i.e., the fourth signal, A represents the amplitude of the fourth signal, and f 0 represents the center frequency of the fourth signal, Δt represents the sampling interval, represents the initial phase of the fourth signal, w(n) represents the noise part of the fourth signal, i represents the index of the time segment, N 0 Represents the number of sampling points in each time segment, and n represents the number of signal sampling points.

[0101] Preferably, step S4 comprises the following steps:

[0102] Step S41: Establishing a correlation vector metric model;

[0103] Step S42: Calculate the distance between each acquisition point location information and the fault point location information in the sampling information, give an observation threshold, and when the relative distance is less than the observation threshold, it is called an associated vector pair, and then count the associated vector pairs to obtain the relative statistical distance;

[0104] Step S43: According to the actual distance between the acquisition point location information and the fault point location information, the waveform feature is matched with the actual distance between the acquisition point location information and the fault point location information, and the correlation interval is used as a correlation relationship. According to the correlation relationship, the traveling wave signal is identified and the fault point position is located.

[0105] More preferably, the expression of the association vector metric model is:

[0106]

[0107] Where C(q, r) represents the correlation vector metric, q represents the singular measure, r represents the observation system of the correlation integral or the delay or observation threshold of the time series, N represents the total number of samples, x represents the global signal eigenvalue, a represents the offset, n represents the amplification factor, and x represents the i represents the value of the i-th sample point in the sample set, x j represents the value of the jth sample point in the sample set, and H(x) represents the step function.

[0108] More preferably, the relative distance in step S42 is the distance between the acquisition point and the fault point, and the expression of the relative distance is:

[0109] D ij =|L i -L f |,

[0110] Among them, D ijIndicates the distance between the acquisition point i and the fault point j, L i represents the location information of the collection point i, L f Indicates the location information of the fault point f;

[0111] When j When <r, the acquisition point i and the fault point f form an associated vector pair, and the counting formula is:

[0112]

[0113] Among them, Npair represents the number of association vector pairs, r represents the observation threshold, and δ represents the decision function;

[0114] The calculation formula of the observation threshold r is:

[0115] C(q, r) = r D ,

[0116] Where D represents the multifractal dimension and C(q, r) represents the correlation vector metric.

[0117] More preferably, the expression of the association relationship in step S43 is:

[0118] R(L i ,L f )=f(D ij ,φ),

[0119]

[0120] Among them, R(L i ,L f ) represents the collection point L i and fault point L f The relationship between ij represents the distance between the acquisition point and the fault point, φ represents the waveform characteristics, α and β represent the adjustment coefficients, v represents the traveling wave propagation speed, T 1f Indicates the time when the fault traveling wave signal arrives at acquisition point 1, T 2f Indicates the time when the fault traveling wave signal arrives at the acquisition point 2, L i Indicates the location of the known collection point, L f Indicates the location of the fault point.

[0121] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A typical traveling wave characteristic parameter extraction method based on time-frequency analysis, characterized in that: The following steps are involved: Step S1: collecting a traveling wave signal, obtaining the location information of a collection point and a fault point of the traveling wave signal, and using the traveling wave signal as a first signal; Step S2: performing interval sampling on the first signal based on a time domain sequence to generate a second signal, and selecting the second signal based on a frequency domain interval to generate a third signal; Step S3: discretely sampling the third signal and recombining the sample to form a fourth signal; Step S4, extracting features from the fourth signal to obtain waveform features, and obtaining correlations between the waveform features and the acquisition point location information and the fault point location information.

2. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 1, characterized in that: The step S1 comprises the following steps: Step S11: setting a collection point of a traveling wave signal and a fault simulation point on the transmission line, and setting a plurality of collection points of the traveling wave signal, and recording the location information of the collection point of the traveling wave signal at each point; Step S12: simulating various types of power transmission line faults, and recording the fault simulation points as fault point location information; Step S13: After the collection point of the fault traveling wave signal detects the traveling wave signal, the traveling wave signal is used as the first signal.

3. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 2, characterized in that: The calculation formula of the fault point position is: L f = L1 + d f or L f = L2 - d f , Among them, d f represents the distance between the fault point and the acquisition point, T1f represents the time when the fault traveling wave signal reaches acquisition point 1, T2f represents the time when the fault traveling wave signal reaches acquisition point 2, v represents the traveling wave propagation speed, L1 represents the position of acquisition point 1, L2 represents the position of acquisition point 2, L f Indicates the location of the fault point.

4. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 1, characterized in that: The step S2 comprises the following steps: Step S21: performing interval sampling on the first signal based on a time domain sequence to generate a second signal; Step S22: performing interval sampling on the second signal based on the frequency domain interval to obtain sampling information; Step S23: splicing the sampling information obtained by the interval sampling in time sequence to obtain a third signal; Step S24: by changing the distance between the frequency intervals, different frequency intervals are sampled at intervals, sampling information of the different frequency intervals is acquired, and a plurality of third signals are obtained.

5. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 4, characterized in that: The expression for generating the second signal by performing interval sampling on the first signal based on the time domain sequence is: Among them, r i (t) represents the signal received in the i-th time interval, that is, the second signal, A represents the amplitude of the second signal, f0 represents the center frequency of the second signal, θ0 represents the initial phase of the second signal, j represents the imaginary unit, w(t) represents the noise part of the second signal, i represents the index of the time segment, that is, the current signal is located in the i-th time interval, t represents the time variable, T0 represents the length of the time segment, that is, the duration of each interval; The expression for obtaining sampling information by performing interval sampling on the second signal based on the frequency domain interval is: Wherein, X(k) represents the sampling information, that is, the sampling value of a finite frequency spectrum in the second signal, and x(n) represents the nth discrete sampling value of the original signal. represents the sampling interval, N represents the total number of sampling points of the signal, k represents the frequency index, n represents the time index, and i represents the imaginary unit; The expression of the third signal is: x k，m (n)=x(t k，m +nT k，m ),n=0,1,2,…,N k，m -1,x third，k (n),k=1,2,...,K, Among them, x(t) represents the original signal, x k，m (n) represents the sampled signal obtained by sampling from the kth frequency interval and the mth time interval, t k，m Indicates the starting time point of sampling, T k，m represents the sampling interval, k represents the frequency interval index, m represents the segment number when the kth frequency interval is sampled, and N k，m Indicates the length of the mth sampling signal in the kth frequency interval, x third，k (n) represents the third signal, K represents the total number of frequency intervals, N k，j It represents the number of points of the jth sampling signal in the kth frequency interval, and n represents the sampling point index of the discrete signal.

6. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 1, characterized in that: The step S3 specifically includes: discretely sampling each of the third signals and then recombining them, respectively splicing them together to form a fourth signal, and obtaining a fourth signal group; the expression of the fourth signal is: Among them, r i (n) represents the received signal at the nth sampling point in the i-th time period, that is, the fourth signal, A represents the amplitude of the fourth signal, f0 represents the center frequency of the fourth signal, Δt represents the sampling interval time, represents the initial phase of the fourth signal, w(n) represents the noise part of the fourth signal, i represents the index of the time segment, N0 represents the number of sampling points in each time segment, and n represents the number of the signal sampling point.

7. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 1, characterized in that: The step S4 comprises the following steps: Step S41: Establishing a correlation vector metric model; Step S42: Calculate the distance between each acquisition point location information and the fault point location information in the sampling information, give an observation threshold, and when the relative distance is less than the observation threshold, it is called an associated vector pair, and then count the associated vector pairs to obtain the relative statistical distance; Step S43: According to the actual distance between the acquisition point location information and the fault point location information, the waveform feature is matched with the actual distance between the acquisition point location information and the fault point location information, and the correlation interval is used as a correlation relationship. According to the correlation relationship, the traveling wave signal is identified and the fault point position is located.

8. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 7, characterized in that: The expression of the association vector metric model is: Where C(q, r) represents the correlation vector metric, q represents the singular measure, r represents the observation system of the correlation integral or the delay or observation threshold of the time series, N represents the total number of samples, x represents the global signal eigenvalue, a represents the offset, n represents the amplification factor, and x represents the i represents the value of the i-th sample point in the sample set, x j represents the value of the jth sample point in the sample set, and H(x) represents the step function.

9. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 7, characterized in that: The relative distance in step S42 is the distance between the acquisition point and the fault point. The relative distance is expressed as: D ij =|L i -L f |, Among them, D ij Indicates the distance between the acquisition point i and the fault point j, L i represents the location information of the collection point i, L f Indicates the location information of the fault point f; When D ij When <r, the acquisition point i and the fault point f form an associated vector pair, and the counting formula is: Among them, N pair represents the number of association vector pairs, r represents the observation threshold, and δ represents the decision function; The calculation formula of the observation threshold r is: C(q,r)=r D , Where D represents the multifractal dimension and C(q, r) represents the correlation vector metric.

10. The method for extracting characteristic parameters of typical traveling waves based on time-frequency analysis according to claim 7, characterized in that: The expression of the association relationship in step S43 is: R(L i ,L f )=f(D ij ,φ), Among them, R(L i , L f ) represents the collection point L i and fault point L f The relationship between ij represents the distance between the acquisition point and the fault point, φ represents the waveform characteristics, α and β represent the adjustment coefficients, v represents the traveling wave propagation speed, T 1f Indicates the time when the fault traveling wave signal arrives at acquisition point 1, T 2f Indicates the time when the fault traveling wave signal arrives at the acquisition point 2, L i Indicates the location of the known collection point, L f Indicates the location of the fault point.

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