Double-ended traveling wave ranging method, device and storage medium applicable to distribution network

Abstract

A traveling wave ranging method, device and storage medium applicable to a distribution network according to the present invention include first, for the traveling wave of the fault phase current, preprocessing the double-ended current traveling wave through normalization and rough zero-phase band-pass filtering in the characteristic frequency band; using spline wavelet transform to calibrate appropriate modulus maximum points as the preliminary estimation of the wave head starting point; then further estimating the wave head on the double-ended time-domain original traveling wave waveform by using the threshold method, and obtaining the accurate wave head starting point through double-ended amplitude interpolation calibration; finally, calculating the fault point position according to the double-ended traveling wave ranging principle. The present invention preprocesses the fault phase current waveform into a current traveling wave in a rough characteristic frequency band through zero-phase filtering means, eliminates the interference outside the characteristic frequency band, takes the wave head inflection point as the wave head starting point, gradually estimates the inflection point through wavelet modulus maximum and signal amplitude threshold methods, and then obtains a more accurate approximation of the inflection point through double-ended amplitude interpolation, so as to achieve a ranging accuracy beyond the sampling rate limit.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and particularly relates to a double - ended traveling wave ranging method, device, and storage medium applicable to distribution networks. 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 generation location, save the manpower and material resources consumed by line patrol, and improve the fault efficiency and time. Studying a fault ranging method suitable for application in the distribution network is of great significance for improving the power supply reliability of the distribution network and reducing power outage losses.

[0003] The research on traveling wave ranging started earlier. The ranging technology and device were first applied to transmission lines, with high measurement accuracy, rapid response, and being unaffected by factors such as line structure asymmetry and transducer transformation error. Currently, it has a relatively mature application in the transmission network. Compared with high - voltage lines, the working conditions faced by the traveling wave ranging application in the distribution network are more complex. The wavelet modulus maximum method, which is mature and widely used in high - voltage lines, cannot be directly used in the distribution network and requires further optimization methods.

[0004] In the distribution network line, 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, the magnitude of the fault voltage phase angle, and the different attenuation and distortion degrees of the double - ended wave heads 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 become smoother. There is a certain distance between the arrival point of the traveling wave head and the appropriate wavelet modulus maximum point. Under the limitation of the finite 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 starting point of the wave head is analyzed, and a high - precision double - ended traveling wave ranging method adapted to the actual characteristics and development requirements of the distribution network is proposed. Summary of the Invention

[0006] A double - ended traveling wave ranging method, device, and storage medium applicable to distribution networks proposed by the present invention can at least solve one of the technical problems in the background art.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A double - ended traveling wave ranging method applicable to distribution networks includes the following steps:

[0009] Step 1: Acquisition of current traveling wave and fault phase judgment:

[0010] Collect the three-phase fault current traveling waves of 1000 points before 200 points and after 800 points of the fault trigger at both ends M and N of the distribution network line through a current transformer: Ia, Ib, Ic, where the sampling rate is 1Mhz and the sampling time is 1 millisecond. 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 selected through the power frequency judgment information or the amplitude of the three-phase traveling wave waveform.

[0011] Step 2: Zero-phase filtering of the fault-phase current traveling wave:

[0012] Directly perform zero-phase band-pass filtering on the fault-phase current traveling wave, and the filtering result is used as the fault vector to be processed, denoted as: IM, IN respectively.

[0013] Step 3: Wavelet decomposition:

[0014] Decompose the band-pass filtered data IM and IN into high-frequency and low-frequency components of a set scale through wavelet transform, and convert the components into modulus matrices mod_M and mod_N. Each row of the matrix corresponds to a scale.

[0015] Step 4: Selection of modulus maxima:

[0016] Based on the double-ended modulus components, select the modulus extreme points that meet the requirements as the initial estimates of the wavefront starting points, which are (p_M, v_M) and (p_N, v_N) respectively.

[0017] Step 5: Threshold estimation of the wavefront starting point of the original traveling wave signal:

[0018] For the double-ended fault-phase traveling wave components, perform normalization based on their respective maximum amplitudes, denoted as I_1M and I_1N; based on the preliminary estimation of the wavefront starting point, obtain the wavefront starting points (uppoint, upvalue) and (downpoint, downvalue) through the constructed threshold screening.

[0019] Step 6: Based on the respective wavefront starting points (uppoint, upvalue) and the corresponding amplitude point pairs (downpoint, downvalue), obtain more accurate wavefront starting points: M_point and N_point through wavefront amplitude interpolation calibration.

[0020] Step 7: Fault distance calculation: According to the point difference between the starting points of the traveling wave waveforms at both ends of the fault And the time difference between the triggering moments of the waveforms at both ends to obtain the time difference between the arrival of the fault traveling wave front at both ends , and then obtain the distances from the fault point to the M end and the N end as:

[0021]

[0022]

[0023] Among them, L is the line distance between the M end and the N end, and v is the traveling wave velocity.

[0024] Furthermore, for the fault phase judgment based on traveling waves in step 1, for single-phase grounding faults, it is judged by the maximum amplitude of the traveling wave, and the amplitude of the fault phase is significantly larger; for two-phase short circuits or groundings, three-phase short circuits or groundings, the fault at the M end is mainly judged first, and the N end follows the judgment result of the M end to keep the judgment results of the double-ended fault phases consistent.

[0025] Furthermore, the specific implementation steps of the zero-phase filtering adopted in step 2 are as follows:

[0026] (1). Exclude the low-frequency band composed of power frequency harmonics and the high-frequency band of system noise, select the intermediate frequency band window (10 kHz - 300 kHz) as the characteristic frequency band, configure the corresponding filtering parameters in combination with the sampling rate and the requirements of linear phase filtering, and obtain the finite impulse response (FIR) filter coefficients;

[0027] (2). Based on the filtering coefficients, perform zero-phase band-pass filtering on the sampled data of the traveling waves of the fault phase currents at both ends, that is, based on the filtering coefficients, perform forward filtering on the data, reverse the result, then perform filtering again and reverse the result to obtain unbiased filtered signals IM and IN.

[0028] Furthermore, in the wavelet decomposition process of step 3, for S(n) (representing: IM or IN), the following discrete dyadic spline wavelet transform is adopted:

[0029]

[0030]

[0031] Here, n represents the serial number, is the approximation coefficient vector of the j-th scale, is the wavelet coefficient vector of the j-th scale, is the convolution symbol, and are from the filter bank vectors of the spline wavelet (simply, the vectors h and g can be taken as [-3 / 2, 3 / 2] and [1 / 8, 3 / 8, 3 / 8, 1 / 8]), and are the high-pass and low-pass filter bank vectors at the j - 1 scale respectively, formed by and gradually inserting zeros, is the compensation factor, and the specific implementation is completed based on the à Trous framework stationary spline wavelet transform. The specific signal space decomposition is represented by the following schematic formula:

[0032]

[0033] S

[0034]

[0035]

[0036]

[0037] In the above schematic formula, S(n) can be equivalently regarded as the coefficient representation in the basis of the entire space. In the subsequent equivalent relationship, the entire space is gradually decomposed to form wavelet subspaces of different scales. and can be understood as the coefficient representation of the basis of the corresponding subspace, where represents the direct sum of independent subspaces, represents the equivalent relationship of the sum of different subspaces, （ , ） represents the paired high and low frequency coefficient combinations of the subspace at a certain scale.

[0038] The definition of the modulus of the wavelet decomposition component at scale j is as follows:

[0039]

[0040] where, represents a modulus extremum in the modulus of the wavelet decomposition component at scale j, is the maximum value of the absolute value of the wavelet component at the j-th scale. The specific implementation of the modulus can obtain the extremum points and extrema through the first and second order differences of the wavelet component, and the amplitude of non-extremum points can be directly set to zero.

[0041] Furthermore, for the modulus extremum screening process in step 4, the specific screening steps are as follows:

[0042] (41) Calculate the scale corresponding to the target frequency band: Based on the sampling frequency fs and the median Target_fs of the target frequency band, the scale corresponding to the target frequency band is obtained by the formula r = floor(1 + ((log(fs / 2 / Target_fs) / log(2.0)))), and the subsequent processing object is the modulus vector of the high-frequency component coefficients of each scale ;

[0043] (42) Start searching from the high-frequency component at the R-th scale, R = r + 1, with 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;

[0044] (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;

[0045] (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.

[0046] Furthermore, for the threshold card point in step 5, there are the following implementation steps:

[0047] (51) For the normalized waveforms I_1M and I_1N, construct the threshold Thd: The threshold consists of two parts: ，

[0048] Where average is the wavefront beforepoint=min(modmaxPoint / 2, hardStarPoint-50) segment, bandwidth is the average of width, noise is the noise amplitude of the wavefront point beforepoint segment, obtained by segment statistics, and polarity is the polarity of the modulus extreme point;

[0049] (52) Based on the threshold Thd1 at one end, within the point range (beforePoint1, modmaxPoint1), compare the amplitudes point by point in reverse order, and obtain the first point that crosses the threshold as the wave head starting point (uppoint, upvalue);

[0050] (53) For the other end, construct the threshold Thd2 and repeat the above process to obtain the starting point (downpoint, downvalue) of the fault traveling wave head at this end.

[0051] Furthermore, for the amplitude interpolation calibration in step 6, since the polarities of the double-ended wave head amplitudes are obviously opposite, that is, , fabs( ) The symbol is an absolute value operation, and the specific implementation steps are as follows:

[0052] If fabs(upvalue) >= fabs(downvalue), the upstream, i.e., the wavefront at the M end, is estimated as follows:

[0053] M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue);

[0054] If fabs(upvalue) < fabs(downvalue), the downstream, i.e., the wavefront at the N end, is estimated as follows:

[0055] N_point = downpoint + (-upvalue - downvalue) / (DownWave[downpoint + 1] - downvalue).

[0056] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0057] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0058] As can be seen from the above technical solutions, the present invention provides a double-terminal fault traveling wave ranging method applicable to a distribution network. First, for the fault-phase current traveling wave, double-terminal current traveling waves are obtained by normalizing and zero-phase band-pass filtering in a rough characteristic frequency band (10 kHz - 300 kHz). The spline wavelet transform is used to calibrate appropriate modulus maximum points as a preliminary estimate of the wavefront starting point. Then, on the double-terminal current waveforms, the threshold method is used to further estimate the wavefront starting point, and the accurate wavefront starting point is obtained through double-terminal amplitude interpolation calibration. Finally, the fault point location is calculated according to the double-terminal traveling wave ranging principle. The present invention preprocesses the fault-phase current waveform into a current traveling wave in a rough characteristic frequency band through filtering means, excludes the noise interference outside the characteristic frequency band, takes the wavefront inflection point as the wavefront starting point, gradually estimates and approaches this inflection point through the wavelet modulus maximum method and the amplitude threshold method, and then obtains an approximation beyond the sampling rate limit for this inflection point through double-terminal amplitude interpolation.

[0059] Compared with the prior art, the technical advantages of the present invention are as follows:

[0060] The present invention uses a new calibration principle proposed by us to calibrate the starting point of a wavefront by the inflection point of a wavefront waveform that is not easily interfered. A new step-by-step estimation and approximation calibration method is adopted for the original signal in the time domain, which can gradually approximate the true wavefront in the case of wavefront distortion and gentle steepness, overcoming the problems of inability to identify or large errors in the case of reduced wavefront singularity, and is applicable to traveling wave ranging for distribution network faults. At the same time, through the preprocessing of wavefront amplitude normalization and zero-phase band-pass filtering, the wavelet modulus maximum value of the wavefront can be made more prominent and easier to screen, and the noise interference in irrelevant frequency bands can be removed;

[0061] Based on the result of the approximation calibration, the present invention obtains a better approximation of the wavefront by interpolating the amplitude near the normalized wavefront punctuation. Under the determined hardware sampling rate, this technical processing can further improve the calibration effect of the wavefront starting point and achieve a ranging accuracy beyond the sampling rate. Brief Description of the Drawings

[0062] Figure 1 It is a schematic flow chart of the calculation method for traveling wave ranging disclosed by the present invention.

[0063] Figure 2 It is a topological structure diagram for implementing the traveling wave ranging experiment test of the distribution network.

[0064] Figure 3 It is a zero-phase band-pass filtering characteristic diagram.

[0065] Figure 4 It is a comparison diagram of the fault traveling wave signal before and after filtering.

[0066] Figure 5 It is a high-frequency component and corresponding modulus diagram of the fault traveling wave signal at the M end.

[0067] Figure 6 It is a high-frequency component and corresponding modulus diagram of the fault traveling wave signal at the N end.

[0068] Figure 7 It is a final wavefront calibration diagram for both ends. Detailed Embodiment

[0069] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0070] As Figure 1 shown, the method for double-end traveling wave ranging applicable to the distribution network described in this embodiment includes the following steps:

[0071] Step 1: Acquisition of current traveling wave and fault phase judgment: Collect 1000 three-phase fault current traveling waves Ia, Ib, and Ic at the M and N ends of the distribution network line, which are 200 points before and 800 points after fault triggering. The sampling rate is 1Mhz, and the sampling time is 1 millisecond. The hardware sampling is equipped with a high-pass filter circuit to remove power frequency and related harmonic signal components below 1khz; and the fault phase is selected through power frequency judgment information or the amplitude of the three-phase traveling wave waveforms.

[0072] Step 2: Zero-phase filtering of the fault phase current traveling wave: Directly perform zero-phase band-pass filtering on the fault phase current traveling wave, and the filtering result is used as the fault vector to be processed, denoted as IM and IN respectively.

[0073] Step 3: Wavelet decomposition: Decompose the band-pass filtered data IM and IN into high-frequency and low-frequency components at a certain scale through wavelet transform, and convert the components into modulus matrices mod_M and mod_N. Each row of the matrix corresponds to a scale.

[0074] Step 4: Selection of modulus maxima: Based on the double-ended modulus components, select appropriate modulus maximum points as the initial estimate of the wavefront starting points, which are (p_M, v_M) and (p_N, v_N) respectively.

[0075] Step 5: Threshold estimation of the wavefront starting point of the original traveling wave signal: Normalize the double-ended fault phase traveling wave components based on their respective maximum amplitudes, denoted as I_1M and I_1N; on the basis of the initial estimate of the wavefront starting point, obtain the wavefront starting points (uppoint, upvalue) and (downpoint, downvalue) through the constructed threshold screening.

[0076] Step 6: Based on the respective wavefront starting points and corresponding amplitude point pairs ((uppoint, upvalue), (downpoint, downvalue)), obtain more accurate wavefront starting points: M_point and N_point through wavefront amplitude interpolation calibration.

[0077] Step 7: Fault distance calculation: According to the point difference between the starting points of the traveling wave waveforms at both ends of the fault and the time difference between the triggering moments of the waveforms at both ends to obtain the time difference between the arrival of the fault traveling wave front at both ends , and then obtain the distances from the fault point to the M end and the N end as:

[0078]

[0079]

[0080] where L is the line distance between the M end and the N end, and v is the traveling wave velocity.

[0081] Specifically, for judging the faulty phase based on traveling waves in step 1, for single-phase grounding faults, it can be judged by the maximum amplitude of the traveling wave, and the amplitude of the faulty phase is significantly larger; for two-phase short circuits or groundings, three-phase short circuits or groundings, the faults at the M end are judged first, and the N end follows the judgment result of the M end to keep the judgment results of the double-ended faulty phases consistent.

[0082] The specific implementation steps of the zero-phase filtering adopted in step 2 are as follows:

[0083] (1). Exclude the low-frequency band composed of power frequency harmonics and the high-frequency band of system noise, select the intermediate frequency band window (10 kHz - 300 kHz) as the characteristic frequency band, configure the corresponding filtering parameters in combination with the sampling rate and the requirements of linear-phase filtering, and obtain the finite impulse response (FIR) filter coefficients;

[0084] (2). Based on the filtering coefficients, perform zero-phase band-pass filtering on the sampled data of the traveling waves of the faulty-phase currents at both ends, that is, based on the filtering coefficients, perform forward filtering on the data, reverse the result, then perform filtering again and reverse the result to obtain unbiased filtered signals IM and IN.

[0085] In the wavelet decomposition process of step 3, for S(n) (representing: IM or IN), the following discrete dyadic spline wavelet transform is adopted:

[0086]

[0087]

[0088] Here, n represents the serial number, is the approximation coefficient vector of the j-th scale, is the wavelet coefficient vector of the j-th scale, is the convolution symbol, and are the filter bank vectors derived from the spline wavelet (simply, the vectors h and g can be taken as [-3 / 2, 3 / 2] and [1 / 8, 3 / 8, 3 / 8, 1 / 8]), and are the high-pass and low-pass filter bank vectors at the (j - 1)-th scale, respectively, formed by and gradually inserting zeros, is the compensation factor (compensating for the error caused by the discrete scale reduction to make the coefficients more accurate); the specific implementation is completed based on the stationary spline wavelet transform of the à Trous framework, and the specific signal space decomposition is represented by the following schematic formula:

[0089]

[0090] S

[0091]

[0092]

[0093]

[0094] In the above schematic formula, S(n) can be equivalently regarded as the coefficient representation in the basis of the entire space. In the subsequent equivalent relationship, the entire space is gradually decomposed to form wavelet subspaces of different scales. and can be understood as the coefficient representation of the basis of the corresponding subspace, where represents the direct sum of independent subspaces, represents the equivalent relationship of the sum of different subspaces, （ , ） represents the paired high and low frequency coefficient combinations of the subspace at a certain scale.

[0095] The definition of the modulus of the wavelet decomposition component at scale j is as follows:

[0096]

[0097] where, represents a modulus extreme value in the modulus of the wavelet decomposition component at scale j, is the maximum value of the absolute value of the wavelet component at the j-th scale. The specific implementation of the modulus can obtain the extreme points and extremes through the first and second order differences of the wavelet component, and the amplitude of the non-extreme points can be directly set to zero.

[0098] For the modulus extreme value screening process in step 4, since the wavelet modulus extreme values at low scales are easily affected by high-frequency noise, and the wavelet modulus extreme values at high scales correspond to lower frequency bands and cannot reflect the high-frequency characteristics of transient traveling waves, the screening scheme starts from a suitable high scale and performs reverse screening, with the cut-off scale being j = 2. Of course, this scheme can also be set to other scales according to the actual situation (for example, j = 1, 3), or a specific scale can be selected alone. The specific screening steps are as follows:

[0099] (1) Calculate the scale corresponding to the target frequency band: Based on the sampling frequency fs and the median Target_fs of the target frequency band, the scale corresponding to the target frequency band is obtained by the formula r = floor(1 + ((log(fs / 2 / Target_fs) / log(2.0)))), and the subsequent processing object is the modulus vector of the high-frequency component coefficients at each scale ;

[0100] (2) Start searching from the high-frequency component at the R-th scale (R = r + 1), with is the threshold value (where the proportionality coefficient Rat = 0.35 and max_R is the maximum value of the absolute value of the amplitude of the high-frequency component at this scale). Sequentially search for the first modulus extreme value and the modulus extreme point that cross the threshold value as the modulus extreme point at this scale, denoted as MaxModPoint_R;

[0101] (3) At the previous scale (R - 1), within the range of plus or minus 2 of the point MaxModPoint_R, sequentially search for the first modulus extreme value and the corresponding point with the same sign as MaxModPoint_R that cross the threshold value （ ） and record it as the modulus extreme point at this scale, MaxModPoint_(R - 1). If the search is empty, record MaxModPoint_(R - 1)=MaxModPoint_R;

[0102] Then enter the previous scale. Repeat the search mechanism in steps (2) and (3) until the second scale. Stop entering the upper scale. MaxModPoint_2 is the modulus extreme point searched at the second scale, and the overall modulus extreme point is recorded as MaxModPoint = MaxModPoint_2.

[0103] For the threshold sticking point in step 5, there are the following implementation steps:

[0104] (1) For the normalized waveforms I_1M and I_1N, construct the threshold Thd: The threshold consists of two parts: ,

[0105] where average is the mean value of the wavefront (beforepoint = min(modmaxPoint / 2, hardStarPoint - 50)) segment (bandwidth is 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;

[0106] (2) Based on one - end threshold Thd1, within the range of points (beforePoint1, modmaxPoint1), compare the amplitudes point - by - point in reverse order to obtain the first point that crosses the threshold value and record it as the wave - head starting point (uppoint, upvalue);

[0107] (3) For the other end, construct the threshold Thd2 and repeat the above process to obtain the wave - head starting point of the fault traveling wave at this end (downpoint, downvalue).

[0108] For the amplitude interpolation calibration in step 6, since the amplitude polarities of the double-ended wavefronts are obviously opposite, that is, , fabs( ) the symbol is an absolute value operation, and the specific implementation steps are as follows:

[0109] If fabs(upvalue) >= fabs(downvalue), the upstream (M-terminal) wavefront is estimated as follows,

[0110] M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue).

[0111] If fabs(upvalue) < fabs(downvalue), the downstream (N-terminal) wavefront is estimated as follows,

[0112] N_point = downpoint + (-upvalue - downvalue) / (DownWave[downpoint + 1] - downvalue).

[0113] The following is an example for illustration:

[0114] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0115] As Figure 1 shown is the overall process framework of this embodiment. The test topology structure consists of Figure 2 shown. The test line is a line with a length of 4 km from node 1 to DTU_82. A double-ended 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 double-ended comtrade standard format is obtained through a hardware filtering circuit and a current transformer acquisition channel, and is transmitted to the master station for processing; the method includes the following steps:

[0116] Step (1): The hardware circuit sampling rate 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 phase currents of the double ends are collected: Ia, Ib, Ic.

[0117] Step (2): The current traveling waves of the fault phase are screened out from the double-ended three-phase fault currents through the sudden change amplitude, and the band-pass filtering is used as the processing object. The band-pass filtering characteristics and the waveform data are as Figure 3 and 4 shown.

[0118] Step 3: Perform cubic spline wavelet transform on the filtered traveling waves of the double-ended fault phases. Screen the modulus maxima of the high- and low-frequency component coefficient vectors to obtain a preliminary estimate of (201 / 201). Further punctuate through the respective thresholds at both ends to obtain the calibration result of the starting points of the traveling wave heads at both ends as (199 / 199); through amplitude interpolation and calibration at both ends, substitute into the interpolation formula

[0119] M_point = uppoint + (-downvalue - upvalue) / (UpWave[uppoint + 1] - upvalue )

[0120] Substitute the parameters to get M_point = 199 + (-0.030797 - (-0.040609)) / (-0.09829 - (-0.040609)) = 198.829992

[0121] Final calibration of the starting points of the traveling wave heads at both ends (198.83 / 199); The high-frequency components and modulus waveforms of the wavelet decomposition at both ends are as shown in Figure 5 、 6 shown; The final calibration situation is as shown in Figure 7 shown.

[0122] Step 4: Set the fault traveling wave velocity as V = 2.93e8 m / s according to the line material, calculate the fault distances as L_M = 2998.505027m and L_N = 1001.494973m, with an error of 1.49m.

[0123] In summary of the above steps, in the modulus maxima screening and smoothing threshold screening, the modulus maxima points and threshold blocking points do not exactly coincide. The threshold blocking points are closer to the inflection points of the wave heads. Through further amplitude interpolation, wave head calibration beyond the sampling accuracy can be obtained, and corresponding better ranging accuracy can be acquired.

[0124] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0125] On yet another hand, the present invention also discloses a computer device including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, it causes the processor to execute the steps of the above method.

[0126] In another embodiment provided by the present application, a computer program product containing instructions is also provided, which when running on a computer causes the computer to execute any of the above embodiments of the double-ended traveling wave ranging method applicable to a distribution network.

[0127] It is understandable that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention. For the explanations, examples, and beneficial effects of the relevant content, reference can be made to the corresponding parts in the above methods.

[0128] 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0129] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device that includes the element.

[0130] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. 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 for the relevant parts, reference can be made to the partial description of the method embodiment.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment 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; 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 wavefront beforepoint=min(modmaxPoint / 2, hardStarPoint-50) segment, bandwidth is the average of width, noise is the noise amplitude of the wavefront point beforepoint segment, obtained by segment statistics, and polarity is the polarity of the modulus extreme point; (52) For the threshold Thd1 at one end, within the point range (beforePoint1, modmaxPoint1), compare the amplitudes point by point in reverse order, and obtain the first point that crosses the threshold as the wave head starting point (uppoint, upvalue); (53) For the other end, construct the threshold Thd2 and repeat the above process to obtain the starting point (downpoint, downvalue) of the fault traveling wave head at this end.

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 method for double - ended traveling - wave ranging applicable to a distribution network according to claim 3, wherein: In the wavelet decomposition process of S3, S(n) represents: IM or IN, and the following discrete dyadic spline wavelet transform is adopted: 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 full space basis. In the subsequent equivalent relationship, the full 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 definition of the modulus of the wavelet decomposition component at the j - scale is 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 modulus extreme 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) Then enter the next higher scale, repeat the search mechanism of steps (42) and (43), stop entering the higher scale at the second scale, MaxModPoint_2 is the modulus extreme point searched at the second scale, and the overall modulus extreme point is recorded as MaxModPoint = MaxModPoint_2.

6. The method for double - ended traveling - wave ranging applicable to a distribution network according to claim 1, wherein: For the amplitude interpolation calibration in S6, since the amplitudes and polarities of the double - ended wavefronts 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 upstream (i.e., the M - end) wavefront is estimated as follows, M_point = uppoint+(-downvalue - upvalue) / (UpWave[uppoint + 1]-upvalue); If fabs(upvalue) < fabs(downvalue), the downstream (i.e., the N - end) wavefront is estimated as follows, N_point = downpoint+(-upvalue - downvalue) / (DownWave[downpoint + 1]-downvalue).

7. 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 6.

8. 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 6.

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