Power transmission line fault fusion distance measurement method and device

Abstract

The invention relates to the technical field of fault detection and positioning, in particular to a power transmission line fault fusion distance measurement method and device, and the method comprises the steps: collecting a current sequence composed of current traveling wave signals at the moment when a fault occurs on a power transmission line; clustering local elements in the current sequence to obtain fault subsequences; calculating a trend fluctuation index of each fault subsequence; obtaining a peak drop difference and a peak interval difference; calculating a periodic confusion coefficient of the current sequence; the main component proportion and the difference index of the singular value sequence of the current sequence are obtained; obtaining a signal purity index of the current sequence based on the correlation between each row of data in the Hankel matrix, the periodic chaos coefficient, the main component proportion and the difference index; and carrying out distance measurement on power transmission line faults based on the signal purity index. According to the invention, the precision of fault distance measurement is improved.

Description

Technical Field

[0001] This application relates to the technical field of fault detection and location, and particularly to a method and device for fusing and ranging transmission line faults. Background Art

[0002] Due to the wide span of transmission lines and the complex and diverse terrains they cross, the transmission environment is relatively complex. Transmission lines are affected by various factors, increasing the probability of faults. To ensure the stability of the power system and provide good power quality, it is very necessary to promptly perform fault ranging and troubleshooting on the fault points of transmission lines after a fault occurs to avoid greater impacts. Fault fusion refers to a technology that locates faults by analyzing the fault information of the line after a fault occurs on the transmission line.

[0003] The traveling wave ranging method is based on the traveling waves generated when a fault occurs on the transmission line to locate the fault point. It is applicable to various types of transmission lines, and the traveling waves are almost immediately generated after a fault occurs. Therefore, the fault can be quickly located, which is beneficial to quickly restoring power supply. After a fault occurs on the transmission line, a transient fault voltage will be generated at the fault location, and singularity points will appear in the current traveling wave signal. Since there is a large amount of noise in the traveling wave signal, only by accurately identifying the singularity points of the fault signal can the location of the fault point be accurately obtained. Existing technologies usually use wavelet denoising to denoise the traveling wave signal and extract the information of the wave head based on the traveling wave ranging method to achieve fault ranging. However, wavelet denoising usually requires setting a threshold to distinguish the noise information and useful information in the signal, with insufficient adaptability. Moreover, the collected traveling wave signals are easily affected by various external environments of the transmission line, resulting in inconsistent degrees of noise interference in different traveling wave signals. Therefore, the denoising effect of wavelet denoising is not good, thus affecting the accuracy of fault ranging. Summary of the Invention

[0004] To solve the above technical problems, the purpose of this application is to provide a method and device for fusing and ranging transmission line faults, and the specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides a method for fusing and ranging transmission line faults, and the method includes the following steps: Collect the current sequence composed of the current traveling wave signals at the moment when a fault occurs on the transmission line; Divide local elements according to the distribution of wave valleys in the current sequence, and cluster the local elements in the current sequence according to the distribution of local elements to obtain each fault subsequence; Based on the fluctuation of the local element change trend and the difference between local elements in the fault subsequence, obtain the trend fluctuation index of each fault subsequence; Obtain the peak drop difference and peak interval difference based on the difference in the change trend of the peaks of the fault subsequences and the difference in the change trend of the ordinal positions of the peaks in the fault subsequences; Obtain the periodic chaos coefficient of the current sequence based on the peak drop difference, peak interval difference, average of the trend fluctuation indices of all fault subsequences, and average of the similarity between fault subsequences; Construct a Hankel matrix of the current sequence, perform singular value decomposition on the Hankel matrix to obtain each singular value, and obtain the proportion of the main components and the difference index of the singular value sequence of the current sequence based on the distribution, average, and difference of the singular values; Obtain the signal purity index of the current sequence based on the correlation between the data of each row in the Hankel matrix, the periodic chaos coefficient, the proportion of the main components, and the difference index; perform fault ranging on the transmission line based on the signal purity index.

[0005] Furthermore, the method for obtaining the fault subsequences is as follows: Use the peak-valley detection algorithm to obtain all the peaks and valleys in the current sequence, divide the current sequence into each subsequence from each valley, calculate the range of the elements in each subsequence, use the ranges of all subsequences as the input of the clustering algorithm, and output each clustering cluster; Calculate the mean value of the internal elements of each clustering cluster, take the clustering cluster with the largest mean value of the internal elements as the fault clustering cluster, and take the current subsequence corresponding to the internal elements of the fault clustering cluster as the fault subsequence.

[0006] Furthermore, the method for obtaining the trend fluctuation index is as follows: Obtain the oscillation coefficients on both sides and the change coefficients on both sides of each fault subsequence based on the fluctuation of the local element change trend and the difference between local elements in the fault subsequence; Combine the oscillation coefficients on both sides and the change coefficients on both sides of each fault subsequence to obtain the trend fluctuation index of each fault subsequence. The formula is: ; where is the trend fluctuation index of the i-th fault subsequence; is the oscillation coefficient on both sides of the i-th fault subsequence; is the change coefficient on both sides of the i-th fault subsequence.

[0007] Furthermore, the method for obtaining the change coefficient on both sides is as follows: For each fault subsequence, obtain the maximum value in each fault subsequence, use the subsequence composed of the maximum value and all the elements on the left side of the maximum value as the left subsequence of each fault subsequence, and use the subsequence composed of all the elements on the right side of the maximum value as the right subsequence of each fault subsequence; Obtain the first-order difference sequence of the left subsequence of each fault subsequence, and use the step function to process all elements in the first-order difference sequence to obtain the difference step sequence; Calculate the absolute value of the difference between the absolute value of the sum of the elements in the difference step sequence and the length of the first-order difference sequence of the left subsequence, and use it as the oscillation index of the left subsequence of each fault subsequence; Adopt the same method as the left subsequence to obtain the oscillation index of the right subsequence of each fault subsequence; calculate the product of the absolute value of the difference and the mean value between the oscillation indexes of the left subsequence and the right subsequence as the two-side oscillation coefficient of each fault subsequence; Calculate the variance of the elements in the left subsequence and the variance of the elements in the right subsequence of each fault subsequence, and use the mean value of the variances of the left subsequence and the right subsequence as the two-side change coefficient of each fault subsequence.

[0008] Furthermore, the method for obtaining the peak interval difference is as follows: Sort all the peaks in all fault subsequences according to their sequence positions in the current sequence to construct a peak sequence; sort the sequence numbers of all peaks in the fault subsequence according to their magnitudes to construct a peak sequence number sequence; Obtain the first-order difference sequences of the peak sequence and the peak sequence number sequence, and use the variance of all elements in the first-order difference sequence of the peak sequence as the peak decline difference; use the variance of all elements in the first-order difference sequence of the peak sequence number sequence as the peak interval difference.

[0009] Furthermore, the method for obtaining the period chaos coefficient is as follows: Calculate the mean value of the trend fluctuation indexes of all fault subsequences as the average fluctuation index; Obtain the mean value of the Pearson similarity coefficients between each fault subsequence and all other fault subsequences as the sequence similarity of each fault subsequence; The formula for the period chaos coefficient is: ; where; D is the period chaos coefficient of the current sequence; are the peak decline difference and the peak interval difference; is the average fluctuation index; P is the mean value of the sequence similarities of all fault subsequences; is the preset tuning parameter coefficient.

[0010] Furthermore, the method for obtaining the main component ratio and the difference index is as follows: Sort all singular values in descending order to construct a singular value sequence; Use the singular value sequence as the input of Otsu's threshold method. The output of Otsu's threshold method is the threshold T. Consider all singular values greater than or equal to the threshold T as important singular values, and all singular values less than the threshold T as minor singular values. Take the ratio of the number of important singular values to the total number of all singular values as the proportion of the main component of the singular value sequence. Calculate the mean and variance of all important singular values in the singular value sequence, and take the product of the mean and variance as the fluctuation index of the important singular values. Use the same method as the fluctuation index of the important singular values to calculate the fluctuation index of the minor singular values, and take the absolute value of the difference between the fluctuation index of the important singular values and the fluctuation index of the minor singular values as the difference index of the singular value sequence.

[0011] Furthermore, the method for obtaining the signal purity index is as follows: Calculate the absolute value of the Pearson similarity coefficient between each row of data in the Hankel matrix and the data of other rows, and take the mean of all the absolute values of the Pearson similarity coefficients of each row of data as the similarity coefficient of each row of data. Take the mean of the similarity coefficients of all rows of data in the Hankel matrix as the self-similarity coefficient of the Hankel matrix. The formula for the signal purity index is: ; where H is the signal purity index of the current sequence; G is the proportion of the main component of the singular value sequence; L is the difference index of the singular value sequence; O is the self-similarity coefficient of the Hankel matrix; D is the period chaos coefficient of the current sequence; is a preset tuning parameter factor.

[0012] Furthermore, the method for fault location of the transmission line based on the signal purity index includes: Obtain the default threshold for wavelet denoising based on the signal purity index of the current sequence. The formula is: ; where is the default threshold for wavelet denoising; n is the number of elements in the current sequence, H is the signal purity index of the current sequence, is the logarithmic function with base 10; is a preset tuning exponent; Denoise the current traveling wave signal according to the default threshold for wavelet denoising, and use the traveling wave ranging method to calculate the fault distance in the transmission line.

[0013] In a second aspect, an embodiment of the present application also provides a transmission line fault fusion ranging device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method described in any one of the above.

[0014] The present application has at least the following beneficial effects: In this application, the trend fluctuation index of subsequences is constructed to calculate the oscillation degree of subsequences and the monotonic degree on both sides of the maximum value. Then, the periodic chaos coefficient is constructed to calculate the similarity degree of fault subsequences in the current sequence, thereby evaluating the degree of noise interference suffered by the current traveling wave signal. Finally, the signal purity index is constructed to further evaluate the noise content in the current traveling wave signal, and based on this, the threshold of wavelet denoising is calculated.

[0015] The beneficial effects are as follows: By constructing the signal purity index to evaluate the degree of noise interference in the current traveling wave signal, and then dynamically adjusting the threshold of wavelet denoising, the problem that the existing technology has poor denoising effect due to inconsistent noise interference degrees in different traveling wave signals is solved, the denoising effect on the current traveling wave signal is improved, and the accuracy of fault location is improved. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a step flow chart of a transmission line fault fusion ranging method provided by an embodiment of the present application; Figure 2 It is a waveform diagram of un-denoised current traveling wave data provided by an embodiment of the present application; Figure 3 It is a schematic diagram of the existing filtering denoising effect provided by an embodiment of the present application; Figure 4 It is a schematic diagram of the improved filtering denoising effect provided by an embodiment of the present application. Detailed Embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of a transmission line fault fusion ranging method and device proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0020] The following specifically describes the specific solutions of a transmission line fault fusion ranging method and device provided by the present application in conjunction with the accompanying drawings.

[0021] Please refer to Figure 1 , which shows a step flowchart of a transmission line fault fusion ranging method provided by an embodiment of the present application. The method includes the following steps: Step S1, obtain relevant data and process it.

[0022] In the present application, a Rogowski coil traveling wave sensor installed on the transmission line is used to collect the current traveling wave signals that appear instantaneously when a fault occurs on the transmission line. The data acquisition frequency is 300KHz, and the acquisition duration each time is 0.1s. The collected current traveling wave signals are converted into numerical data through an analog-to-digital converter, and a current sequence is constructed according to the time sequence of data acquisition.

[0023] Step S2, construct a trend fluctuation index to evaluate the degree of data oscillation inside the current traveling wave signal when a fault occurs on the transmission line; construct a period chaos coefficient to evaluate whether the fault subsequence in the current sequence still has similarity; finally, construct a signal purity index to evaluate the degree of noise interference received by the current sequence.

[0024] When no fault occurs, the current signal has strong periodicity; if a fault occurs, the current signal will almost suddenly increase simultaneously when the fault occurs, and the data fluctuation becomes several times larger; however, although the current signal has a sudden increase change, after the sudden increase change, the current signal still has periodicity, the intervals between peaks are consistent, and the peaks and the faults both decrease steadily to a certain extent over time.

[0025] If the degree of noise interference in the current traveling wave signal is small, the fluctuating data in the current sequence still has a certain degree of periodicity; if the degree of noise interference is very large, the periodicity of the fluctuating data in the current sequence will be destroyed; therefore, the degree of noise interference can be evaluated by calculating whether the fluctuating data in the current sequence still has periodicity after a fault occurs.

[0026] Since the data collected is when a fault occurs on the transmission line, and the trend difference and data fluctuation difference between the current data after the fault and before the fault are both large, but at the same time, the current data after the fault and before the fault both have a certain internal similarity, the abnormally large fluctuating current data after the fault can be extracted by clustering.

[0027] All the peaks and valleys in the current sequence are obtained through the peak-valley detection algorithm, and the current sequence is segmented into multiple current subsequences according to the valleys. Calculate the range of each current subsequence, and use the ranges of all current subsequences as the input of the k-means clustering algorithm. Set the value of k in the k-means clustering algorithm to 2. The output of the k-means clustering algorithm is two clusters. The k-means algorithm is a well-known technology and will not be elaborated here.

[0028] Calculate the mean value of the internal elements of each cluster. Since the data fluctuation of the current after the fault is much larger than that before the fault, and the range of the current subsequence after the fault is larger, the cluster with the largest mean value of the internal elements is taken as the fault cluster. The current subsequence corresponding to the internal elements of the fault cluster is taken as the fault subsequence.

[0029] For each fault subsequence, obtain the maximum value in each fault subsequence. The subsequence composed of the maximum value and all the elements on the left side of the maximum value is taken as the left subsequence of each fault subsequence, and the subsequence composed of all the elements on the right side of the maximum value is taken as the right subsequence of each fault subsequence.

[0030] If the interference degree of the current traveling wave signal by noise is small, the data fluctuation inside each fault subsequence should be relatively smooth, the change between data is relatively small and orderly. Therefore, the left subsequence and the right subsequence should respectively show an increasing and a decreasing trend, and the degree of increase or decrease each time is relatively consistent; if the interference degree by noise is very large, the data fluctuation will be large and disorderly.

[0031] Obtain the first-order difference sequence of the left subsequence of each fault subsequence, and through The function judges and modifies all the elements in the first-order difference sequence to obtain the difference step sequence. The modification rule is as follows: if the element value is greater than 0, the element is set to 1; if the element value is 0, the element remains unchanged; if the element value is less than 0, the element is set to -1.

[0032] Take the absolute value of the difference between the sum of the elements in the difference step sequence and the length of the first-order difference sequence as the oscillation index of the left subsequence of each fault subsequence; when the oscillation index is 0, it proves that the left subsequence has strict monotonicity. The larger the oscillation index, the worse the monotonicity of the left subsequence. Through The function can judge the monotonicity of the sequence without considering the size of the element value.

[0033] In the same way, obtain the oscillation index of the right subsequence of each fault subsequence; calculate the absolute value of the difference and the mean value between the oscillation indices of the left subsequence and the right subsequence, and take the product of the absolute value of the difference and the mean value as the two-side oscillation coefficient of each fault subsequence.

[0034] Calculate the variance of the elements in the left subsequence and the variance of the elements in the right subsequence of each fault subsequence respectively, and use the mean of the variances of the left subsequence and the right subsequence as the change coefficients on both sides of each fault subsequence. The smaller the change coefficients on both sides are, the more consistent the degree of change of the data on both sides of the maximum value is each time in the fault subsequence, and the relatively stable the fluctuation of the data on both sides is.

[0035] Furthermore, in order to evaluate the data smoothing degree of each fault subsequence, construct the trend fluctuation index of each fault subsequence. The formula is: . In the formula, is the trend fluctuation index of the i-th fault subsequence; is the oscillation coefficient on both sides of the i-th fault subsequence; is the change coefficient on both sides of the i-th fault subsequence.

[0036] If the oscillation coefficient on both sides is smaller, it indicates that in the i-th fault subsequence, both the left subsequence and the right subsequence have strong monotonicity and the monotonicity degrees are relatively consistent, and the possibility of data oscillation is small; if the change coefficient on both sides is smaller, it indicates that the change amplitude of the internal data in the left subsequence and the right subsequence is relatively consistent each time; therefore, if the trend fluctuation index is smaller, it indicates that the internal data change trend of the i-th fault subsequence is smoother, and the change amplitude of the data is more consistent each time, and the influence of noise interference is smaller.

[0037] If the oscillation coefficient on both sides is larger, it indicates that the monotonicity of the left subsequence and the right subsequence is weaker, and the possibility of large data oscillation is greater; if the change coefficient on both sides is larger, it indicates that the data change amplitude in the left subsequence and the right subsequence is also larger; therefore, if the trend fluctuation index is larger, it indicates that the change amplitude of the numbers in the i-th fault subsequence each time is larger, and the influence of noise interference is greater.

[0038] So far, obtain the trend fluctuation index of each fault subsequence.

[0039] Sort all the peaks in all the fault subsequences according to their sequence positions in the current sequence to construct a peak sequence; sort the sequence positions of all the peaks in the fault subsequence according to their sizes to construct a peak sequence position sequence.

[0040] If the degree of noise interference when the transmission line fails is very small, the degree of decrease between adjacent peaks in the post-fault current sequence is relatively consistent, and the interval between adjacent peaks is relatively consistent, then the differences between adjacent data in the peak sequence and the peak sequence position sequence are relatively consistent.

[0041] Obtain the first-order difference sequence of the peak sequence, and take the variance of the first-order difference sequence as the peak drop difference; similarly, take the variance of the first-order difference sequence of the peak rank sequence as the peak interval difference.

[0042] Furthermore, calculate the mean of the trend fluctuation indices of all fault subsequences as the average fluctuation index.

[0043] Obtain the mean of the Pearson similarity coefficients between the i-th fault subsequence and all other fault subsequences as the sequence similarity of the i-th fault subsequence.

[0044] Furthermore, in order to evaluate the degree of noise interference received by the current traveling wave signal, construct the periodic chaos coefficient of the current sequence, and the formula is: . Where; D is the periodic chaos coefficient of the current sequence; are the peak drop difference and the peak interval difference respectively; is the average fluctuation index; P is the mean of the sequence similarities of all fault subsequences; is a preset tuning parameter, and in this embodiment, it is taken as 1 to avoid the denominator being 0.

[0045] If the peak drop difference and the peak interval difference are both small, it indicates that after the fault occurs, the peaks in each fault subsequence still have strong regularity, the peaks decrease with time, and the degree of decrease is the same and the intervals between the peaks are the same; if the average fluctuation index is small, it indicates that the data within each fault subsequence is relatively smooth and the data change range is relatively consistent; if the mean P of the sequence similarities is large, it indicates that there is a certain similarity between the fault subsequences and the data trends are relatively consistent; therefore, if the periodic chaos coefficient D is smaller, it indicates that the degree of noise interference is smaller and there is still a certain similarity between the fault subsequences.

[0046] If the peak drop difference and the peak interval difference are both large, it indicates that the peak fluctuation differences between the fault subsequences are large, and they no longer have the regularity of the same degree of decrease and the same interval; if the average fluctuation index is larger, it indicates that the data change degrees within each fault subsequence are more inconsistent and the data oscillation degree is larger; if the mean P of the sequence similarities is smaller, it indicates that the correlation between the fault subsequences is poorer and the data change trends are more inconsistent; therefore, if the periodic chaos coefficient D is larger, it indicates that the degree of noise interference received by the current traveling wave signal is larger.

[0047] So far, obtain the periodic chaos coefficient of the current sequence.

[0048] Due to the non-uniformity of line parameters and unpredictable factors such as external interference on the hardware during the acquisition process, the collected traveling wave signals will contain a large amount of high-frequency noise. A Hankel matrix is constructed through the current sequence and singular value decomposition is performed, and the decomposed singular values are arranged in descending order to construct a singular value sequence. The construction of the Hankel matrix and singular value decomposition are well-known techniques and will not be elaborated here.

[0049] In the current traveling wave signals, larger singular values are usually related to the main components of the traveling wave signals, such as the transient response and fundamental frequency components during a fault, while smaller singular values often correspond to noise components.

[0050] By further analyzing the decomposed singular values, if the noise content in the current traveling wave signals is very low, the rows in the constructed Hankel matrix will be relatively similar, and several larger singular values in the decomposed singular values will correspond to the main components of the current traveling wave signals, and the differences between the larger singular values are relatively large; if the noise content in the current traveling wave signals is particularly high, the differences between the rows in the constructed Hankel matrix are relatively large. Although there will be larger singular values in the decomposed singular values, since the main components of the signal are masked by noise, the number of larger singular values will be less. On the contrary, the number of smaller singular values will increase, and the singular values corresponding to the noise are relatively consistent.

[0051] The singular value sequence is used as the input of the Otsu threshold method. The output of the Otsu threshold method is the threshold T. All singular values greater than or equal to the threshold T are regarded as important singular values, and all singular values less than the threshold T are regarded as secondary singular values. The ratio of the number of important singular values to the total number of all singular values is used as the proportion G of the main components in the singular value sequence; the larger the proportion of the main components, the more the number of larger singular values, and the better the signal quality.

[0052] Calculate the mean and variance of all important singular values in the singular value sequence, and take the product of the mean and variance as the fluctuation index of the important singular values; similarly, calculate the fluctuation index of the secondary singular values, and take the absolute value of the difference between the fluctuation index of the important singular values and the fluctuation index of the secondary singular values as the difference index L of the singular value sequence; the larger the difference index, the greater the fluctuation difference and mean difference between the important singular values and the secondary singular values, indicating that the main components corresponding to the larger singular values are more obvious and the degree of noise interference is smaller.

[0053] In the Hankel matrix, calculate the absolute value of the Pearson similarity coefficient between each row of data and the data of other rows, and take the mean of all the absolute values of the Pearson similarity coefficients of each row of data as the similarity coefficient of each row of data; take the mean of the similarity coefficients of all row data in the Hankel matrix as the self-similarity coefficient O of the Hankel matrix.

[0054] Further, in order to reflect the degree of noise interference on the current traveling wave signal, a signal purity index is constructed, and the formula is: ; where H is the signal purity index of the current sequence; G is the proportion of the main components in the singular value sequence; L is the difference index of the singular value sequence; O is the self-similarity coefficient of the Hankel matrix; D is the period chaos coefficient of the current sequence; is a preset tuning factor. To avoid a zero denominator, in this embodiment, its value is 1.

[0055] If the proportion of the main components G is larger, it indicates that the number of larger singular values decomposed from the Hankel matrix constructed by the current sequence is more, and the corresponding main components in the current traveling wave signal are more; if the difference index L is larger, it indicates that the difference between the important singular values and the minor singular values is larger; if the self-similarity coefficient O is larger, it indicates that the data in each row of the Hankel matrix constructed by the current sequence are more similar and the noise content is less; if the period chaos coefficient D is smaller, it indicates that the degree of noise interference is smaller; therefore, if the signal purity index H is larger, it indicates that the degree of noise interference on the current traveling wave signal is smaller.

[0056] If the proportion of the main components G is smaller, it indicates that the number of important singular values is less; if the difference index L is smaller, it indicates that the difference between the important singular values and the minor singular values is smaller, the singular value fluctuation is smaller, and the noise content is larger; if the self-similarity coefficient O is smaller, it indicates that the similarity between each row of data in the Hankel matrix is smaller; if the period chaos coefficient D is larger, it indicates that the degree of noise interference is larger; therefore, if the signal purity index H is smaller, it indicates that the degree of noise interference on the current traveling wave signal is larger.

[0057] Step S3: Denoise the current traveling wave signal and implement fault location.

[0058] In the method for estimating the threshold of wavelet denoising, the default threshold determination model determines the threshold according to the intensity of the noise. Therefore, in this application, the default threshold of wavelet denoising is calculated using the signal purity index obtained by calculation, and the threshold is obtained, where is the default threshold of wavelet denoising; n is the number of elements in the current sequence, H is the signal purity index of the current sequence, is the logarithmic function with base 10; is a preset tuning exponent. To avoid a zero denominator, in this embodiment, its value is 1.

[0059] Further, the collected current traveling wave signal is denoised according to the threshold thr. Among them, the waveform diagram of the un-denoised current traveling wave data is as Figure 2 shown, and the schematic diagram of the existing filtering denoising effect is as Figure 3As shown in the figure, although the existing filtering and denoising algorithms have good denoising effects, the denoising effects are too smooth, and the useful information in the denoised signal is lost, making it difficult to distinguish when a fault occurred; the schematic diagram of the improved filtering and denoising effect is as shown in Figure 4 As shown, not only the original data is denoised, but also the useful information in the signal is not lost. It can be clearly found that the current waveform has abnormal changes at 0.025 seconds. Then, for the denoised traveling wave signal, the fault distance ranging formula in the traveling wave ranging method is used to calculate the fault distance in the transmission line, realizing a fault fusion ranging method and device for transmission lines.

[0060] Based on the same inventive concept as the above method, the embodiment of the present application also provides a fault fusion ranging device for transmission lines, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned fault fusion ranging methods for transmission lines.

[0061] It should be noted that: the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0063] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A transmission line fault fusion distance measurement method, characterized in that: The method comprises the following steps: Collect the current sequence composed of the current traveling wave signal at the moment of fault on the transmission line; The local elements are divided according to the distribution of the troughs in the current sequence, and the local elements in the current sequence are clustered according to the distribution of the local elements to obtain each fault subsequence; Based on the fluctuation of the change trend of local elements in the fault subsequence and the difference between the local elements, the trend fluctuation index of each fault subsequence is obtained; Based on the difference in the change trend of the peak value of the fault subsequence and the difference in the change trend of the position sequence of the peak value in the fault subsequence, the peak drop difference and the peak interval difference are obtained; Based on the peak drop difference, the peak interval difference, the average trend fluctuation index of all fault subsequences, and the average similarity between fault subsequences, the period chaos coefficient of the current sequence is obtained; Construct the Hankel matrix of the current sequence, perform singular value decomposition on the Hankel matrix to obtain each singular value, and obtain the main component proportion and difference index of the singular value sequence of the current sequence based on the distribution, average and difference of the singular values; The signal purity index of the current sequence is obtained based on the correlation, periodic chaos coefficient, main component proportion and difference index among the data rows in the Hankel matrix; the transmission line fault is located based on the signal purity index.

2. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method for obtaining the fault subsequence is: Use the peak and valley detection algorithm to obtain all peaks and valleys in the current sequence, divide the current sequence into subsequences from each valley, calculate the range of elements in each subsequence, use the range of all subsequences as the input of the clustering algorithm, and output each clustering cluster; The internal element mean of each cluster is calculated, and the cluster with the largest internal element mean is taken as the fault cluster, and the current subsequence corresponding to the internal elements of the fault cluster is taken as the fault subsequence.

3. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method for obtaining the trend fluctuation index is: Based on the fluctuation of the change trend of the local elements in the fault subsequence and the difference between the local elements, the oscillation coefficients on both sides and the change coefficients on both sides of each fault subsequence are obtained; Combining the oscillation coefficients on both sides and the variation coefficients on both sides of each fault subsequence, the trend fluctuation index of each fault subsequence is obtained. The formula is: ; In the formula, is the trend fluctuation index of the i-th fault subsequence; is the oscillation coefficient on both sides of the i-th fault subsequence; is the coefficient of variation on both sides of the i-th fault subsequence.

4. A transmission line fault fusion distance measurement method as claimed in claim 3, characterized in that: The method for obtaining the variation coefficients on both sides is: For each fault subsequence, obtain the maximum value in each fault subsequence, take the subsequence consisting of the maximum value and all elements to the left of the maximum value as the left subsequence of each fault subsequence, and take the subsequence consisting of all elements to the right of the maximum value as the right subsequence of each fault subsequence; Obtain the first-order difference sequence of the left subsequence of each fault subsequence, and use the step function to process all elements in the first-order difference sequence to obtain a differential step sequence; Calculate the absolute value of the difference between the absolute value of the sum of the elements in the differential step sequence and the length of the first-order difference sequence of the left subsequence as the oscillation index of the left subsequence of each fault subsequence; Using the same method as the left subsequence, obtain the oscillation index of the right subsequence of each fault subsequence; Calculate the product of the absolute value and the mean of the difference between the oscillation indexes of the left subsequence and the right subsequence as the oscillation coefficient on both sides of each fault subsequence; The variance of the elements in the left subsequence and the variance of the elements in the right subsequence of each fault subsequence are calculated, and the mean of the variance of the left subsequence and the right subsequence is taken as the coefficient of variation on both sides of each fault subsequence.

5. A transmission line fault fusion distance measurement method as claimed in claim 2, characterized in that: The method for obtaining the peak interval difference is: All peaks in all fault subsequences are sorted according to their position in the current sequence to construct a peak sequence; the positions of all peaks in the fault subsequences are sorted according to their size to construct a peak position sequence; The first-order difference sequence of the peak sequence and the peak position sequence is obtained, and the variance of all elements in the first-order difference sequence of the peak sequence is taken as the peak drop difference; the variance of all elements in the first-order difference sequence of the peak position sequence is taken as the peak interval difference.

6. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method for obtaining the periodic disorder coefficient is: Calculate the average of the trend fluctuation indexes of all fault subsequences as the average fluctuation index; Obtaining the mean of the Pearson similarity coefficients between each fault subsequence and all other fault subsequences as the sequence similarity of each fault subsequence; The formula for the periodic disorder coefficient is: ; in; D is the periodic chaos coefficient of the current sequence; is the peak drop difference and peak interval difference; is the average fluctuation index; P is the mean of the sequence similarity of all fault subsequences; is the preset tuning coefficient.

7. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method for obtaining the main component proportion and difference index is: Sort all singular values ​​in descending order to construct a singular value sequence; The singular value sequence is used as the input of the Otsu threshold method, and the output of the Otsu threshold method is the threshold T. All singular values ​​greater than or equal to the threshold T are regarded as important singular values, and all singular values ​​less than the threshold T are regarded as secondary singular values. The ratio of the number of important singular values ​​to the total number of all singular values ​​is regarded as the main component ratio of the singular value sequence. Calculate the mean and variance of all important singular values ​​in the singular value sequence, and take the product of the mean and variance as the fluctuation index of the important singular value; use the same method as the fluctuation index of the important singular value to calculate the fluctuation index of the secondary singular value, and take the absolute value of the difference between the fluctuation index of the important singular value and the fluctuation index of the secondary singular value as the difference index of the singular value sequence.

8. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method for obtaining the signal purity index is: Calculate the absolute value of the Pearson similarity coefficient between each row of data in the Hankel matrix and other rows of data, and use the average of the absolute values ​​of all the Pearson similarity coefficients of each row of data as the similarity coefficient of each row of data; use the average of the similarity coefficients of all rows of data in the Hankel matrix as the self-similarity coefficient of the Hankel matrix; The formula for the signal purity index is: ; In the formula, H is the signal purity index of the current sequence; G is the main component ratio of the singular value sequence; L is the difference index of the singular value sequence; O is the self-similarity coefficient of the Hankel matrix; D is the periodic chaos coefficient of the current sequence; It is the preset parameter factor.

9. A transmission line fault fusion distance measurement method as claimed in claim 1, characterized in that: The method of measuring the distance of a transmission line fault based on a signal purity index comprises: The default threshold of wavelet denoising is obtained based on the signal purity index of the current sequence. The formula is: ; In the formula, is the default threshold of wavelet denoising; n is the number of elements in the current sequence, H is the signal purity index of the current sequence, is a logarithmic function with base 10; is the preset parameter index; The current traveling wave signal is denoised according to the default threshold of wavelet denoising, and the fault distance in the transmission line is calculated using the traveling wave ranging method.

10. A transmission line fault fusion ranging device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the transmission line fault fusion ranging method as described in any one of claims 1-9 are implemented.

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