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

Abstract

According to the double-end traveling wave distance measurement wave head calibration method and device suitable for the power distribution network and the medium, a proper wavelet high-frequency component is selected through a characteristic frequency band, and a proper mode extreme point is calibrated to serve as initial estimation of a wave head starting point; constructing a smooth threshold value on the low-frequency component corresponding to the characteristic frequency band based on the preliminary estimation and the hard trigger point; a more accurate wave head starting point is obtained through the self-adaptive smooth threshold value search; and finally, calculating the position of a fault point according to a double-end traveling wave fault location formula. The invention provides a novel principle and method for completing coordinated point marking by taking waveform break point calibration of a wave head as wave head starting point calibration and fully utilizing appropriate-scale high and low frequency components of a wavelet domain, and practices prove that the novel calibration principle and method are not easily influenced by traveling wave attenuation, distortion and noise interference caused by traveling wave dispersion and wave impedance; and meanwhile, the problem of low calibration precision caused by inconsistent wave head characteristic frequency spectrums of the traveling waves at the two ends can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a double-end traveling wave ranging wave head calibration method, equipment and medium suitable for distribution network. Background Art

[0002] Traveling wave ranging technology and devices were first used in transmission lines. They have high measurement accuracy, rapid response, and are not affected by factors such as line structure asymmetry and transformer transmission errors. In the wavelet transform modulus maximum principle, which is currently maturely applied in high-voltage power grids, the modulus maximum point of the wavelet component corresponds to the point where the component waveform changes fastest. Such a point is located at a certain point on the rising or falling edge of the wave head. Since the rising or falling edge of the traveling wave head in the transmission network is steep, directly using the modulus maximum point as the wave head starting point can also obtain a better ranging effect; Compared with high-voltage lines, the working conditions faced by the application of traveling wave ranging in distribution networks are more complex. The neutral point of medium and low voltage distribution systems mostly adopts non-effective grounding methods, and at the same time, they mostly adopt a radial network structure, with the main characteristics of multiple branch feeders and a mixture of overhead lines / cable lines. When a grounding or short-circuit fault occurs, the neutral point grounding method, grounding resistance, line parameters, topology, distributed parameters, and the number of shunt branches directly affect the shape, amplitude, and steepness of the fault traveling wave head; At the same time, in the distribution network line, the working environment of the on-site traveling wave ranging device will have a lot of random noise, and the traveling wave propagation path and length, the size of the transition resistance and the size of the fault voltage phase angle directly affect the shape and steepness characteristics of the wave head, causing the wave head to be distorted and become flatter. For this reason, the wavelet modulus maximum principle is difficult to effectively and accurately calibrate the starting point of the wave head. Therefore, further wave head starting point calibration principles and methods are needed in the distribution network traveling wave ranging. Summary of the invention

[0003] The present invention proposes a double-terminal traveling wave ranging wave head calibration method, device and storage medium suitable for a distribution network, which can solve at least one of the technical problems in the background technology.

[0004] To achieve the above object, the present invention adopts the following technical solutions: A double-terminal traveling wave ranging wave head calibration method applicable to a distribution network comprises the following steps: Step 1: Extraction of fault traveling wave: The three-phase fault current traveling wave sampling sequence of 1000 points, 200 points before and 800 points after the fault triggering, is collected through the current transformer at the M and N ends of the distribution network line: Ia, Ib, Ic, where the sampling rate is selected as 1Mhz, the sampling time is one millisecond, and the hardware sampling is equipped with a high-pass filter device to remove the power frequency and related harmonic signal components below 10khz; Step 2: Construct the modulus fault component: Perform phase-mode transformation on the three-phase fault current traveling wave sequence after sampling and filtering: (I0, I1, I2) = A(Ia, Ib, Ic)', where A is the phase-mode transformation matrix, I0 is the zero-mode component, and I1 and I2 are line-mode components; select the line-mode component I1 as the fault modulus vector to be processed, and the fault traveling wave moduli at both ends of M and N are recorded as: IM, IN respectively; Step 3: Wavelet decomposition: Decompose the fault row moduli IM and IN at both ends of M and N into multi-scale high and low frequency components respectively through wavelet transform, and combine the multi-scale high and low frequency components into modulus matrices mod_M and mod_N, where each row of the matrix corresponds to a scale; Step 4: Modulus extreme value screening: Based on the modulus matrix of both ends M and N, set the characteristic frequency band to 10khz~200khz, and screen the modulus extreme value points that meet the requirements as the preliminary estimate of the starting point of the fault traveling wave head reaching both ends, which are recorded as MaxModPoint_M and MaxModPoint_N respectively; Step 5: Further estimation of the wave head starting point: Based on the preliminary estimation of the wave head starting point, combined with the hardware wave head trigger point hardStarPoint (known when the traveling wave is sampled), a smoothing threshold is constructed, and based on the threshold screening, more accurate wave head starting point estimates headStartPoint_M and headStartPoint_N are obtained; Step 6: Fault distance calculation: Estimate the difference between headStartPoint_M and headStartPoint_N based on the precise wave head starting points at both ends of M and N The time difference between the fault traveling wave head reaching both ends is calculated by the triggering time of the waveform recording at both ends , and then the distance from the fault point to the M and N ends is obtained through the distance measurement formula:

[0005]

[0006] Wherein, L is the line distance between the M end and the N end, and v is the speed of the fault traveling wave.

[0007] Furthermore, in the selection of the transformation matrix in step 2, under the premise that the fault phase has been determined, the commonly used phase mode transformation is selected, including the Karenbel transformation matrix A=(1,1,1; 1,-1,0; 1,0,-1). In the case of uncertain fault phase, the improved version of the phase mode transformation matrix A=(1,1,1; 1,-5,4; 1, 4,-5) is used. Furthermore, in the wavelet decomposition process of step 3, for the fault modulus S(n) (representing the fault modulus to be processed: IM or IN), the following discrete binary spline wavelet transform is used:

[0008]

[0009] 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 example, the vectors h and g can be [-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 a compensation factor used to overcome the impact of scale; the specific implementation of wavelet decomposition is based on the stationary spline wavelet transform of the à Trous framework, and the specific signal space decomposition is expressed by the following schematic formula:

[0010]

[0011]

[0012]

[0013]

[0014] The fault modulus S(n) in the above schematic formula can be equivalently regarded as a 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 the paired combination of high and low frequency coefficients of the subspace at a certain scale. Furthermore, the modulus of the j-scale wavelet decomposition component is defined as follows:

[0015] 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 wavelet component coefficient modulus is to obtain local 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.

[0016] Furthermore, for the mode extreme value screening process in step 4, the screening scheme starts from the scale corresponding to the characteristic frequency and traces back in reverse order to the cutoff scale j=2.

[0017] Furthermore, the specific screening steps in step 4 are as follows: (1) 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)}, where floor{} means rounding up the variables in the brackets, and the processing object is the modulus vector of the high-frequency component coefficients of each scale in the modulus matrix. ; (2) Start the search from the high-frequency component of the Rth scale, R = r+1, with Rat max_R 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 sequentially search to obtain the point number corresponding to the first maximum or minimum value of the high-frequency component element of this scale whose absolute value is greater than the threshold. This point number is used as the modulus extreme point of this scale, recorded as MaxModPoint_R; (3) In the upward scale R-1, based on the range of the point MaxModPoint_R plus or minus 2, sequentially search for elements whose absolute value exceeds the threshold Rat max_R-1, the first modulus extreme value and corresponding point with the same sign as MaxModPoint_R, record this point as the modulus extreme value point of this scale, MaxModPoint_(R-1), if the search is empty, record MaxModPoint_(R-1) =MaxModPoint_R; (4) Enter the next scale and repeat the search mechanisms of (2) and (3). Stop the search at the second scale and record the overall modulus extreme point as MaxModPoint = MaxModPoint_2. (5) The two-end modulus extreme value points are recorded as MaxModPoint_M and MaxModPoint_N respectively.

[0018] Furthermore, for step 5 to obtain a more accurate estimation of the wave head starting point, the following implementation steps are performed: For the M end, obtain a more accurate estimation of the wave head starting point. Based on the selected modulus extreme point MaxModPoint =MaxModPoint_M, determine the wave head front point beforePoint = min(MaxModPoint / 2, hardStarPoint-50) with reference to the hardware starting point. The processing object is the coefficient vector of the low-frequency component of the modulus matrix corresponding to the scale of the characteristic frequency band; (2) Determine the smoothing threshold smoothThd based on the waveform before the wave front point, set the smoothing starting point startPoint = 50, and set the smoothing bandwidth smoothWidth = 20; (2.1) For each point from the starting point startPoint to beforePoint, calculate the difference between the amplitude of the point and the average amplitude of the points in smoothWidth before this point, record the corresponding absolute value of the difference, and form a sequence; (2.2) The maximum value of this difference sequence is calculated as maxValue, and appropriate relaxation is performed. The relaxation coefficient can be selected as 1.2, and the threshold smoothThd = 1.2 is generated. maxValue; (3) Based on the threshold smoothThd, within the point range (beforePoint, MaxModPoint), compare the amplitudes point by point in reverse order, and obtain the first point that exceeds the threshold as a more accurate estimate of the wave head starting point headStartPoint_M; (4) Repeat the above process for the other end to obtain a more accurate estimate of the wave head starting point headStartPoint_N of the fault traveling wave at that end.

[0019] 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.

[0020] 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.

[0021] It can be seen from the above technical scheme that the present invention is based on the theoretical research and test practice of wavelet transform modulus extreme value detection, analyzes the essential process of wave head detection, and proposes a new double-end traveling wave head calibration and ranging method. Specifically, the present invention provides a double-end fault traveling wave ranging method suitable for distribution network. First, the appropriate wavelet high-frequency component is selected through the characteristic frequency band (10khz-200khz), and the appropriate modulus extreme value point is calibrated as the preliminary estimate of the wave head starting point; Secondly, a smoothing threshold is constructed based on the preliminary estimate and hard trigger point on the low-frequency component corresponding to the characteristic frequency band; Furthermore, a more accurate wave head starting point is obtained through the adaptive smoothing threshold search; Finally, the fault point position is calculated according to the double-end traveling wave ranging formula. Different from the conventional calibration process in which the modulus maximum value point is directly calibrated as the wave head starting point in the traditional wavelet transform method, the present invention proposes a calibration principle of taking the wave head waveform inflection point as the wave head starting point and making full use of the appropriate high and low frequency components in the wavelet domain to coordinate and gradually complete the calibrating and ranging process.

[0022] Compared with the prior art, the present invention has the following advantages: The present invention proposes a calibration principle that uses a wave head waveform inflection point that is not easily disturbed as the wave head starting point, and a step-by-step progressive screening calibration method based on modulus maximum screening and adaptive threshold card points. This method can obtain a punctuation point that is closer to the actual wave head starting point when the signal-to-noise ratio of the traveling wave signal is small, and achieve better ranging effect and accuracy. At the same time, the algorithm overcomes the problem that conventional algorithms cannot recognize or have large errors in the case of wave head distortion and gentle steepness, and has better ranging effect and strong applicability in distribution network traveling wave moment measurement.

[0023] At the same time, in terms of implementation plan, the present invention organizes the overall structure based on the distribution characteristics of the distribution network and the principles of distributed collection and master station calculation, automatically collects three-phase current traveling waves and uploads waveform data, and obtains accurate ranging effects through master station analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the flow chart of the calculation method of traveling wave ranging disclosed in the present invention; Figure 2 The topological structure diagram for implementing the distribution network traveling wave ranging experiment test; Figure 3 It is the wavelet high frequency component and corresponding modulus diagram of the fault traveling wave modulus signal at the M end; Figure 4 is the wavelet low-frequency component of the fault traveling wave modulus signal at the M end and the corresponding modulus diagram; Figure 5 It is the wavelet high frequency component and corresponding modulus diagram of the N-terminal fault traveling wave modulus signal; Figure 6 It is the wavelet low-frequency component of the traveling wave modulus signal of the N-terminal fault and the corresponding modulus diagram; Figure 7 It is a schematic diagram of the calibration of the upper modulus maximum and the smoothing threshold of the second-order low-frequency component of the traveling wave modulus signal of the double-terminal fault; Figure 8 Schematic diagram of the final calibration point of the double-ended wave head at the original fault traveling wave modulus waveform. DETAILED DESCRIPTION

[0025] 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.

[0026] Based on the smooth wave head characteristics of the distribution network analyzed in the above background, the principle of calibrating the modulus maximum point (the point with the largest amplitude change) as the wave head starting point in the traditional wavelet transform method has inevitable principle errors in the distribution network. Therefore, the present invention proposes a calibration principle of using the waveform inflection point before the modulus maximum point as the wave head starting point, thereby forming a double-terminal traveling wave ranging method suitable for the distribution network.

[0027] The embodiments of the present invention specifically adopt the following technical solutions: An improved double-terminal traveling wave head calibration and ranging method for a distribution network, the method comprising the following steps: Step 1: Extraction of fault traveling wave: The three-phase fault current traveling wave sampling sequence of 1000 points, 200 points before and 800 points after the fault triggering, is collected through the current transformer at the M and N ends of the distribution network line: Ia, Ib, Ic, where the sampling rate is selected as 1Mhz, the sampling time is one millisecond, and the hardware sampling is equipped with a high-pass filter device to remove the power frequency and related harmonic signal components below 10khz; Step 2: Perform phase-mode transformation on the three-phase fault current traveling wave sequence in S1 after sampling and filtering: (I0, I1, I2) = A (Ia, Ib, Ic)', where A is the phase-mode transformation matrix, I0 is the zero-mode component, and I1 and I2 are line-mode components; select the line-mode component I1 as the fault modulus vector to be processed, and the fault traveling wave moduli at both ends of M and N are recorded as: IM, IN respectively; Step 3: Wavelet decomposition: Decompose the fault row moduli IM and IN at both ends of M and N into high and low frequency components of multiple scales through wavelet transform, and organize the components into modulus matrices mod_M and mod_N, where each row of the matrix corresponds to one scale; Step 4: Modulus extreme value screening: Based on the modulus matrix of both ends M and N, set the characteristic frequency band to 10khz~200khz, and screen the modulus extreme value points that meet the requirements as the preliminary estimate of the starting point of the fault traveling wave head reaching both ends, which are recorded as MaxModPoint_M and MaxModPoint_N respectively; Step 5: Further estimation of the wave head starting point: Based on the preliminary estimation of the wave head starting point, combined with the hardware wave head trigger point hardStarPoint (known when the traveling wave is sampled), a smoothing threshold is constructed, and based on the threshold screening, more accurate wave head starting point estimates headStartPoint_M and headStartPoint_N are obtained; Step 6: Fault distance calculation: Estimate the difference between headStartPoint_M and headStartPoint_N based on the precise wave head starting points at both ends of M and N The time difference between the fault traveling wave head reaching both ends is calculated by the triggering time of the waveform recording at both ends , and then the distance from the fault point to the M and N ends is obtained through the distance measurement formula:

[0028]

[0029] Wherein, L is the line distance between the M end and the N end, and v is the speed of the traveling wave.

[0030] Specifically, in step 2, for the selection of the transformation matrix, under the premise that the fault phase has been determined, the commonly used phase mode transformation such as the Karenbel transformation matrix A=(1,1,1; 1,-1,0; 1,0,-1) is selected. In the case of uncertain fault phase, the improved version of the phase mode transformation matrix A=(1,1,1; 1,-5,4; 1, 4,-5) is used.

[0031] Specifically, in the wavelet decomposition process of step 3, for the fault modulus S(n) (representing the fault modulus to be processed: IM or IN), the following discrete binary spline wavelet transform is used:

[0032]

[0033] 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 example, the vectors h and g can be [-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 (compensation and reduction of 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:

[0034]

[0035]

[0036]

[0037]

[0038] The fault modulus S(n) in the above schematic formula can be equivalently regarded as a 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 the paired combination of high and low frequency coefficients of the subspace at a certain scale.

[0039] The modulus of the further j-scale wavelet decomposition component is defined as follows:

[0040] 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 wavelet component coefficient modulus is to obtain local 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.

[0041] Specifically, for the modulus extreme value screening process in step 4, since the low-scale wavelet modulus extreme value is easily affected by high-frequency noise, and the high-scale wavelet modulus extreme value corresponds to a low frequency band and cannot reflect the high-frequency characteristics of the transient traveling wave, the screening scheme starts from the scale corresponding to the characteristic frequency and goes back in reverse order to the cutoff scale j = 2. Of course, this scheme can also be set to other scales (for example, j = 1, 3, 4) according to actual conditions.

[0042] The specific screening steps are as follows: (1) 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)}, where floor{} means rounding up the variables in the brackets, and the processing object is the modulus vector of the high-frequency component coefficients of each scale in the modulus matrix. ; (2) Starting from the Rth scale high frequency component, search element by element sequentially, where R = r+1, with Rat max_R is the threshold, the proportional coefficient Rat = 0.35, max_R is the maximum absolute value of the high-frequency component amplitude of this scale, and the point number corresponding to the first maximum or minimum value of the high-frequency component element of this scale whose absolute value is greater than the threshold is sequentially searched. This point number is used as the modulus extreme point of this scale, recorded as MaxModPoint_R; (3) In the upward scale R-1, based on the range of the point MaxModPoint_R plus or minus 2, sequentially search for elements whose absolute value exceeds the threshold Rat max_R-1, the first modulus extreme value and corresponding point with the same sign as MaxModPoint_R, record this point as the modulus extreme value point of this scale: MaxModPoint_(R-1), if the search is empty, record MaxModPoint_(R-1) =MaxModPoint_R; (4) Enter the next higher scale and repeat the search mechanisms of (2) and (3). Stop the search at the second scale and record the overall modulus extreme point as MaxModPoint = MaxModPoint_2.

[0043] (5) The two-end modulus extreme value points are recorded as MaxModPoint_M and MaxModPoint_N respectively.

[0044] Specifically, for step 5 to obtain a more accurate estimation of the wave head starting point, the following implementation steps are performed: (1) Obtain a more accurate estimate of the wave head starting point for the M end. Based on the selected modulus extreme point MaxModPoint = MaxModPoint_M, determine the wave head front point beforePoint = min(MaxModPoint / 2, hardStarPoint-50) with reference to the hardware starting point. The processing object is the coefficient vector of the low-frequency component of the modulus matrix corresponding to the scale of the characteristic frequency band; (2) Determine the smoothing threshold smoothThd based on the waveform before the wavefront point, set the smoothing start point startPoint = 50, and set the smoothing bandwidth smoothWidth = 20; (2.1) For each point from the starting point startPoint to beforePoint, calculate the difference between the amplitude of the point and the average amplitude of the points in smoothWidth before this point, record the corresponding absolute value of the difference, and form a difference sequence; (2.2) The maximum value of this difference sequence is calculated as maxValue, and the threshold smoothThd=1.2 is generated by appropriate relaxation (the relaxation coefficient is selected as 1.2) maxValue; (3) Based on the threshold smoothThd, within the point range (beforePoint, modmaxPoint), compare the amplitudes point by point in reverse order, and obtain the first point that exceeds the threshold as a more accurate estimate of the wave head starting point (headStartPoint_M).

[0045] (4) Repeat the above process for the other end to obtain a more accurate estimate of the wave head starting point (headStartPoint_N) of the fault traveling wave at that end.

[0046] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments of the specification.

[0047] like Figure 1 The overall process framework of this embodiment is shown in FIG. Figure 2 As shown, the main test line is a line from DTU-82 to DTU_83 with a length of 5km. A two-terminal traveling wave signal for determining the fault distance is generated by a traveling wave signal generator, and appropriate white noise is added. 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 main station for processing; the processing method used 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.

[0048] Step (2): The two-terminal three-phase fault current obtained in step (1) is subjected to phase mode transformation (Karenbein transformation) to obtain the fault traveling wave line mode current components at both ends of MN (MN corresponds to DTU-82 and DTU-83 respectively) as the processing object. The data is as follows: Figure 8 shown.

[0049] Step 3: Perform wavelet transform on the line modulus current component of the double-terminal fault traveling wave, and screen the modulus extreme values ​​of the coefficient vectors of the second scale high and low frequency components respectively. The results are 500 and 497. Further, the calibration results of the starting point of the double-terminal traveling wave head are obtained by smoothing the threshold points, 498 and 494. The double-terminal wavelet decomposition components and modulus waveforms are as follows: Figure 3 , 4 and 5 and 6; the results of mode extreme value screening and threshold calibration on the second scale low frequency component are shown in Figure 7 As shown; the final wave head punctuation on the original signal is shown as Figure 8 .

[0050] Step 4: Set the fault wave velocity to V=2.93e8m / s based on the line material, and calculate the fault distance to be L_M =4032m, L_N = 968m with an error of 32m.

[0051] To sum up the above steps, in the modulus extreme value screening and smoothing threshold card points, there are certain differences between the appropriate modulus extreme value points and the threshold card points, but the threshold card points are closer to the real wave head starting point. The ranging results also further prove this conclusion. At the same time, this case shows that the modulus extreme value point only corresponds to the fastest changing point of the wave head, and the wave head inflection point is closer to the real wave head starting point.

[0052] Compared with the prior art, the present invention has the following advantages: The present invention organizes the overall structure based on the principle of distributed collection and master station calculation, which is suitable for the distribution characteristics of the distribution network, automatically collects three-phase current traveling waves, sends waveform data, and obtains accurate ranging effect through master station analysis; The present invention proposes a gradual progressive estimation calibration principle and screening method for the starting point of a fault traveling wave head based on modulus maximum and adaptive threshold, which can obtain a calibration point closer to the actual starting point of the traveling wave head when the signal-to-noise ratio of the traveling wave signal is small, and achieve better ranging effect and accuracy. At the same time, the algorithm overcomes the problems of being unable to identify or having large errors in the case of wave head distortion and gentle steepness, and has strong applicability in distribution network traveling wave moment measurement.

[0053] 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.

[0054] 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.

[0055] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer is run on a computer, the computer executes any of the two-terminal traveling wave ranging wave head calibration methods applicable to a distribution network in the above embodiments.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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 wave head calibration method suitable for distribution network, characterized in that: The following steps are included: S1: The three-phase fault current traveling wave sampling sequence is collected through the mutual inductor, i.e., the first 200 points and the last 800 points of the fault traveling wave at both ends of the line M and N, i.e., a total of 1000 points: Ia, Ib, Ic, with a sampling rate of 1Mhz; S2: Perform phase mode transformation on the sampling sequence in S1: (I0, I1, I2) = A(Ia, Ib, Ic)', where A is the phase mode transformation matrix, I0 is the zero mode component, and I1 and I2 are line mode components respectively; Select I1 as the fault modulus, and the fault moduli at both ends of M and N are recorded as: IM, IN; 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, the characteristic frequency band is set to 10khz~200khz, and the respective modulus extreme points are selected as the preliminary estimation of the starting points of the wave heads at both ends; S5: Based on the preliminary estimation of the wave head starting points at both ends, a smoothing threshold is constructed and a more accurate wave head starting point estimation is screened; S6: The time difference of the fault wave head reaching both ends is calculated by the difference between the more accurate wave head starting point estimation at both ends of M and N and the waveform recording triggering time at both ends of M and N. , the distances from the fault point to the M and N terminals are obtained by the distance measurement formula: L is the line distance between the two ends of M and N, and v is the speed of the fault traveling wave.

2. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 1 is characterized in that: For the selection of transformation matrix in phase mode transformation in S2, under the premise that the fault phase has been determined, the commonly used phase mode transformation includes Karen Bell transformation matrix A=(1,1,1; 1,-1,0; 1,0,-1). In the case of uncertain fault phase, the improved phase mode transformation matrix A=(1,1,1; 1,-5,4; 1, 4,-5) is used.

3. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 1 is characterized in that: In the wavelet transform process of S3, for the fault modulus S(n), S(n) represents the fault modulus to be processed: IM or IN, the following discrete binary spline wavelet transform is used: 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, and They are respectively the high-pass and low-pass filter group vectors at the j-1 scale, and are and By gradually inserting zeros at alternate points, is the compensation factor; The specific implementation of wavelet decomposition is based on the stationary spline wavelet transform of the à Trous framework. The specific signal space decomposition is expressed by the following schematic formula: The fault modulus S(n) in the above schematic formula 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 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 the paired combination of high and low frequency coefficients of the subspace at a certain scale.

4. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 3 is characterized in that: 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. The specific implementation of the wavelet component coefficient modulus is to obtain local 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.

5. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 3 is characterized in that: For the mode extreme point screening process in S4, the screening scheme starts from the scale corresponding to the characteristic frequency band and traces back in reverse order to the cutoff scale j=2.

6. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 3 is characterized in that: The steps for selecting the extreme value points in S4 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)}, where floor{} means rounding up the variables in the brackets, and the processing object is the modulus of the high-frequency component coefficients of each scale in the modulus matrix. ; (2) Starting from the Rth scale high frequency component, search element by element sequentially, where R = r + 1, with Rat max_R is the threshold, the proportional coefficient Rat=0.35, max_R is the maximum absolute value of the high-frequency component amplitude of this scale, and the point number corresponding to the first maximum or minimum value of the high-frequency component element of this scale whose absolute value is greater than the threshold is sequentially searched. This point number is used as the modulus extreme point of this scale, recorded as MaxModPoint_R; (3) In the upward scale R-1, based on the range of plus or minus 2 of the point MaxModPoint_R, sequentially search for elements whose absolute value exceeds the threshold Rat max_R-1, the first modulus extreme value and corresponding point with the same sign as MaxModPoint_R, record this point as the modulus extreme value point of this scale: MaxModPoint_(R-1), if the search is empty, record MaxModPoint_(R-1) =MaxModPoint_R; (4) Enter the next higher scale and repeat the search mechanisms of (2) and (3) until the second scale stops and the next higher scale is reached. The overall modulus extreme point is recorded as MaxModPoint = MaxModPoint_2, where MaxModPoint_2 is the modulus extreme point at the second scale search. (5) The modulus extreme points that meet the requirements at both ends of M and N are recorded as MaxModPoint_M and MaxModPoint_N respectively.

7. The double-terminal traveling wave ranging wave head calibration method applicable to the distribution network according to claim 6 is characterized in that: For S5, constructing a smoothing threshold and screening for a more accurate estimation of the wave head starting point includes the following steps: (1) Obtain a more accurate estimation of the wave head starting point for the M end. Based on the selected modulus extreme point MaxModPoint = MaxModPoint_M, determine the wave head front point beforePoint = min(MaxModPoint / 2, hardStarPoint-50) with reference to the hardware starting point. The processing object is the coefficient vector of the low-frequency component of the modulus matrix corresponding to the scale of the characteristic frequency band; (2) Determine the smoothing threshold smoothThd based on the waveform before the wavefront point, set the smoothing start point startPoint=50, and set the smoothing bandwidth smoothWidth=20; (2.1) For each point from the starting point startPoint to beforePoint, calculate the difference between the amplitude of the point and the average amplitude of the points in smoothWidth before this point, record the corresponding absolute value of the difference, and form a difference sequence; (2.2) The maximum value of this difference sequence is counted as maxValue, and relaxation is performed. The relaxation coefficient is selected as 1.2, and the threshold smoothThd = 1.2 is generated. maxValue; (3) Based on the threshold smoothThd, within the point range (beforePoint, MaxModPoint), compare the amplitudes point by point in reverse order, and obtain the first point that exceeds the threshold as a more accurate estimate of the wave head starting point headStartPoint_M; (4) Repeat the above process for the other end to obtain a more accurate estimate of the wave head starting point headStartPoint_N of the fault traveling wave at that end.

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

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 perform the steps of the method according to any one of claims 1 to 7.

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