Double-end traveling wave distance measurement method and device suitable for power distribution network and storage medium

Abstract

The invention relates to a double-end traveling wave distance measurement method and device suitable for a power distribution network and a storage medium, and the method comprises the steps: firstly, carrying out the preprocessing of a fault phase current traveling wave through the normalization and rough characteristic frequency band zero-phase band-pass filtering of a double-end current traveling wave; using spline wavelet transform to calibrate a proper modulus maximum point as initial estimation of a wave head starting point; further estimating a wave head on the double-end time domain original traveling wave waveform by using a threshold method, and obtaining an accurate wave head starting point through double-end amplitude interpolation correction; and finally, calculating the position of a fault point according to a double-end traveling wave distance measurement principle. According to the method, a fault phase current waveform is preprocessed into a current traveling wave in a rough characteristic frequency band through a zero-phase filtering means, interference outside the characteristic frequency band is eliminated, a wave head break point serves as a wave head starting point, the break point is estimated step by step through a wavelet modulus maximum value and a signal amplitude threshold value method, and the fault phase current waveform is obtained. And more accurate approximation of the break point is obtained through double-end amplitude interpolation, so that the range finding precision exceeding the sampling rate limit is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a double-terminal traveling wave ranging method, device and storage medium applicable to a distribution network. Background Art

[0002] The fault traveling wave ranging technology of the distribution network can directly and accurately locate the fault point through the ranging system, which can ensure the accurate operation of the relay protection device, accurately locate the fault location, save manpower and material resources consumed by line inspection, and improve fault efficiency and time. Research on fault ranging methods suitable for distribution networks is of great significance to improving the power supply reliability of distribution networks and reducing power outage losses.

[0003] The research on traveling wave ranging started early. The ranging technology and devices were first applied in transmission lines. They have high measurement accuracy, rapid response, and are not affected by factors such as line structure asymmetry and transformer transmission error. At present, they have been relatively maturely applied in transmission networks. Compared with high-voltage lines, the working conditions faced by the traveling wave ranging application in distribution networks are more complex. The wavelet modulus maximum method, which is mature and widely used in high-voltage lines, cannot be directly used in distribution networks, and further optimization processing methods are needed.

[0004] In the distribution network, there will be a large amount of random noise in the working environment of the on-site traveling wave ranging device. Factors such as the traveling wave propagation path and length, the size of the transition resistance and the phase angle of the fault voltage, and the different attenuation and distortion degrees of the double-ended wave head directly affect the slope characteristics of the rising or falling edge of the wave head. High-resistance grounding will make the rising or falling edge of the wave head smoother. The arrival point of the traveling wave head is at a certain distance from the appropriate wavelet modulus maximum point. Under the limitation of limited sampling rate, it is still challenging to obtain better ranging accuracy.

[0005] In the present invention, based on the research and test practice of wavelet transform modulus extreme value detection theory and spline interpolation estimation, the essential process of detecting the wave head starting point is analyzed, and a high-precision two-terminal traveling wave ranging method that adapts to the actual characteristics and development requirements of the distribution network is proposed. Summary of the invention

[0006] The present invention proposes a double-terminal traveling wave ranging method, device and storage medium applicable to a distribution network, which can solve at least one of the technical problems in the background technology.

[0007] To achieve the above object, the present invention adopts the following technical solutions: A double-terminal traveling wave ranging method applicable to a distribution network comprises the following steps: Step 1: Acquisition of current traveling wave and fault judgment: The three-phase fault current traveling waves of 1000 points, 200 points before and 800 points after the fault triggering, are collected at the M and N ends of the distribution network line through current transformers: Ia, Ib, Ic, where the sampling rate is 1Mhz, the sampling time is 1 millisecond, and the hardware sampling is equipped with a high-pass filter circuit to remove the power frequency and related harmonic signal components below 1khz; and the fault phase is screened out through the power frequency judgment information or the amplitude of the three-phase traveling wave waveform; Step 2: Zero-phase filtering of the fault phase current traveling wave: Directly perform zero-phase bandpass filtering on the fault phase current traveling wave, and the filtering results are used as the fault vectors to be processed, which are recorded as: IM, IN; Step 3: Wavelet decomposition: The bandpass filter data IM and IN are decomposed into high-frequency and low-frequency components of set scales through wavelet transform, and the components are converted into modulus matrices mod_M and mod_N, where each row of the matrix corresponds to a scale; Step 4: Modulus maximum screening: Based on the double-end modulus components, the modulus extreme points that meet the requirements are selected as the preliminary estimation of the wave head starting point, which are (p_M, v_M) and (p_N, v_N). Step 5: Threshold estimation of the starting point of the wave head of the original traveling wave signal: The traveling wave components of the double-terminal fault phase are normalized based on their respective maximum amplitudes and recorded as I_1M and I_1N. Based on the preliminary estimation of the wave head starting point, the wave head starting points (uppoint, upvalue) and (downpoint, downvalue) are obtained through the constructed threshold screening. Step 6: Based on the respective wave head starting points (uppoint, upvalue) and the corresponding amplitude point pairs (downpoint, downvalue), obtain more accurate wave head starting points: M_point and N_point through wave head amplitude interpolation calibration; Step 7: Fault distance calculation: Based on the difference between the starting points of the traveling wave waveform at both ends of the fault The time difference between the fault traveling wave head reaching both ends and the waveform triggering time at both ends is obtained , and then obtain the distance from the fault point to the M end and the N end:

[0008]

[0009] Wherein, L is the line distance between the M end and the N end, and v is the speed of the traveling wave.

[0010] Furthermore, for the fault phase judgment based on the traveling wave in step 1, the single-phase grounding fault is judged by the maximum amplitude of the traveling wave, and the amplitude of the fault phase is obviously larger; for two-phase short circuit or grounding, three-phase short circuit or grounding, the fault at the M end is judged first, and the N end follows the judgment result of the M end to keep the judgment result of the double-end fault phase consistent. Furthermore, the specific implementation steps of the zero-phase filtering used in step 2 are as follows: (1) Exclude the low-frequency band composed of power frequency harmonics and the over-high frequency band of system noise, select the middle frequency band window (10 kHz-300 kHz) as the characteristic frequency band, configure the corresponding filtering parameters according to the sampling rate and linear phase filtering requirements, and obtain the finite impulse response (FIR) filter coefficients; (2) Based on the filter coefficient, the fault phase current traveling wave sampling data at both ends is processed by zero-phase bandpass filtering, that is, based on the filter coefficient, the data is forward filtered, the result is reversed, and then filtered and the result is reversed to obtain unbiased filtered signals IM and IN. Furthermore, in the wavelet decomposition process of step 3, S(n) (representing: IM or IN) adopts the following discrete binary spline wavelet transform:

[0011]

[0012] Here n represents the sequence number, is the approximate coefficient vector of the jth scale, is the wavelet coefficient vector of the jth scale, is the convolution symbol, and The filter bank vector derived from the spline wavelet (for simplicity, the vectors h and g can be taken as [-3 / 2,3 / 2] and [1 / 8,3 / 8,3 / 8,1 / 8]), and They are respectively the high-pass and low-pass filter group vectors at the j-1 scale, and are and Gradually insert zero to form, is the compensation factor, which is implemented based on the à Trous frame stationary spline wavelet transform. The specific signal space decomposition is expressed by the following schematic formula:

[0013] S

[0014]

[0015]

[0016] In the above schematic formula, S(n) can be equivalently regarded as the coefficient representation in the whole space basis. In the subsequent equivalent relationship, the whole space is gradually decomposed to form wavelet subspaces of different scales. and It can be understood as the coefficient representation of the corresponding subspace basis, where represents the direct sum of independent subspaces, represents the equivalence relation between different subspaces and, （ , ） Represents a combination of paired high- and low-frequency coefficients in a subspace at a certain scale.

[0017] The modulus of the j-scale wavelet decomposition component is defined as follows:

[0018] in, Represents a modulus extreme value in the modulus of the j-scale wavelet decomposition component, is the maximum absolute value of the wavelet component at the jth scale. The specific implementation of the modulus can obtain the extreme points and extreme values ​​through the first and second order differences of the wavelet components, and the amplitude of the non-extreme points can be directly set to zero.

[0019] Furthermore, for the module extreme value screening process in step 4, the specific screening steps are as follows: (41) Calculate the scale corresponding to the target frequency band: Based on the sampling frequency fs and the median value of the target frequency band Target_fs, the scale corresponding to the target frequency band is obtained by the formula r = floor(1+((log(fs / 2 / Target_fs) / log(2.0)))). The subsequent processing object is the modulus vector of the high-frequency component coefficient of each scale ; (42) Start the search from the high-frequency component of the Rth scale, R = r + 1, and is the threshold, Rat is the proportional coefficient, max_R is the maximum absolute value of the high-frequency component amplitude of this scale, and the first modulus extreme value and modulus extreme value point that crosses the threshold are sequentially searched as the modulus extreme value point of this scale, recorded as MaxModPoint_R; (43) In the upward scale R-1, based on the point MaxModPoint_R within the range of plus or minus 2, sequentially search for the point that crosses the threshold The first modulus extreme value and corresponding point with the same sign as MaxModPoint_R are recorded as the modulus extreme value point of this scale, MaxModPoint_(R-1). If the search is empty, record MaxModPoint_(R-1)=MaxModPoint_R; (44) Then enter the next higher scale and repeat the search mechanism in steps (42) and (43). Stop entering the higher scale at the second scale. MaxModPoint_2 is the modulus extreme point at the second scale search. The overall modulus extreme point is recorded as MaxModPoint = MaxModPoint_2.

[0020] Further, for the threshold sticking point in step 5, the following implementation steps are as follows: (51) For the normalized waveforms I_1M and I_1N, construct the threshold Thd: The threshold consists of two parts: ， where average is the mean value of the wavefront beforepoint = min(modmaxPoint / 2, hardStarPoint - 50) segment with a bandwidth of width, noise is the noise amplitude of the wavefront point beforepoint segment obtained through segment statistics, and polarity is the polarity of the modulus extreme point; (52) Based on one - end threshold Thd1, within the point range of (beforePoint1, modmaxPoint1), compare its amplitude point - by - point in reverse order and obtain the first point that crosses the threshold, denoted as the wavefront starting point (uppoint, upvalue); (53) For the other end, construct the threshold Thd2 and repeat the above process to obtain the wavefront starting point (downpoint, downvalue) of the fault traveling wave at this end.

[0021] Further, for the amplitude interpolation calibration in step 6, since the amplitudes and polarities of the wavefronts at both ends are obviously opposite, that is ,fabs( ) The symbol is an absolute - value operation. The specific implementation steps are as follows: If fabs(upvalue) >= fabs(downvalue), for the upstream (i.e., M - end) wavefront, the following estimation is used, M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue); If fabs(upvalue) < fabs(downvalue), for the downstream (i.e., N - end) wavefront, the following estimation is used, N_point = downpoint + (-upvalue - downvalue) / (DownWave[downpoint + 1] - downvalue).

[0022] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0023] On the other hand, the present invention further discloses a computer device, 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 the steps of the above method.

[0024] It can be seen from the above technical scheme that the present invention provides a two-terminal fault traveling wave ranging method suitable for distribution network. First, for the fault phase current traveling wave, the two-terminal current traveling wave is obtained by normalization and rough characteristic frequency band (10khz-300khz) zero-phase bandpass filtering, and the spline wavelet transform is used to calibrate the appropriate modulus maximum point as the preliminary estimate of the wave head starting point; then the threshold method is used on the two-terminal current waveform to further estimate the wave head starting point, and the accurate wave head starting point is obtained by two-terminal amplitude interpolation correction; finally, the fault point position is calculated according to the two-terminal traveling wave ranging principle. The present invention pre-processes the fault phase current waveform into a current traveling wave within a rough characteristic frequency band by filtering means, eliminates noise interference outside the characteristic frequency band, takes the wave head inflection point as the wave head starting point, gradually estimates and approaches the inflection point by wavelet modulus maximum method and amplitude threshold method, and then obtains the approximation of the inflection point beyond the sampling rate limit by two-terminal amplitude interpolation.

[0025] Compared with the prior art, the technical advantages of the present invention are as follows: The present invention uses the new calibration principle that we have proposed to calibrate the wave head starting point with the wave head waveform inflection point that is not easily disturbed, and adopts a new step-by-step estimation approximation calibration method on the original signal in the time domain. It can gradually approximate the real wave head under the condition of wave head distortion and gentle steepness, and overcomes the problem of being unable to identify or having large errors when the wave head singularity is reduced. It is applicable to the traveling wave distance measurement of distribution network faults. At the same time, the present invention can make the wave head wavelet modulus maximum more prominent and easy to screen through the preprocessing of wave head amplitude normalization and zero-phase bandpass filtering, and remove the noise interference in irrelevant frequency bands; Based on the result of approximation calibration, the present invention obtains a better approximation of the wave head by interpolating the amplitude near the normalized wave head mark point. Under a certain hardware sampling rate, this technical processing can further improve the calibration effect of the wave head starting point and achieve a ranging accuracy that exceeds the sampling rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The present invention is a flow chart of the calculation method of traveling wave ranging disclosed in the present invention.

[0027] Figure 2 Topological diagram for implementing the traveling wave ranging experiment test in the distribution network.

[0028] Figure 3 This is the zero-phase bandpass filter characteristic diagram.

[0029] Figure 4 This is a comparison diagram of the fault traveling wave signal before and after filtering.

[0030] Figure 5 It is the high frequency component of the fault traveling wave signal at the M end and the corresponding modulus diagram.

[0031] Figure 6 It is the high frequency component and corresponding modulus diagram of the N-end fault traveling wave signal.

[0032] Figure 7 This is the final wave head calibration diagram of the double ends. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0034] like Figure 1 As shown, the double-terminal traveling wave ranging method applicable to the distribution network described in this embodiment includes the following steps: Step 1: Acquisition of current traveling waves and judgment of fault phase: The three-phase fault current traveling waves of 1000 points, 200 points before and 800 points after the fault triggering, are collected by current transformers at the M and N ends of the distribution network line: Ia, Ib, Ic, where the sampling rate is 1Mhz, the sampling time is 1 millisecond, and the hardware sampling is equipped with a high-pass filter circuit to remove the power frequency and related harmonic signal components below 1khz; and the fault phase is screened out by judging the power frequency information or the amplitude of the three-phase traveling wave waveform; Step 2: Zero-phase filtering of the fault phase current traveling wave: directly perform zero-phase bandpass filtering on the fault phase current traveling wave, and the filtering results are used as the fault vectors to be processed, which are recorded as: IM, IN respectively; Step 3: Wavelet decomposition: Decompose the bandpass filter data IM and IN into high-frequency and low-frequency components of a certain scale through wavelet transform, and convert the components into modulus matrices mod_M and mod_N, where each row of the matrix corresponds to a scale; Step 4: Modulus maximum screening: Based on the double-end modulus components, select appropriate modulus extreme points as preliminary estimates of the wave head starting point, which are (p_M, v_M) and (p_N, v_N). Step 5: Threshold estimation of the wave head starting point of the original traveling wave signal: The traveling wave components of the double-end fault phase are normalized based on their respective maximum amplitudes, recorded as I_1M and I_1N; based on the preliminary estimation of the wave head starting point, the wave head starting points (uppoint, upvalue) and (downpoint, downvalue) are obtained through the constructed threshold screening; Step 6: Based on the respective wave head starting points and corresponding amplitude point pairs ((uppoint, upvalue), (downpoint, downvalue)), obtain more accurate wave head starting points: M_point and N_point through wave head amplitude interpolation calibration; Step 7: Fault distance calculation: Based on the difference between the starting points of the traveling wave waveform at both ends of the fault The time difference between the fault traveling wave head reaching both ends and the waveform triggering time at both ends is obtained , and then obtain the distance from the fault point to the M end and the N end:

[0035]

[0036] Wherein, L is the line distance between the M end and the N end, and v is the speed of the traveling wave.

[0037] Specifically, for the fault phase judgment based on the traveling wave in step 1, the single-phase grounding fault can be judged by the maximum amplitude of the traveling wave, and the amplitude of the fault phase is obviously larger; for two-phase short circuit or grounding, three-phase short circuit or grounding, the fault at the M end is judged first, and the N end follows the judgment result of the M end to keep the judgment result of the double-end fault phase consistent.

[0038] The specific implementation steps of the zero-phase filtering used in step 2 are as follows: (1) Exclude the low-frequency band composed of power frequency harmonics and the over-high frequency band of system noise, select the middle frequency band window (10 kHz-300 kHz) as the characteristic frequency band, configure the corresponding filtering parameters according to the sampling rate and linear phase filtering requirements, and obtain the finite impulse response (FIR) filter coefficients; (2) Based on the filter coefficient, the fault phase current traveling wave sampling data at both ends is processed by zero-phase bandpass filtering, that is, based on the filter coefficient, the data is forward filtered, the result is reversed, and then filtered and the result is reversed to obtain unbiased filtered signals IM and IN.

[0039] In the wavelet decomposition process of step 3, S(n) (representing: IM or IN) adopts the following discrete binary spline wavelet transform:

[0040]

[0041] Here, n represents the sequence number, is the approximate coefficient vector of the jth scale, is the wavelet coefficient vector of the jth scale, is the convolution symbol, and The filter bank vector derived from the spline wavelet (for simplicity, the vectors h and g can be taken as [-3 / 2,3 / 2] and [1 / 8,3 / 8,3 / 8,1 / 8]), and They are respectively the high-pass and low-pass filter group vectors at the j-1 scale, and are and Gradually insert zero to form, is the compensation factor (compensating for the error caused by scale discreteness to make the coefficient more accurate); the specific implementation is completed by the stationary spline wavelet transform based on the à Trous framework, and the specific signal space decomposition is expressed by the following schematic formula:

[0042] S

[0043]

[0044]

[0045] In the above schematic formula, S(n) can be equivalently regarded as the coefficient representation in the whole space basis. In the subsequent equivalent relationship, the whole space is gradually decomposed to form wavelet subspaces of different scales. and It can be understood as the coefficient representation of the corresponding subspace basis, where represents the direct sum of independent subspaces, represents the equivalence relation between different subspaces and, （ , ） Represents a combination of paired high- and low-frequency coefficients in a subspace at a certain scale.

[0046] The modulus of the j-scale wavelet decomposition component is defined as follows:

[0047] in, Represents a modulus extreme value in the modulus of the j-scale wavelet decomposition component, is the maximum absolute value of the wavelet component at the jth scale. The specific implementation of the modulus can obtain the extreme points and extreme values ​​through the first and second order differences of the wavelet components, and the amplitude of the non-extreme points can be directly set to zero.

[0048] For the mode extreme value screening process in step 4, since the low-scale wavelet mode extreme value is easily affected by high-frequency noise, and the high-scale wavelet mode extreme value corresponds to a low frequency band and cannot reflect the high-frequency characteristics of transient traveling waves, the screening scheme is selected from a suitable high scale, and the screening is carried out in reverse order, with the cutoff scale being j=2. Of course, this scheme can also be set to other scales (for example, j=1, 3) according to actual conditions, or a specific scale can be selected separately. The specific screening steps are as follows: (1) Calculate and obtain the scale corresponding to the target frequency band: Based on the sampling frequency fs and the median value of the target frequency band Target_fs, the scale corresponding to the target frequency band is obtained by the formula r = floor(1+((log(fs / 2 / Target_fs) / log(2.0)))). The subsequent processing object is the modulus vector of the high-frequency component coefficient of each scale. ; (2) Start the search from the Rth scale high frequency component (R=r+1), is the threshold (where the proportional coefficient Rat=0.35, max_R is the maximum absolute value of the high-frequency component amplitude of this scale), and the first modulus extreme value and modulus extreme value point that crosses the threshold are sequentially searched as the modulus extreme value point of this scale, recorded as MaxModPoint_R; (3) In the upward scale (R-1), based on the point MaxModPoint_R within the range of plus or minus 2, sequentially search for the point that crosses the threshold （ ） The first modulus extreme value and corresponding point with the same sign as MaxModPoint_R are recorded as the modulus extreme value point of this scale, MaxModPoint_(R-1). If the search is empty, record MaxModPoint_(R-1)=MaxModPoint_R; Then enter the next higher scale and repeat the search mechanism of steps (2) and (3) until the second scale stops and enters the next higher scale. MaxModPoint_2 is the modulus extreme point at the search point of the second scale. The overall modulus extreme point is recorded as MaxModPoint=MaxModPoint_2.

[0049] For the threshold card point in step 5, there are the following implementation steps: (1) For the normalized waveforms I_1M and I_1N, construct the threshold Thd: The threshold consists of two parts: , where average is the mean value of the wavefront segment (with a bandwidth of width) at (beforepoint = min(modmaxPoint / 2, hardStarPoint - 50)), noise is the noise amplitude of the wavefront point (beforepoint) segment, obtained through segment statistics, and polarity is the polarity of the modulus extreme point; (2) Based on one - end threshold Thd1, within the range of points (beforePoint1, modmaxPoint1), compare the amplitudes point - by - point in reverse order, and obtain the first point that crosses the threshold, denoted as the wave - head starting point (uppoint, upvalue); (3) For the other end, construct threshold Thd2 and repeat the above process to obtain the wave - head starting point (downpoint, downvalue) of the fault traveling wave at this end.

[0050] For the amplitude interpolation calibration in step 6, since the amplitudes and polarities of the wave - heads at both ends are obviously opposite, that is ,fabs( ) The symbol is an absolute - value operation, and the specific implementation steps are as follows: If fabs(upvalue) >= fabs(downvalue), the upstream (M - end) wave - head is estimated as follows, M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue).

[0051] If fabs(upvalue) < fabs(downvalue), the downstream (N - end) wave - head is estimated as follows, N_point = downpoint + (-upvalue - downvalue) / (DownWave[downpoint + 1] - downvalue).

[0052] The following is an example for illustration: The technical solution of the present invention will be further introduced in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0053] As Figure 1 shown is the overall process framework of this embodiment. The test topology structure consists of Figure 2As shown, the test line is a line from node 1 to DTU_82 with a length of 4 km. A two-terminal traveling wave signal for determining the fault distance is generated by a traveling wave signal generator, appropriate white noise is added, and then the three-phase fault traveling wave waveform data in the two-terminal comtrade standard format is obtained through the hardware filtering circuit and the mutual inductor acquisition channel, and is transmitted to the master station for processing; the method includes the following steps: Step (1): The sampling rate of the hardware circuit is 1 MHz, the interval between two sampling points is 1 microsecond, the sampling time is 1 millisecond, the number of sampling points is 1000, and the three-phase currents at both ends are collected: Ia, Ib, Ic.

[0054] Step (2): The current traveling wave of the fault phase is selected by the mutation amplitude of the double-terminal three-phase fault current, and the band-pass filter is used as the processing object. The band-pass filter characteristics and waveform data are as follows: Figure 3 and 4 shown.

[0055] Step 3: Perform cubic spline wavelet transform on the filtered double-terminal fault phase traveling wave, perform modulus maximum screening on the coefficient vectors of high- and low-frequency components, and obtain a preliminary estimate of (201 / 201). Further punctuate the double-terminal thresholds to obtain the calibration result of the starting point of the double-terminal traveling wave head (199 / 199). After the double-terminal amplitude interpolation calibration, the interpolation formula is M_point=uppoint+(-downvalue-upvalue) / (UpWave[uppoint+1]-upvalue ) Substituting the parameters, we get M_point=199+(-0.030797-(-0.040609)) / (-0.09829-(-0.040609))=198.829992 The final calibration of the starting point of the double-ended traveling wave head (198.83 / 199); the high-frequency component and modulus waveform of the double-ended wavelet decomposition are as follows Figure 5 , 6 The final calibration is shown in Figure 7 shown.

[0056] Step 4: Set the fault wave velocity to V=2.93e8 m / s based on the line material, calculate the fault distance to be L_M=2998.505027m, L_N=1001.494973m, and the error is 1.49m.

[0057] To summarize the above steps, in the modulus maximum screening and smoothing threshold screening, the modulus maximum point and the threshold card point are not completely consistent. The threshold card point is closer to the inflection point of the wave head. Further amplitude interpolation can obtain a wave head calibration that exceeds the sampling accuracy and obtain a correspondingly better ranging accuracy.

[0058] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0059] On the other hand, the present invention further discloses a computer device, 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 the steps of the above method.

[0060] In another embodiment provided in the present application, a computer program product including instructions is also provided. When the computer program product is executed on a computer, the computer executes any one of the two-terminal traveling wave ranging methods applicable to a distribution network in the above embodiments.

[0061] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0062] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0064] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A double-terminal traveling wave ranging method applicable to a distribution network, characterized in that: The following steps are included: S1. Obtain a three-phase fault current traveling wave sampling sequence of 1000 points, including 200 points before and 800 points after the arrival of the fault traveling wave at both ends of the line M and N, through a mutual inductor. The sampling rate is 1Mhz. The sampling filter circuit has removed the power frequency and harmonics below 1khz, and the fault phase is determined through the traveling wave information. S2, perform zero-phase bandpass filtering on the fault phase current traveling wave sampling sequence, and the filtering results are IM and IN respectively; S3, decompose IM and IN into multi-scale high and low frequency components through wavelet transform and form the modulus matrices at both ends of M and N respectively; S4. Based on the modulus matrix at both ends of M and N, select the modulus extreme value points that meet the requirements as the preliminary estimation of the wave head starting points at both ends of M and N; S5, normalize IM and IN, make a preliminary estimate based on the starting point of the wave head at both ends of M and N, and further estimate the starting point of the wave head at both ends of M and N by constructing a threshold screening; S6, based on the further estimation of the wave head starting point at both ends of M and N, obtain a more accurate estimation of the wave head starting point at both ends of M and N through amplitude interpolation calibration; S7, the time difference of the fault wave head arriving at both ends is obtained by the difference between the estimated starting points of the more accurate wave head at both ends of M and N and the triggering time of the waveform at both ends , the distances from the fault point to the M and N terminals are obtained by the following formula: Where, L is the line distance between the M and N terminals, and v is the speed of the fault traveling wave.

2. The double-terminal traveling wave ranging method applicable to a distribution network according to claim 1, characterized in that: Determining the fault phase by using traveling wave information in S1 specifically includes: For single-phase grounding fault, the maximum amplitude of the traveling wave is used to judge, and the phase with a significantly larger amplitude is the fault phase; For two-phase short circuit or grounding, three-phase short circuit or grounding, the fault at the M end is judged first, and the N end follows the judgment result of the M end to keep the judgment results of the fault phases at both ends consistent.

3. The double-terminal traveling wave ranging method applicable to a distribution network according to claim 1, characterized in that: In S2, zero-phase bandpass filtering is performed on the fault phase current traveling wave sampling sequence. The specific steps are as follows: S21, exclude the low frequency band composed of power frequency harmonics and the over-high frequency band of system noise, select the intermediate frequency band window 10khz-300khz as the characteristic frequency band, configure the corresponding filtering parameters in combination with the sampling rate and linear phase filtering requirements, and obtain the finite impulse response FIR filter coefficients; S22. Based on the FIR filter coefficients, the fault phase current traveling wave sampling data at both ends are subjected to zero-phase bandpass filtering. That is, based on the FIR filter coefficients, the data is forward filtered, the results are reversed, and then filtered and the results are reversed to obtain unbiased filtered signals IM and IN.

4. The double-terminal traveling wave ranging method applicable to a distribution network according to claim 3, characterized in that: In the wavelet decomposition process of S3, S(n) represents: IM or IN, and the following discrete binary spline wavelet transform is used: n represents the serial number, is the approximate coefficient vector of the jth scale, is the wavelet coefficient vector of the jth scale, is the convolution symbol, and The filter bank vector derived from the spline wavelet, and They are respectively the high-pass and low-pass filter group vectors at the j-1 scale, and are and Gradually insert zeros to generate is the compensation factor, which is implemented based on the stationary spline wavelet transform of the à Trous framework. The specific signal space decomposition is expressed by the following schematic formula: S In the above schematic formula, S(n) can be equivalently regarded as the coefficient representation of the whole space basis. In the subsequent equivalent relationship, the whole space is gradually decomposed to form wavelet subspaces of different scales. and is the coefficient representation of the corresponding subspace basis, where represents the direct sum of independent subspaces, represents the equivalence relation between different subspaces and, （ , ） Represents a combination of paired high- and low-frequency coefficients in a subspace at a certain scale; The modulus of the j-scale wavelet decomposition component is defined as follows: in, Represents a modulus extreme value in the modulus of the j-scale wavelet decomposition component, is the maximum absolute value of the wavelet component at the jth scale.

5. The double-terminal traveling wave ranging method applicable to a distribution network according to claim 1, characterized in that: For screening the module extreme value points that meet the requirements in S4, the specific screening steps are as follows: (41) Calculate the scale corresponding to the target frequency band: Based on the sampling frequency fs and the median value of the target frequency band Target_fs, the scale corresponding to the target frequency band is obtained by the formula r = floor(1+((log(fs / 2 / Target_fs) / log(2.0)))). The subsequent processing object is the modulus vector of the high-frequency component coefficient of each scale ; (42) Start the search from the Rth scale high frequency component, R = r + 1, and is the threshold, Rat is the proportional coefficient, max_R is the maximum absolute value of the high-frequency component amplitude of this scale, and the first modulus extreme value and modulus extreme value point that crosses the threshold are sequentially searched as the modulus extreme value point of this scale, recorded as MaxModPoint_R; (43) In the upward scale R-1, based on the point MaxModPoint_R within the range of plus or minus 2, sequentially search for the point that crosses the threshold The first modulus extreme value and corresponding point with the same sign as MaxModPoint_R are recorded as the modulus extreme value point of this scale, MaxModPoint_(R-1). If the search is empty, record MaxModPoint_(R-1) =MaxModPoint_R; (44) Enter the next higher scale and repeat the search mechanism of steps (42) and (43) until the second scale stops and enters the next higher scale. MaxModPoint_2 is the modulus extreme point at the search point of the second scale. The overall modulus extreme point is recorded as MaxModPoint=MaxModPoint_2.

6. The double-terminal traveling wave ranging method applicable to a distribution network according to claim 1, characterized in that: For S5, the wave head starting point is further estimated by constructing a threshold filter, including the following steps: (51) For the normalized waveforms I_1M and I_1N, construct the threshold Thd: The threshold consists of two parts: , where average is the mean value of the wavefront beforepoint=min(modmaxPoint / 2, hardStarPoint - 50) segment with a bandwidth of width, noise is the noise amplitude of the wavefront point beforepoint segment, obtained through segment statistics, and polarity is the polarity of the modulo extreme point; (52) For one - end threshold Thd1, within the range of points (beforePoint1, modmaxPoint1), compare the amplitudes point - by - point in reverse order, and obtain the first point that crosses the threshold, denoted as the wave - head starting point (uppoint, upvalue); (53) For the other end, construct the threshold Thd2, repeat the above process, and obtain the wave - head starting point of the fault traveling wave at this end (downpoint, downvalue).

7. The method for double - ended traveling - wave ranging applicable to a distribution network according to claim 1, wherein: For the amplitude interpolation calibration of S6, since the amplitudes and polarities of the double - ended wave - heads are obviously opposite, that is , fabs( ) The symbol is to take the absolute value operation, and the specific implementation steps are as follows: If fabs(upvalue) >= fabs(downvalue), the wave - head at the upstream end, i.e., the M - end, is estimated as follows M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue); If fabs(upvalue) < fabs(downvalue), the wave - head at the downstream end, i.e., the N - end, is estimated as follows N_point = downpoint + (-upvalue - downvalue) / (DownWave[downpoint + 1] - downvalue).

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

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