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

Abstract

The embodiment of the invention discloses a fault traveling wave head calibration method and device, equipment and a storage medium, and the method comprises the steps: collecting a fault traveling wave signal when a power distribution network breaks down; decomposing the fault traveling wave signal based on a pole symmetry mode decomposition algorithm to obtain a target mode component of the fault traveling wave signal; performing differential 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 wave head of the fault traveling wave according to the extreme point in the energy spectrum sequence. On one hand, fault traveling wave signals are decomposed by using an extreme point mode decomposition method, low-frequency signals can be effectively screened and filtered out, and interference of noise on the signals is reduced; and on the other hand, the improved Teager energy operator is adopted to carry out time-frequency analysis on the fault traveling wave signal component, and the frequency domain characteristic of the signal is enhanced, so that the anti-noise capability of the algorithm is improved, and accurate calibration of the fault traveling wave signal is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and in particular to a calibration method and device, equipment and storage medium for a fault traveling wave head. Background Art

[0002] Accurate identification of distribution line faults is an important basic link in the reliable operation of power grids. As the scale of power grids continues to grow, rapid and accurate identification of distribution line faults plays an extremely critical role in improving fault handling effects, reducing troubleshooting costs, and quickly restoring power supply after faults.

[0003] Among the current distribution network fault location methods, the traveling wave location method has high requirements for positioning accuracy. The accuracy of fault location is closely related to the traveling wave head time. Regardless of the traveling wave location method, it is necessary to detect the time when the traveling wave head arrives at the detection position, so the calibration of the traveling wave head will affect the positioning accuracy. At present, there is no more accurate method to detect the arrival time of the traveling wave head. Summary of the invention

[0004] Based on this, it is necessary to propose a calibration method and device, equipment and storage medium for the fault traveling wave head to address the above problems in order to improve the accuracy of the calibration results.

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

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

[0007] 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;

[0008] 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;

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

[0010] Furthermore, the improved teager energy algorithm is used to perform a differential operation on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence, which specifically includes:

[0011] Acquire the base frequency and the sampling frequency of the fault traveling wave signal;

[0012] Determine a sampling time parameter according to the base frequency and the sampling frequency;

[0013] 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;

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

[0015] Further, the determining of the sampling time parameter according to the base frequency and the sampling frequency specifically includes:

[0016] Determining a value range of the sampling time parameter according to the base frequency and the sampling frequency;

[0017] Select any point in the value range 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] Where f0 is the fundamental frequency, f m is the sampling frequency, and i is the sampling time parameter.

[0021] Furthermore, 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(ti)

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

[0024] Furthermore, the step of calibrating the fault traveling wave head according to the extreme value points in the energy spectrum sequence specifically includes:

[0025] Obtaining extreme value points in the energy spectrum sequence;

[0026] Connect two adjacent extreme value points to obtain several energy line segments;

[0027] 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;

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

[0029] Furthermore, the pole-symmetric modal decomposition algorithm is used to decompose the fault traveling wave signal to obtain the target modal component of the fault traveling wave signal, which specifically includes:

[0030] 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 line segments, wherein the initial value of K is 1;

[0031] Adding a boundary center point to the fault traveling wave signal using a linear interpolation method;

[0032] Generating a plurality of signal interpolation curves according to the center point of the traveling wave segment and the center point of the boundary;

[0033] Calculating the curve mean of all the signal interpolation curves;

[0034] 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 line segments are continued;

[0035] 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.

[0036] Furthermore, it is characterized in that the method further comprises:

[0037] 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;

[0038] Changing the value of the first cycle threshold value multiple times, and obtaining a residual obtained by decomposing the fault traveling wave signal under the first cycle threshold value each time;

[0039] Calculating a relative standard deviation according to the fault traveling wave signal and the residual, determining a target relative standard deviation that meets a preset screening standard according to the magnitudes of all relative standard deviations, and obtaining a target first cycle threshold value corresponding to the target relative standard deviation;

[0040] 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.

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

[0042] The signal acquisition module is used to collect fault traveling wave signals when a fault occurs in the distribution network;

[0043] 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;

[0044] 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;

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

[0046] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0047] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer device, including 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 executes the steps of the method described in the first aspect.

[0048] The embodiments of the present invention have the following beneficial effects:

[0049] The embodiment of the present invention proposes a method for calibrating the head of a fault traveling wave, the method comprising: when a fault occurs in the distribution network, collecting the fault traveling wave signal; decomposing the fault traveling wave signal based on the extreme point symmetric modal decomposition algorithm to obtain the target modal component of the fault traveling wave signal; using the improved Teager energy algorithm to perform differential operations on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence; and determining the position of the fault traveling wave head according to the extreme point in the energy spectrum sequence. On the one hand, the extreme point modal decomposition method is used to decompose the fault traveling wave signal, 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 to enhance the frequency domain characteristics of the signal, thereby improving the anti-noise ability of the algorithm, so as to achieve accurate calibration of the fault traveling wave signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] in:

[0052] Figure 1 It is a flow chart of a method for calibrating a fault traveling wave head according to an embodiment of the present invention;

[0053] Figure 2 It is a structural block diagram of a calibration device for a fault traveling wave head according to an embodiment of the present invention;

[0054] Figure 3 1 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Among the current distribution network fault location methods, the fault traveling wave location method has high positioning accuracy and is basically not affected by transition resistance and neutral point grounding method, 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 location method is used, it is necessary to detect the time when the traveling wave head arrives at the detection position, so the calibration accuracy of the traveling wave head will affect the accuracy of fault location.

[0057] At present, the traveling wave head calibration method is usually based on the equal time-frequency analysis method of wavelet transform, which analyzes the signal by translating and scaling a wavelet function at different scales. However, since the wavelet transform method theoretically has a large number of wavelet basis functions and decomposition scales, it is difficult to have good adaptive ability; or based on the Hilbert-Huang transform, the empirical mode decomposition method and the Hilbert transform are combined to decompose the high-frequency components by relying on signal changes, but the empirical mode decomposition method has the problems of over-envelope and under-envelope in theory, and the decomposed modal components will have serious endpoint effects and aliasing phenomena, making it difficult to calibrate the traveling wave head signal, or the traditional Teager energy operator cannot ignore the noise energy when the signal-to-noise ratio is low, and the detection results have large deviations, affecting the accuracy of the results.

[0058] Based on this, this application uses the extreme symmetric modal decomposition algorithm (ESMD) to filter out the low-frequency signal components of the fault signal, and adopts a new teager energy operator (NTEO) operation to improve the algorithm's anti-noise ability on the basis of the original signal, and enhance the amplitude mutation characteristics of the traveling wave head, so as to accurately identify the traveling wave head and achieve accurate positioning of the fault point.

[0059] For details, please refer to Figure 1 , Figure 1 : is a flow chart of a method for calibrating a fault traveling wave head in an embodiment of the present invention, the method comprising:

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

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

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

[0063] Step 120 : 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.

[0064] Specifically, the maximum and minimum points in the fault traveling wave signal are found through the pole symmetric modal decomposition algorithm, and the signal is decomposed based on this to obtain multiple intrinsic mode functions of the fault traveling wave signal, and any of the intrinsic mode functions is used as the target modal component for subsequent analysis.

[0065] In one embodiment of the present invention, the first-layer modal component is selected as the target modal component. Since the first-level 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: Use the improved Teager energy algorithm to perform a differential operation on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence.

[0067] Specifically, the traditional teager energy operator (TEO) operation uses three sampling points adjacent to the signal to estimate the instantaneous energy of the signal at the current sample point, but its anti-noise ability is insufficient. Considering that the energy of the noise will affect the result of the energy operator operation to a certain extent, affecting the accuracy of the detection result, the embodiment of the present invention improves the traditional teager energy operator (TEO) operation. The improved teager energy algorithm can reduce the energy of the noise, effectively reduce the noise of the instantaneous energy of the signal at the current sample point, and improve the calculation accuracy of the signal energy.

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

[0069] Step 140: determine the position of the fault traveling wave head according to the extreme value points in the energy spectrum sequence.

[0070] In an embodiment of the present invention, the maximum points and the minimum points in the energy spectrum sequence are obtained. The maximum points and the minimum points reflect the characteristics of instantaneous energy mutation. The characteristic mutation points can be determined based on the maximum points and the minimum points in the energy spectrum sequence. The initial moment when the fault traveling wave head reaches the sampling point is determined according to the sampling time corresponding to the characteristic mutation point, and then the position of the fault traveling wave head is determined according to the preset traveling wave positioning algorithm.

[0071] The embodiment of the present invention utilizes 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, so as to achieve accurate calibration of the fault traveling wave signal.

[0072] In one embodiment of the present invention, step 120, 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:

[0073] Step 210, 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 line segments, wherein the initial value of K is 1.

[0074] Specifically, obtain the maximum and minimum points of the fault traveling wave signal M(t) M = {m1, m2, m3, ..., m n}. By connecting two adjacent extreme points, we can get (n-1) traveling wave segments, such as m1m2, m2m3, m3m4, etc.

[0075] Step 220, use linear interpolation method to add boundary center points to the fault traveling wave signal.

[0076] Since the length of the fault traveling wave signal is limited by the sampling time and has a starting point and an ending point, the embodiment of the present invention uses a 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 linear interpolation to obtain the boundary center point Z0; the center point between the ending point of the fault traveling wave signal and the next signal point is predicted by linear interpolation to obtain the boundary center point Z n .

[0078] Step 230, generate multiple signal interpolation curves according to the center point and boundary center point of the traveling wave segment.

[0079] In the embodiment of the present invention, the center point Z1, Z2, Z3, Z4, ..., Z n-1 Based on all the center points Z0, Z1, Z2, Z3, Z4, ..., Z n Generate several signal interpolation curves L1, L2, L3, L4, ..., L x .

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

[0081] Specifically, the average curve of all 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 does not meet the preset curve condition and K does not meet the preset first cycle condition, calculate the first difference between the fault traveling wave signal and the curve mean, take the first difference as the fault traveling wave signal, and let K=K+1, and continue to execute the Kth time to obtain the extreme point of the fault traveling wave signal, and connect the two adjacent extreme points of the fault traveling wave signal to obtain a number of traveling wave segments.

[0084] Specifically, the curve mean L is compared with the preset curve condition. If the curve mean does not meet the curve condition, the first difference [M(t)-L] between the fault traveling wave signal M(t) and the average curve L is calculated, and the first difference is used as the fault traveling wave signal for update. Step 210-Step 240 is repeatedly executed until the curve mean meets the curve condition, or the number of cycles K reaches the preset first cycle condition.

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

[0086]

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

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

[0089] Step 260: When the curve mean satisfies the preset curve condition or K satisfies 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 used as the target modal component.

[0090] In one embodiment of the present invention, when the curve mean is less than or equal to the curve threshold, or the number of cycles K reaches the maximum number of cycles, the fault traveling wave signal is decomposed to obtain the first layer of modal components.

[0091] Since the modal components of the first level usually represent the high-frequency components in the traveling wave signal, which often contain the main features or key information of the signal, the modal components of the first level are taken as the target modal components.

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

[0093] Step 270, changing the value of the first cycle threshold multiple times, and obtaining the residual obtained by decomposing the fault traveling wave signal under the first cycle threshold each time.

[0094] Specifically, the steps of Step 210 to Step 260 are executed for the i-th time (the initial value of i is 1) to obtain the i-th layer modal component IMF i and the residual amount R i , according to the fault traveling wave signal M(t) and the i-th layer modal component IMF i Take the difference and get the second difference [M(t)-IMF i], and update the second difference as the fault traveling wave signal, set i=i+1 and continue to execute the steps of Step210-Step260 for the i-th time until the residual R obtained by the last decomposition is N The number of extreme points in meets the preset threshold.

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

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

[0097]

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

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

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

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

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

[0103] In one embodiment of the present invention, step 130, using an improved teager energy algorithm to perform a differential operation on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence, specifically includes:

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

[0105] Step 320, determine the sampling time parameters according to the base frequency and the sampling frequency.

[0106] Step 330, improve the teager energy algorithm according to the sampling time parameter, and perform differential 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, obtain the energy spectrum sequence according to the energy operator corresponding to different sampling times in the target modal component.

[0108] In the embodiment of the present invention, the improved teager energy algorithm introduces a sampling time parameter i to reduce noise energy, and calculates the energy of the current sampling point by selecting three points that are i sampling points in front and behind.

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

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

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

[0112] Specifically, the form of 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] From this we can know: 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 roughly understood that the equation holds true, that is, when the sampling frequency is greater than 8 times the fundamental frequency of the signal, there is a simple method for measuring signal energy, which is expressed as:

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

[0118] Among them, ψ[M(t)] is the sampling signal energy, and M(t) is the 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 can be obtained. The sampling time parameter can be any value in the value range. By constraining the sampling time parameter i, the value of the sampling time parameter i can be selected based on actual needs, so that the energy operator result calculated based on the sampling time parameter i is more accurate and effectively reduces the influence of noise.

[0122] In the embodiment of the present invention, 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(ti)

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

[0125] In one embodiment of the invention, step 140, determining the position of the fault traveling wave head according to the extreme point in the energy spectrum sequence, specifically includes:

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

[0127] Specifically, each maximum point and minimum point of the energy spectrum sequence X(t) within the sampling time is obtained.

[0128] Step 420, connect two adjacent extreme points to obtain several energy segments.

[0129] Specifically, two adjacent extreme value points are connected in sequence to obtain a number of energy segments.

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

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

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

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

[0134] Wave head characteristics include: the arrival time of the traveling wave head at the sampling device, signal amplitude and frequency, etc.

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

[0136]

[0137] Where V′(t) is the signal amplitude, f′(t) is the signal frequency;

[0138] In the embodiment of the present invention, the time when the traveling wave head reaches the measuring device is calibrated by detecting the peak value of the instantaneous energy, and the position of the faulty traveling wave head is determined based on the arrival time and a preset positioning formula. The preset positioning formula can be a method of calculating the position of the faulty traveling wave head based on parameters such as the arrival time and the traveling wave speed.

[0139] The present invention also proposes a calibration device for the fault traveling wave head, which can be referred to Figure 2 , Figure 2 2 is a structural block diagram of a calibration device for a fault traveling wave head in an embodiment of the present invention. The device includes: a signal acquisition module 201 , a signal decomposition module 202 and a fault location module 203 .

[0140] The signal acquisition module 201 is used to collect fault traveling wave signals when a fault occurs in the distribution network.

[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 location module 203 is used to perform differential operation on the target modal component of the fault traveling wave signal using the improved Teager energy algorithm to obtain an energy spectrum sequence and determine the position of the fault traveling wave head according to the extreme value point in the energy spectrum sequence.

[0143] The calibration device for the fault traveling wave head proposed in the embodiment of the present invention 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 to perform time-frequency analysis on the fault traveling wave signal component. 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, so as to realize the accurate calibration of the fault traveling wave signal.

[0144] Figure 3 FIG. 1 shows an internal structure diagram of a computer device in one embodiment of the present invention. The computer device may 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 may implement each step 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 may implement each step in the above method embodiment. Those skilled in the art will understand 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0145] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and 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 proposed, which stores a computer program. When the computer program is executed by a processor, the processor executes each step in the above method embodiment. A person of ordinary skill in the art can understand that all or part of the processes in the above embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium, and the program can include the processes of the embodiments of the above methods when executed. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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 may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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; The fault traveling wave head is calibrated according to the extreme value points in the energy spectrum sequence.

2. The method according to claim 1, characterized in that The improved teager energy algorithm is used to perform a differential operation on the target modal component of the fault traveling wave signal to obtain an energy spectrum sequence, which specifically includes: Acquire the base frequency and the sampling frequency of the fault traveling wave signal; Determine a sampling time parameter according to the base 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; An energy spectrum sequence is obtained according to energy operators corresponding to different sampling times in the target modal component.

3. The method according to claim 2, characterized in that Determining the sampling time parameter according to the base frequency and the sampling frequency specifically includes: Determining a value range of the sampling time parameter according to the base frequency and the sampling frequency; Select any point in the value range as the sampling time parameter; The value range of the sampling time is calculated by the following formula: f0 / f m <1 / 8i Where f0 is the fundamental frequency, f m is the sampling frequency, and i is the sampling time parameter.

4. The method according to claim 2, characterized in that 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.

5. The method according to claim 1, characterized in that The step of calibrating the fault traveling wave head according to the extreme value points in the energy spectrum sequence specifically includes: Obtaining extreme value points in the energy spectrum sequence; Connect two adjacent extreme value points to obtain several energy line 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 line segment.

6. The method according to claim 1, characterized in that 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 line 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 the 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 line 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.

7. The method according to claim 6, characterized in that 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 value multiple times, and obtaining a residual obtained by decomposing the fault traveling wave signal under the first cycle threshold value each time; Calculating a relative standard deviation according to the fault traveling wave signal and the residual, determining a target relative standard deviation that meets a preset screening standard according to 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.

8. A calibration device for a fault traveling wave head, characterized in that: 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.

9. 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 7.

10. 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 7.

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