Fault traveling wave front calibration method and device, equipment and storage medium

Abstract

Embodiments of the present application disclose a fault traveling wave front calibration method and device, equipment and a storage medium, the method comprising: collecting a fault traveling wave signal when a power distribution network fails; decomposing the fault traveling wave signal based on a pole symmetry modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal; performing difference operation on the target modal component of the fault traveling wave signal using an improved Teager energy algorithm to obtain an energy spectrum sequence; and determining the position of the fault traveling wave front according to extreme points in the energy spectrum sequence. On the one hand, the fault traveling wave signal is decomposed using the extreme point modal decomposition method, which can effectively filter out low-frequency signals and reduce the interference of noise on the signal; on the other hand, the improved Teager energy operator is used to perform time-frequency analysis on the fault traveling wave signal component, which enhances the frequency domain characteristics of the signal, thereby improving the anti-noise ability of the algorithm, so as to realize accurate calibration of the fault traveling wave signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault location, in particular to a method and device for calibrating a fault traveling wave front, equipment and a storage medium. BACKGROUND

[0002] Accurate identification of distribution line faults is an important foundation for reliable operation of power grids. With the increasing scale of power grids, fast and accurate identification of distribution line faults plays a crucial role in improving fault handling effectiveness, reducing the cost of troubleshooting, and quickly restoring power supply after a fault.

[0003] In current distribution network fault location methods, the traveling wave location method requires high positioning accuracy, and the fault location accuracy is closely related to the traveling wave front time. Regardless of which traveling wave location method is used, the time of the traveling wave front arriving at the detection position needs to be detected, so the calibration of the traveling wave front will affect the positioning accuracy. There is currently no accurate method for detecting the arrival time of the traveling wave front. SUMMARY

[0004] Therefore, it is necessary to propose a method and device for calibrating a fault traveling wave front, equipment and a storage medium to improve the accuracy of the calibration result.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a method for calibrating a fault traveling wave front, which comprises:

[0006] When a fault occurs in a distribution network, a fault traveling wave signal is collected;

[0007] The fault traveling wave signal is decomposed based on a pole symmetry modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal;

[0008] The target modal component of the fault traveling wave signal is subjected to a difference operation using an improved teager energy algorithm to obtain an energy spectrum sequence;

[0009] The fault traveling wave front is calibrated according to extreme points in the energy spectrum sequence.

[0010] Further, the method for calibrating a fault traveling wave front comprises:

[0011] The fundamental frequency and the sampling frequency of the fault traveling wave signal are obtained;

[0012] The sampling time parameter is determined according to the fundamental frequency and the sampling frequency;

[0013] The Teager energy algorithm is improved according to the sampling time parameter, and a differential operation is performed on the target modal component of the fault traveling wave signal according to the improved Teager energy algorithm to obtain an energy operator corresponding to different sampling times in the target modal component;

[0014] An energy spectrum sequence is obtained according to the energy operator corresponding to different sampling times in the target modal component.

[0015] Further, the sampling time parameter is determined according to the fundamental frequency and the sampling frequency, and specifically includes:

[0016] The value range of the sampling time parameter is determined according to the fundamental frequency and the sampling frequency;

[0017] Any point in the value range is taken as the sampling time parameter;

[0018] The value range of the sampling time is calculated by the following formula:

[0019] f0 / f m <1 / 8i

[0020] In the formula, f0 is the fundamental frequency, f m is the sampling frequency, and i is the sampling time parameter.

[0021] Further, the energy operator corresponding to different sampling times in the target modal component is calculated by the following formula:

[0022] Ψ[IMF1(t)]=IMF1(t)IMF1(t)-IMF1(t+i)IMF1(t-i)

[0023] In the formula, IMF1(t) is the target modal component, t is the sampling time, and i is the sampling time parameter.

[0024] Further, the fault traveling wave front is calibrated according to the extreme point in the energy spectrum sequence, and specifically includes:

[0025] The extreme point in the energy spectrum sequence is obtained;

[0026] Connecting adjacent two extreme points, a plurality of energy line segments are obtained;

[0027] The slopes of all energy line segments are calculated, and the slopes of all energy line segments are compared to determine a target energy line segment corresponding to the maximum absolute value of the slope;

[0028] The position of the fault traveling wave front is determined according to the sampling time corresponding to the target energy line segment.

[0029] Further, the pole-symmetry modal decomposition algorithm is used to decompose the fault traveling wave signal to obtain a target modal component of the fault traveling wave signal, and specifically includes:

[0030] Kth time, the extreme points of the fault traveling wave signal are obtained, and adjacent two extreme points of the fault traveling wave signal are connected to obtain a plurality of traveling wave line segments, wherein the initial value of K is 1;

[0031] The boundary center points are added to the fault traveling wave signal by using linear interpolation method;

[0032] A plurality of signal interpolation curves are generated according to the center points of the traveling wave line segments and the boundary center points;

[0033] The curve mean values of all the signal interpolation curves are calculated;

[0034] When the curve mean values do not satisfy the preset curve condition and K does not satisfy the preset first cycle condition, a first difference value between the fault traveling wave signal and the curve mean values is calculated, the first difference value is taken as the fault traveling wave signal, K is set to K+1, and the step of obtaining the extreme points of the fault traveling wave signal and connecting adjacent two extreme points of the fault traveling wave signal to obtain a plurality of traveling wave line segments is continued to be executed;

[0035] When the curve mean values satisfy the preset curve condition or K satisfies the preset first cycle condition, the fault traveling wave signal is decomposed to obtain a first layer modal component, and the first layer modal component is taken as the target modal component.

[0036] Further, the method further includes:

[0037] The first cycle condition is that K is greater than a first cycle threshold value, and the first cycle threshold value is any value in a preset cycle range;

[0038] The value of the first cycle threshold value is changed multiple times, and a residual amount obtained by decomposing the fault traveling wave signal under each first cycle threshold value is obtained;

[0039] The relative standard deviations are calculated according to the fault traveling wave signal and the residual amount, the target relative standard deviations meeting the preset screening standard are determined according to the sizes of all the relative standard deviations, and a target first cycle threshold value corresponding to the target relative standard deviations is obtained;

[0040] The first layer modal component of the fault traveling wave signal obtained under the target first cycle threshold value is taken as the target modal component.

[0041] To achieve the above object, the second aspect of the present application provides a fault traveling wave head calibration device, the device comprises: a signal acquisition module, a signal decomposition module and a fault location module;

[0042] The signal acquisition module is configured to acquire a fault traveling wave signal when a fault occurs in a power distribution network.

[0043] The signal decomposition module is configured to decompose the fault traveling wave signal based on a pole symmetry modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal.

[0044] The fault location module is configured to perform a difference operation on the target modal component of the fault traveling wave signal by using an improved Teager energy algorithm to obtain an energy spectrum sequence.

[0045] The position of the fault traveling wave head is determined according to extreme points in the energy spectrum sequence.

[0046] To achieve the above object, the third aspect of the present application provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to make the processor perform the steps of the method according to the first aspect.

[0047] To achieve the above object, the fourth aspect of the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor perform the steps of the method according to the first aspect.

[0048] The present application has the following advantages:

[0049] The present application provides a fault traveling wave head calibration method, which comprises the following steps: acquiring a fault traveling wave signal when a fault occurs in a power distribution network; decomposing the fault traveling wave signal based on a pole symmetry modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal; performing a difference operation on the target modal component of the fault traveling wave signal by using an improved Teager energy algorithm to obtain an energy spectrum sequence; and determining the position of the fault traveling wave head according to extreme points in the energy spectrum sequence. On the one hand, the fault traveling wave signal is decomposed by using the extreme point modal decomposition method, which can effectively filter out low-frequency signals. On the other hand, the improved Teager energy operator is used to perform time-frequency analysis on the fault traveling wave signal component, which enhances the frequency domain characteristics of the signal and improves the anti-noise ability of the algorithm, so as to realize accurate calibration of the fault traveling wave signal. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only show some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0051] Wherein:

[0052] Figure 1 The flowchart of the fault traveling wave head calibration method of the embodiment of the present application;

[0053] Figure 2 The structural block diagram of the fault traveling wave head calibration device of the embodiment of the present application;

[0054] Figure 3 The internal structure diagram of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort are within the scope of protection of the present application.

[0056] In the current power distribution network fault location method, the fault traveling wave positioning method has higher positioning accuracy and is basically not affected by the transition resistance and the neutral point grounding mode, so it is widely used in the current power system. The accuracy of fault location is closely related to the traveling wave head time, but no matter which traveling wave positioning method is used, the time of traveling wave head arriving at the detection position needs to be detected, so the calibration accuracy of traveling wave head will affect the fault location accuracy.

[0057] At present, the traveling wave head calibration method is usually based on the isochronous frequency analysis method of wavelet transform, which analyzes the signal by translating and scaling a wavelet function at different scales. However, due to the large number of wavelet basis functions and decomposition scales in the wavelet transform method in theory, it is difficult to have good adaptive ability. Or based on Hilbert-Huang transform combined with empirical mode decomposition and Hilbert transform, relying on signal change to decompose high frequency components. However, the empirical mode decomposition method has the problems of over envelope and under envelope in theory, and the modal components decomposed will appear serious end effect and aliasing phenomenon, causing difficulty in traveling wave head signal calibration, or the traditional Teager energy operator cannot ignore the noise energy in the case of low signal-to-noise ratio, and the detection result has large deviation, affecting the accuracy of the result.

[0058] Based on this, the extreme point symmetry modal decomposition algorithm (ESMD) is used to filter low-frequency signal components from the fault signal, and a new teager energy operator (NTEO) operation is used to improve the anti-noise capability of the algorithm based on the original signal, enhance the amplitude mutation characteristics of the traveling wave head, and accurately identify the traveling wave head to accurately locate the fault point.

[0059] For details, please refer to Figure 1 , Figure 1 The flowchart of the method for calibrating the fault traveling wave head in the embodiment of the application is shown in the figure, and the method comprises:

[0060] Step 110: When a fault occurs in the power distribution network, a fault traveling wave signal is collected.

[0061] In a power system, when a fault (such as a short circuit, grounding, etc.) occurs, a transient voltage and current traveling wave is generated at the fault point. These traveling waves propagate at a speed close to the speed of light in the transmission line and propagate along the line to both sides. The traveling wave carries fault information, so the traveling wave signal under fault can be obtained, and analysis can be performed based on the traveling wave signal to achieve fault location.

[0062] In the embodiment of the application, a preset acquisition frequency is set, and a sensor is installed at a preset position to collect the traveling wave signal based on the acquisition frequency.

[0063] Step 120: The fault traveling wave signal is decomposed based on the extreme point symmetry modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal.

[0064] Specifically, the extreme point symmetry modal decomposition algorithm is used to find the maximum and minimum points in the fault traveling wave signal, and the signal is decomposed based on this to obtain a plurality of intrinsic modal functions of the fault traveling wave signal. Any intrinsic modal function is selected as a target modal component for subsequent analysis.

[0065] In an embodiment of the application, the first layer modal component is selected as the target modal component. Since the first layer modal component usually represents the high-frequency component in the traveling wave signal, the high-frequency component often contains the main characteristics or key information of the signal. Therefore, subsequent analysis based on the first layer modal component can more accurately obtain the fault information in the traveling wave signal.

[0066] Step 130: The target modal component of the fault traveling wave signal is subjected to a difference operation using an improved teager energy algorithm to obtain an energy spectrum sequence.

[0067] Specifically, the traditional teager energy operator (TEO) operation estimates the signal instantaneous energy of the current sample point by using three adjacent sample points of the signal, but the anti-noise capability is insufficient. Considering that the energy of the noise will affect the operation result of the energy operator to some extent and affect the accuracy of the detection result, the embodiment of the present application improves the traditional teager energy operator (TEO) operation. The improved teager energy algorithm can reduce the energy of the noise, effectively denoises the signal instantaneous energy of the current sample point, and improves the calculation accuracy of the signal energy.

[0068] In the embodiment of the present application, the teager energy algorithm is improved, and the difference operation is performed on each sample point in the target modal component to obtain the signal instantaneous energy of each sample point, and then the energy spectrum sequence of the target modal component is obtained based on the time sequence.

[0069] Step 140, determining the position of the fault traveling wave front according to the extreme points in the energy spectrum sequence.

[0070] In the embodiment of the present application, the maximum value point and the minimum value point in the energy spectrum sequence are obtained, the maximum value point and the minimum value point reflect the instantaneous energy mutation characteristics, the feature mutation point can be determined based on the maximum value point and the minimum value point in the energy spectrum sequence, the initial time when the fault traveling wave front reaches the sample point is determined according to the sampling time corresponding to the feature mutation point, and then the position of the fault traveling wave front is determined according to the preset traveling wave positioning algorithm.

[0071] The embodiment of the present application utilizes the extreme point modal decomposition method to decompose the fault traveling wave signal, effectively filters out the low-frequency signal, retains the high-frequency signal as the analysis basis, and improves the accuracy of the analysis result. Secondly, the improved Teager energy operator is used for time-frequency analysis of the fault traveling wave signal component. By improving the anti-noise capability of the improved Teager energy operator, the influence of noise energy on the signal instantaneous energy is reduced, so as to realize accurate calibration of the fault traveling wave signal.

[0072] In an embodiment of the present application, step 120, the fault traveling wave signal is decomposed based on the pole symmetry modal decomposition algorithm to obtain the target modal component of the fault traveling wave signal, specifically including:

[0073] Step 210, the extreme points of the fault traveling wave signal are obtained for the Kth time, and the adjacent two extreme points of the fault traveling wave signal are connected to obtain a plurality of traveling wave line segments, wherein the initial value of K is 1.

[0074] Specifically, the maximum value point and the minimum value point of the fault traveling wave signal M(t) are obtained, M={m1, m2, m3, …, m n}。Two adjacent extreme points are connected to obtain (n-1) traveling wave line segments, such as m1m2, m2m3, m3m4……etc.

[0075] Step 220, adding the boundary center point of the fault traveling wave signal by using the linear interpolation method.

[0076] Since the length of the fault traveling wave signal is limited by the sampling time, there are a starting point and a terminal point, the embodiment of the application adopts the linear interpolation method to predict the boundary center point of the fault traveling wave signal.

[0077] Specifically, the center point between the starting point of the fault traveling wave signal and the previous signal point is predicted by using the linear interpolation method to obtain the boundary center point Z0; the center point between the terminal point of the fault traveling wave signal and the next signal point is predicted by using the linear interpolation method to obtain the boundary center point Z n .

[0078] Step 230, generating a plurality of signal interpolation curves according to the center points and the boundary center points of the traveling wave line segments.

[0079] In the embodiment of the application, the center points Z1, Z2, Z3, Z4, …, Z n-1 are marked for each traveling wave line segment. n Based on all the center points Z0, Z1, Z2, Z3, Z4, …, Z x , a plurality of signal interpolation curves L1, L2, L3, L4, …, L x are generated.

[0080] Step 240, calculating the curve mean value of all the signal interpolation curves.

[0081] Specifically, the average curve of all the signal interpolation curves is calculated, which is calculated by the following formula:

[0082] L=(L1+L2+…+L x ) / x

[0083] Step 250, when the curve mean value does not satisfy the preset curve condition and K does not satisfy the preset first cycle condition, a first difference value between the fault traveling wave signal and the curve mean value is calculated, the first difference value is taken as the fault traveling wave signal, K is set to K+1, and the step of acquiring the extreme points of the fault traveling wave signal for the Kth time is continued to be executed, and the adjacent two extreme points of the fault traveling wave signal are connected to obtain a plurality of traveling wave line segments.

[0084] Specifically, the curve mean value L is compared with the preset curve condition, if the curve mean value does not satisfy the curve condition, a first difference value [M(t)-L] between the fault traveling wave signal M(t) and the average curve L is calculated, and the first difference value is taken as the fault traveling wave signal to be updated, and Steps 210-240 are repeatedly executed until the curve mean value satisfies the curve condition or the cycle number K satisfies the preset first cycle condition.

[0085] In an embodiment of the present application, the curve condition is that the curve mean value is not greater than a curve threshold value ε, and ε = 0.0001ξ0, where ξ0 is the standard deviation of the fault traveling wave signal M(t). The ξ0 is calculated by the following formula:

[0086]

[0087] In the formula, M is the average signal value of the fault traveling wave signal M(t), N is the total number of the sampling signals in the fault traveling wave signal, and M N is the Nth sampling signal.

[0088] In an embodiment of the present application, the first cycle condition is that K > K max , that is, the cycle number K is greater than the preset maximum cycle number.

[0089] Step 260, when the curve mean value meets the preset curve condition or K meets the preset first cycle condition, the fault traveling wave signal is decomposed to obtain the first layer modal component, and the first layer modal component is taken as the target modal component.

[0090] In an embodiment of the present application, when the curve mean value is less than or equal to the curve threshold value, or the cycle number K reaches the maximum cycle number, the fault traveling wave signal is decomposed to obtain the first layer modal component.

[0091] Since the first layer modal component usually represents the high frequency component in the traveling wave signal, the high frequency component often contains the main features or key information of the signal. Therefore, the first layer modal component is taken as the target modal component.

[0092] In an embodiment of the present application, the maximum cycle number is also the first cycle threshold value, and the first cycle threshold value is any value in the preset cycle range [K m , K n ]. The present application further proposes:

[0093] Step 270, the value of the first cycle threshold value is changed for multiple times, and the residual obtained by decomposing the fault traveling wave signal under each first cycle threshold value is acquired.

[0094] Specifically, the i-th (the initial value of i is 1) execution of the steps of Step 210-Step 260 obtains the i-th layer modal component IMF i and the residual R i , the second difference value [M(t)-IMF i is obtained by subtracting the i-th layer modal component IMF i from the fault traveling wave signal M(t).the second difference value is updated as a fault traveling wave signal, i is set as i+1, and the steps of the i th execution of Step 210-Step 260 are continued until the residual R N the extreme point quantity in the residual R

[0095] Step 280, calculate the relative standard deviation according to the fault traveling wave signal and the residual, determine a target relative standard deviation meeting a preset screening standard according to the sizes of all the relative standard deviations, and obtain a target first cycle threshold corresponding to the target relative standard deviation.

[0096] In the embodiment of the present application, the relative standard deviation between the fault traveling wave signal and the residual is calculated by the following formula:

[0097]

[0098] In the formula, M is the fault traveling wave signal, N is the total number of cycles, and R is the residual.

[0099] According to the relative standard deviation σ0 and the standard deviation ξ0 of the fault traveling wave signal M(t), the signal standard deviation ratio σ0 / ξ0 is calculated.

[0100] The signal standard deviation ratios under different first cycle thresholds are calculated by the above method, all the signal standard deviation ratios are compared, the minimum signal standard deviation ratio is found, and the first cycle threshold corresponding to the minimum signal standard deviation ratio is taken as the target first cycle threshold.

[0101] Step 290, take the first layer modal component of the fault traveling wave signal obtained under the target first cycle threshold as a target modal component.

[0102] In the embodiment of the present application, the first layer modal component obtained based on Step 210-Step 260 under the condition of the target first cycle threshold is taken as the target modal component.

[0103] In an embodiment of the present application, step 130, the target modal component of the fault traveling wave signal is subjected to a difference operation by using the improved teager energy algorithm to obtain an energy spectrum sequence, which specifically includes:

[0104] Step 310, obtain a fundamental frequency and a sampling frequency of the fault traveling wave signal.

[0105] Step 320, determine a sampling time parameter according to the fundamental frequency and the sampling frequency.

[0106] Step 330, improving the teager energy algorithm according to the sampling time parameter, and performing difference operation on the target modal component of the fault traveling wave signal according to the improved teager energy algorithm to obtain the energy operator corresponding to different sampling times in the target modal component.

[0107] Step 340, obtaining an energy spectrum sequence according to the energy operator corresponding to different sampling times in the target modal component.

[0108] In the embodiment of the application, the improved teager energy algorithm introduces a sampling time parameter i to reduce noise energy, and the energy of the current sampling point is calculated by selecting three sampling points before and after the current sampling point.

[0109] Specifically, the traditional teager energy algorithm uses a discrete Teager energy operator to calibrate the fault traveling wave front of the target modal component:

[0110] ψ [IMF1 (t)] = IMF1 (t) IMF1 (t) - IMF1 (t+1) IMF1 (t-1)

[0111] In the formula, IMF1 is the target modal component, and IMF1 (t-1) IMF1 (t) IMF1 (t+1) are three adjacent sampling points in the target modal component.

[0112] Specifically, the form of the discrete signal is defined as:

[0113]

[0114] Where ω is the angular frequency, f0 is the fundamental frequency, and f m is the sampling frequency.

[0115] Therefore, A 2 sin 2 ω = M 2 (t) - M (t+1) M (t-1)

[0116] When ω < π / 2, that is, f0 < f m / 4, the above equation has a unique solution; and when ω < π / 4, that is, f0 < f m / 8, it can be approximately understood that the equation is established, that is, when the sampling frequency is greater than 8 times the basic frequency of the signal, there is a simple signal energy measurement method, and the expression form is:

[0117] ψ [M (t)] = M 2 (t) - M (t+1) M (t-1) ≈ A 2 ω 2

[0118] Wherein, ψ[M(t)] is a sampling signal energy, M(t) is a fault traveling wave signal.

[0119] Based on this, the constraint condition of the sampling time parameter i in the improved teager energy algorithm is:

[0120] f0 / f m <1 / 8i

[0121] The upper limit of the sampling time parameter i can be calculated by the above formula, and then the value range of the sampling time parameter is obtained, and the sampling time parameter can be any value in the value range. By constraining the sampling time parameter i, the sampling time parameter i value can be selected based on actual requirements, so that the energy operator result calculated based on the sampling time parameter i is more accurate, and the influence of noise is effectively reduced.

[0122] In the embodiment of the application, the energy operator corresponding to different sampling times in the target modal component is calculated by the following formula:

[0123] Ψ[IMF1(t)]=IMF1(t)IMF1(t)-IMF1(t+i)IMF1(t-i)

[0124] In the formula, IMF1(t) is a target modal component, t is a sampling time, and i is a sampling time parameter.

[0125] In the embodiment of the application, step 140, the position of the fault traveling wave front is determined according to the extreme points in the energy spectrum sequence, and specifically includes:

[0126] Step 410, obtaining the extreme points in the energy spectrum sequence.

[0127] Specifically, the maximum points and minimum points of the energy spectrum sequence X(t) in the sampling time are obtained.

[0128] Step 420, connecting adjacent two extreme points to obtain a plurality of energy line segments.

[0129] Specifically, adjacent two extreme points are connected in turn to obtain a plurality of energy line segments.

[0130] Step 430, calculating the slopes of all energy line segments, and comparing the slopes of all energy line segments to determine the target energy line segment corresponding to the maximum value of the absolute value of the slope.

[0131] Specifically, the slopes of the energy line segments are calculated, and the absolute values of all slopes are compared to obtain the maximum slope absolute value, and the energy line segment corresponding to the maximum slope absolute value is taken as the target energy line segment.

[0132] Step 440, determining the position of the fault traveling wave wave front according to the sampling time corresponding to the target energy line segment.

[0133] In the embodiment of the application, when the absolute value of the slope reaches the maximum, the signal at the sampling time corresponding to the time is taken as a mutation feature point, and the wave head feature is extracted through the mutation feature point, and the position of the fault traveling wave wave front is determined based on the wave head feature.

[0134] The wave head feature includes the arrival time of the traveling wave wave front to the sampling device, signal amplitude and frequency, etc.

[0135] The signal amplitude and frequency can be calculated by the following formula:

[0136]

[0137] Wherein, V'(t) is the signal amplitude, f'(t) is the signal frequency;

[0138] In the embodiment of the application, the time of the traveling wave wave front to the measuring device is calibrated by detecting the peak value of the instantaneous energy, and the position of the fault traveling wave wave front is determined based on the arrival time and the preset positioning formula. The preset positioning formula can be the position of the fault traveling wave wave front calculated according to the arrival time and the traveling wave speed and other parameters.

[0139] In the application, a calibration device for the fault traveling wave wave front is also proposed, which can be referred to Figure 2 , Figure 2 The structure block diagram of the calibration device for the fault traveling wave wave front in the embodiment of the application is shown in the figure, and the device includes a signal acquisition module 201, a signal decomposition module 202 and a fault positioning module 203.

[0140] The signal acquisition module 201 is used to acquire the fault traveling wave signal when the power distribution network fails.

[0141] The signal decomposition module 202 is used to decompose the fault traveling wave signal based on the pole symmetry modal decomposition algorithm to obtain the target modal component of the fault traveling wave signal.

[0142] The fault positioning module 203 is used to perform difference operation on the target modal component of the fault traveling wave signal by using the improved teager energy algorithm to obtain the energy spectrum sequence. The position of the fault traveling wave wave front is determined according to the extreme point in the energy spectrum sequence.

[0143] The calibration device for the fault traveling wave head proposed in an embodiment of the present invention uses the extreme point modal decomposition method to decompose the fault traveling wave signal, effectively screens and filters out low-frequency signals, retains high-frequency signals as the basis for analysis, and improves the accuracy of the analysis results. Secondly, an improved Teager energy operator is used to perform time-frequency analysis on the fault traveling wave signal components. By improving the anti-noise ability of the improved Teager energy operator, the influence of noise energy on the instantaneous energy of the signal is reduced, thereby achieving accurate calibration of the fault traveling wave signal.

[0144] Figure 3 FIG1 shows the internal structure of a computer device in one embodiment of the present invention. The computer device can be a terminal or a system. Figure 3 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0146] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to make the processor perform the steps of the above method embodiments. A person of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing relevant hardware. The program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0147] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0148] The above embodiments only express several implementation manners of the present application, and the description is specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A method for calibrating a fault traveling wave head, characterized in that: The method comprises: When a fault occurs in the distribution network, the fault traveling wave signal is collected; Decomposing the fault traveling wave signal based on a pole symmetric modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal; Using an improved Teager energy algorithm, a differential operation is performed on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence; calibrating the fault traveling wave head according to the extreme value points in the energy spectrum sequence; The improved Teager energy algorithm is used to perform differential operation on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence, which specifically includes: Acquiring a fundamental frequency and a sampling frequency of the fault traveling wave signal; Determining a sampling time parameter according to the fundamental frequency and the sampling frequency; improving the Teager energy algorithm according to the sampling time parameter, and performing a differential operation on the target modal component of the fault traveling wave signal according to the improved Teager energy algorithm to obtain energy operators corresponding to different sampling times in the target modal component; Obtaining an energy spectrum sequence according to energy operators corresponding to different sampling times in the target modal component; The step of determining the sampling time parameter according to the fundamental frequency and the sampling frequency specifically includes: Determining a value range of the sampling time parameter according to the fundamental frequency and the sampling frequency; Select any point in the value range as the sampling time parameter; The value range of the sampling time parameter is calculated by the following formula: Where f0 is the fundamental frequency, f m is the sampling frequency, i is the sampling time parameter; The energy operator corresponding to different sampling times in the target modal component is calculated by the following formula: Ψ[IMF1(t)]=IMF1(t)IMF1(t)-IMF1(t+i)IMF1(ti) Where IMF1(t) is the target modal component, t is the sampling time, and i is the sampling time parameter; The step of calibrating the fault traveling wave head according to the extreme points in the energy spectrum sequence specifically includes: Obtaining extreme points in the energy spectrum sequence; Connect two adjacent extreme value points to obtain several energy segments; Calculate the slopes of all energy line segments, compare the slopes of all energy line segments, and determine the target energy line segment corresponding to the maximum absolute value of the slope; The position of the fault traveling wave head is determined according to the sampling time corresponding to the target energy segment.

2. The method according to claim 1, wherein Decomposing the fault traveling wave signal based on the pole symmetry modal decomposition algorithm to obtain the target modal component of the fault traveling wave signal specifically includes: The extreme value point of the fault traveling wave signal is obtained for the Kth time, and two adjacent extreme value points of the fault traveling wave signal are connected to obtain a plurality of traveling wave segments, wherein the initial value of K is 1; Adding a boundary center point to the fault traveling wave signal using a linear interpolation method; generating a plurality of signal interpolation curves according to the center point of the traveling wave segment and the center point of the boundary; Calculating a curve mean of all the signal interpolation curves; When the curve mean does not satisfy the preset curve condition and K does not satisfy the preset first cycle condition, a first difference between the fault traveling wave signal and the curve mean is calculated, the first difference is used as the fault traveling wave signal, and K=K+1 is set, and the steps of obtaining the extreme value point of the fault traveling wave signal for the Kth time and connecting two adjacent extreme value points of the fault traveling wave signal to obtain a plurality of traveling wave segments are continued; When the curve mean satisfies a preset curve condition or K satisfies a preset first cycle condition, the fault traveling wave signal is decomposed to obtain a first layer of modal components, and the first layer of modal components is used as the target modal components.

3. The method according to claim 2, wherein The method further comprises: The first cycle condition is that K is greater than a first cycle threshold, and the first cycle threshold is any value in a preset cycle range; changing the value of the first cycle threshold multiple times, and obtaining a residual obtained by decomposing the fault traveling wave signal at each first cycle threshold; Calculating a relative standard deviation based on the fault traveling wave signal and the residual, determining a target relative standard deviation that meets a preset screening criterion based on the magnitudes of all relative standard deviations, and obtaining a target first cycle threshold value corresponding to the target relative standard deviation; The first layer modal component of the fault traveling wave signal obtained under the target first cycle threshold is used as the target modal component.

4. A calibration device for a fault traveling wave head, characterized in that: According to the method according to any one of claims 1 to 3, the device comprises: a signal acquisition module, a signal decomposition module and a fault location module; The signal acquisition module is used to collect fault traveling wave signals when a fault occurs in the distribution network; The signal decomposition module is used to decompose the fault traveling wave signal based on a pole symmetric modal decomposition algorithm to obtain a target modal component of the fault traveling wave signal; The fault location module is used to perform a differential operation on the target modal component of the fault traveling wave signal using an improved Teager energy algorithm to obtain an energy spectrum sequence; The position of the fault traveling wave head is determined according to the extreme value points in the energy spectrum sequence.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 3.

6. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 3.

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