Fault location method and system based on adaptive scale wavelet

Abstract

The invention discloses a fault location method and system based on adaptive scale wavelets, and the method comprises the steps: carrying out the discretization of a preset wavelet basis, and generating an adaptive scale wavelet library; based on the wavelet of each scale in the wavelet library, single-layer discrete wavelet decomposition is carried out on the independent fault signal, and a high-frequency coefficient corresponding to each scale is extracted; calculating a corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficient corresponding to each scale; and determining an optimal scale based on the energy-entropy comprehensive evaluation index, and carrying out fault distance measurement and calculation through the optimal scale. According to the fault distance measurement method and system based on the adaptive scale wavelet provided by the technical scheme of the invention, rapid and accurate fault location distance measurement is realized.

Description

Technical Field

[0001] This invention relates to the field of power system fault location technology, and more specifically, to a fault location method and system based on adaptive scale wavelet. Background Technology

[0002] In DC transmission lines, the speed and accuracy of fault location directly determine the safe and reliable operation of the system. Traditional fault location methods often employ fixed wavelet bases and perform wavelet analysis on a single scale. Fixed-scale wavelet filters can only capture fault wave signals within a preset frequency band, and noise interference can increase wavefront detection errors. Secondly, signals on long-distance lines experience attenuation and high-frequency noise superposition during transmission. Traditional methods often rely on empirical thresholds for wavefront discrimination, and the selection of these thresholds lacks adaptability. Thirdly, existing technologies often use a single energy or amplitude index to evaluate the effectiveness of wavelet coefficients, focusing only on signal strength while ignoring the sparsity of the coefficient distribution. This makes it difficult to distinguish between the true wavefront and noise peaks even under high noise levels. Summary of the Invention

[0003] The present invention provides a fault location method and system based on adaptive scale wavelet to solve the problem of how to improve the accuracy and reliability of fault location in DC transmission lines.

[0004] To address the above problems, this invention provides a fault location method based on adaptive scaling wavelet, the method comprising:

[0005] Based on the discretization of the preset wavelet basis, an adaptive-scale wavelet library is generated.

[0006] Based on wavelets at each scale in the wavelet library, single-level discrete wavelet decomposition is performed on independent fault signals to extract high-frequency coefficients corresponding to each scale.

[0007] Based on the high-frequency coefficients corresponding to each scale, the corresponding energy-entropy comprehensive evaluation index is calculated.

[0008] The optimal scale is determined based on the energy-entropy comprehensive evaluation index, and the fault distance is calculated using the optimal scale.

[0009] Preferably, generating an adaptive-scale wavelet library based on a preset wavelet basis includes:

[0010] Discretize the wavelet basis:

[0011]

[0012] Among them, T s =1 / f s Let f be the sampling period, K be the discrete length of the mother wavelet, and f' be the sampling period.s Here, k is the sampling frequency, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet basis.

[0013] Scaling the discretized wavelet basis:

[0014]

[0015] When α>1,

[0016]

[0017] When 0 < α < 1,

[0018]

[0019] Among them, h α [k] represents the scale wavelet coefficients, and α is the scale factor. For scaled integer extraction coefficients, This is the expansion step size after rounding up.

[0020] Preferably, the step of performing single-level discrete wavelet decomposition on independent fault signals based on wavelets at each scale in the wavelet library, and extracting the high-frequency coefficients corresponding to each scale, includes:

[0021] Based on wavelets at each scale α, a single-level discrete wavelet decomposition is performed on the independent fault signal to extract the high-frequency coefficients d corresponding to each scale. α [m]:

[0022]

[0023] Where h[k] is the k-th sampling coefficient, x is the fault signal sampling point, 2m+k is the sampling point index, m is the high-frequency coefficient index, N is the fault signal length, and L α The effective length of the scale-adaptive wavelet filter.

[0024] Preferably, the step of calculating the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale includes:

[0025]

[0026] Where E(α) is the energy, M is the total number of high-frequency coefficients, and p m (α) is the energy probability of the m-th high-frequency coefficient, H(α) is the scale entropy, and w E w is the energy weighting coefficient. H J(α) is the entropy weighting coefficient, and J(α) is the comprehensive evaluation index.

[0027] Preferably, the step of determining the optimal scale based on the energy-entropy comprehensive evaluation index and calculating the fault distance using the optimal scale includes:

[0028]

[0029] d = v(Δt) / 2.

[0030] Where E(α) is the energy, M is the total number of high-frequency coefficients, and p m (α) is the energy probability of the m-th high-frequency coefficient, H(α) is the scale entropy, and w E w is the energy weighting coefficient. H J(α) is the entropy weighting coefficient, and J(α) is the comprehensive evaluation index.

[0031] According to another aspect of the present invention, the present invention provides a fault location system based on adaptive scaling wavelet, the system comprising:

[0032] The generation unit is used to generate an adaptive-scale wavelet library based on the discretization of a preset wavelet basis;

[0033] The extraction unit is used to perform single-level discrete wavelet decomposition on independent fault signals based on wavelets of each scale in the wavelet library, and extract the high-frequency coefficients corresponding to each scale.

[0034] The computing unit is used to calculate the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale.

[0035] The result unit is used to determine the optimal scale based on the energy-entropy comprehensive evaluation index, and to calculate the fault distance using the optimal scale.

[0036] Preferably, the generation unit is used to generate an adaptive-scale wavelet library based on a preset wavelet basis, comprising:

[0037] Discretize the wavelet basis:

[0038]

[0039] Among them, T s =1 / f s Let f be the sampling period, K be the discrete length of the mother wavelet, and f' be the sampling period. s Here, k is the sampling frequency, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet basis.

[0040] Scaling the discretized wavelet basis:

[0041]

[0042] When α>1,

[0043]

[0044] When 0 < α < 1,

[0045]

[0046] Among them, h α [k] represents the scale wavelet coefficients, and α is the scale factor. For scaled integer extraction coefficients, This is the expansion step size after rounding up.

[0047] Preferably, the extraction unit is used to perform single-level discrete wavelet decomposition on independent fault signals based on wavelets of each scale in the wavelet library, and extract the high-frequency coefficients corresponding to each scale, including:

[0048] Based on wavelets at each scale α, a single-level discrete wavelet decomposition is performed on the independent fault signal to extract the high-frequency coefficients d corresponding to each scale. α [m]:

[0049]

[0050] Where h[k] is the k-th sampling coefficient, x is the fault signal sampling point, 2m+k is the sampling point index, m is the high-frequency coefficient index, N is the fault signal length, and L α The effective length of the scale-adaptive wavelet filter.

[0051] Preferably, the computing unit is used to calculate the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale, including:

[0052]

[0053]

[0054] Where E(α) is the energy, M is the total number of high-frequency coefficients, and p m (α) is the energy probability of the m-th high-frequency coefficient, H(α) is the scale entropy, and w E w is the energy weighting coefficient. H H(α) is the entropy weighting coefficient, and H(α) is the comprehensive evaluation index.

[0055] Preferably, the result unit is used to determine the optimal scale based on the energy-entropy comprehensive evaluation index, and to calculate the fault distance using the optimal scale, including:

[0056]

[0057] d = v(Δt) / 2.

[0058] Where, α * This is the optimal scale. The optimal scaling factor is denoted by v, where v is the wave velocity and Δt is the time difference between the arrival times of the fault wave and the distance measuring devices at both ends of the line.

[0059] This invention provides a fault location method and system based on adaptive scale wavelets. The method includes: discretizing a preset wavelet basis to generate an adaptive scale wavelet library; performing single-level discrete wavelet decomposition on independent fault signals based on wavelets of each scale in the wavelet library, extracting high-frequency coefficients corresponding to each scale; calculating the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients of each scale; determining the optimal scale based on the energy-entropy comprehensive evaluation index, and calculating the fault distance using the optimal scale. This invention provides a fault location method and system based on adaptive scale wavelets, achieving rapid and accurate fault location measurement. Attached Figure Description

[0060] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0061] Figure 1 A flowchart of a fault location method based on adaptive scaling wavelet according to a preferred embodiment of the present invention; and

[0062] Figure 2 This is a system structure diagram of a fault location method based on adaptive scale wavelet according to a preferred embodiment of the present invention. Detailed Implementation

[0063] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0064] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0065] Figure 1 This is a flowchart of a fault location method based on adaptive scaling wavelet according to a preferred embodiment of the present invention.

[0066] This invention provides a traveling wave ranging method and system based on adaptive scale wavelets to achieve a ranging scheme for rapid and accurate fault location.

[0067] like Figure 1 As shown, this invention provides a fault location method based on adaptive scaling wavelets, the method comprising:

[0068] Step 101: Based on the discretization of the preset wavelet basis, generate an adaptive-scale wavelet library;

[0069] Preferably, an adaptive-scale wavelet library is generated based on a preset wavelet basis, including:

[0070] Discretize the wavelet basis:

[0071]

[0072] Among them, T s =1 / f s Let f be the sampling period, K be the discrete length of the mother wavelet, and f' be the sampling period. s Here, k is the sampling frequency, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet basis.

[0073] Scaling the discretized wavelet basis:

[0074]

[0075] When α>1,

[0076]

[0077] When 0 < α < 1,

[0078]

[0079] Among them, h α [k] represents the scale wavelet coefficients, and α is the scale factor. For scaled integer extraction coefficients, This is the expansion step size after rounding up.

[0080] This invention generates an adaptive scaled wavelet library based on wavelet bases, including wavelet base discretization:

[0081]

[0082] Among them, T s =1 / f s Where K is the sampling period, and K is the discrete length of the mother wavelet;

[0083] Wavelet scaling:

[0084] Among them, h α [k] represents the scale wavelet coefficients, and α is the scale factor. For scaled integer extraction coefficients, This is the expansion step size after rounding up.

[0085]

[0086] When α>1,

[0087]

[0088] When 0 < α < 1,

[0089]

[0090] Step 102: Perform single-level discrete wavelet decomposition on the independent fault signals at each scale in the wavelet library, and extract the high-frequency coefficients corresponding to each scale;

[0091] Preferably, single-level discrete wavelet decomposition is performed on the independent fault signals at each scale in the wavelet library to extract the high-frequency coefficients corresponding to each scale, including:

[0092] Perform single-level discrete wavelet decomposition on the independent fault signal at each scale α, and extract the high-frequency coefficients d corresponding to each scale. α [m]:

[0093]

[0094] Where h[k] is the k-th sampling coefficient, x is the fault signal sampling point, 2m+k is the sampling point index, m is the high-frequency coefficient index, N is the fault signal length, and L α The effective length of the scale-adaptive wavelet filter.

[0095] This invention performs single-level discrete wavelet decomposition on each scale in the adaptive scaling wavelet library to obtain high-frequency coefficients, including:

[0096] For each scale α i Perform single-level discrete wavelet decomposition to obtain high-frequency coefficients.

[0097]

[0098] Step 103: Calculate the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale;

[0099] Preferably, based on the high-frequency coefficients corresponding to each scale, the corresponding energy-entropy comprehensive evaluation index is calculated, including:

[0100]

[0101] Where E(α) is the energy, M is the total number of high-frequency coefficients, and p m (α) is the energy probability of the m-th high-frequency coefficient, H(α) is the scale entropy, and w E w is the energy weighting coefficient. H J(α) is the entropy weighting coefficient, and J(α) is the comprehensive evaluation index.

[0102] This invention calculates a comprehensive energy-entropy evaluation index for each scale, including:

[0103] energy

[0104] Normalized energy distribution

[0105] entropy

[0106] Comprehensive evaluation indicators

[0107] p m (α) represents the proportion of the energy of the m-th high-frequency coefficient in the total energy at this scale; w E This is to normalize the total energy to the average energy of each coefficient.

[0108] Step 104: Determine the optimal scale based on the energy-entropy comprehensive evaluation index, and calculate the fault distance using the optimal scale.

[0109] Preferably, the optimal scale is determined based on the energy-entropy comprehensive evaluation index, and the fault distance is calculated using the optimal scale, including:

[0110]

[0111] d = v(Δt) / 2.

[0112] Where, α * This is the optimal scale. The optimal scaling factor is denoted by v, where v is the wave velocity and Δt is the time difference between the arrival times of the fault wave and the distance measuring devices at both ends of the line. The goal is to find the α that minimizes J(α) from a given set of candidate scale factors (all possible scale factors).

[0113] This invention selects the optimal scale based on comprehensive evaluation indicators and uses the optimal scale to complete the fault distance calculation, including:

[0114] Selecting the optimal scale

[0115] Calculate the fault distance: d = v(Δt) / 2.

[0116] This invention discloses a fault location method and system based on adaptive scale wavelets, comprising: generating an adaptive scale wavelet library based on a wavelet basis; performing single-level discrete wavelet decomposition on each scale in the adaptive scale wavelet library to obtain high-frequency coefficients; calculating an energy-entropy comprehensive evaluation index for each scale; selecting the optimal scale based on the comprehensive evaluation index, and using the optimal scale to complete the fault distance measurement.

[0117] This invention selects the optimal scale through a comprehensive evaluation of energy and entropy, accurately captures the characteristics of fault traveling waves, and reduces ranging errors caused by interference. This invention uses single-layer discrete wavelet decomposition to avoid redundant calculations of multi-layer decomposition, and combines an adaptive scale library to reduce invalid analysis and achieve rapid ranging. The adaptive scale wavelet library of this invention covers fault characteristics in multiple scenarios, can adapt to different line parameters and fault types, and improves ranging stability under complex working conditions.

[0118] Figure 2 This is a system structure diagram of a fault location method based on adaptive scale wavelet according to a preferred embodiment of the present invention.

[0119] like Figure 2 As shown, this invention provides a fault location system based on adaptive scale wavelets, the system comprising:

[0120] The generation unit 201 is used to generate an adaptive-scale wavelet library based on the discretization of a preset wavelet basis.

[0121] Preferably, the generation unit 201 is used to generate an adaptive-scale wavelet library based on a preset wavelet basis, including:

[0122] Discretize the wavelet basis:

[0123]

[0124] Among them, T s =1 / f s Let f be the sampling period, K be the discrete length of the mother wavelet, and f' be the sampling period. s Here, k is the sampling frequency, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet basis.

[0125] Scaling the discretized wavelet basis:

[0126]

[0127] When α>1,

[0128]

[0129] When 0 < α < 1,

[0130]

[0131] Among them, h α [k] represents the scale wavelet coefficients, and α is the scale factor. For scaled integer extraction coefficients, This is the expansion step size after rounding up.

[0132] Extraction unit 202 is used to perform single-level discrete wavelet decomposition on independent fault signals based on wavelets of each scale in the wavelet library, and extract the high-frequency coefficients corresponding to each scale.

[0133] Preferably, the extraction unit 202 is used to perform single-level discrete wavelet decomposition on the independent fault signal based on wavelets at each scale in the wavelet library, and extract the high-frequency coefficients corresponding to each scale, including:

[0134] Based on wavelets at each scale α, a single-level discrete wavelet decomposition is performed on the independent fault signal to extract the high-frequency coefficients d corresponding to each scale. α [m]:

[0135]

[0136] Where h[k] is the k-th sampling coefficient, x is the fault signal sampling point, 2m+k is the sampling point index, m is the high-frequency coefficient index, N is the fault signal length, and L α The effective length of the scale-adaptive wavelet filter.

[0137] The calculation unit 203 is used to calculate the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale.

[0138] Preferably, the calculation unit 203 is used to calculate the corresponding energy-entropy comprehensive evaluation index based on the high-frequency coefficients corresponding to each scale, including:

[0139]

[0140] Where E(α) is the energy, M is the total number of high-frequency coefficients, and p m (α) is the energy probability of the m-th high-frequency coefficient, H(α) is the scale entropy, and w E w is the energy weighting coefficient. H J(α) is the entropy weighting coefficient, and J(α) is the comprehensive evaluation index.

[0141] Result unit 204 is used to determine the optimal scale based on the energy-entropy comprehensive evaluation index and to calculate the fault distance using the optimal scale.

[0142] Preferably, the result unit 204 is used to determine the optimal scale based on the energy-entropy comprehensive evaluation index, and to calculate the fault distance using the optimal scale, including:

[0143]

[0144] d = v(Δt) / 2.

[0145] Where, α * This is the optimal scale. The optimal scaling factor is denoted by v, where v is the wave velocity and Δt is the time difference between the arrival times of the fault wave and the distance measuring devices at both ends of the line.

[0146] The fault location system based on adaptive scale wavelet of the present invention corresponds to the fault location method based on adaptive scale wavelet of another preferred embodiment of the present invention, and will not be described again here.

[0147] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0148] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0152] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0153] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0154] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1.A method for fault location based on adaptive scale wavelet, the method comprising: generating a wavelet library of adaptive scale based on discretization of a preset wavelet basis; performing single-layer discrete wavelet decomposition on an independent fault signal based on each scale wavelet in the wavelet library to extract high-frequency coefficients corresponding to each scale; calculating an energy-entropy comprehensive evaluation index corresponding to each scale based on the high-frequency coefficients; determining an optimal scale based on the energy-entropy comprehensive evaluation index and calculating fault distance by using the optimal scale. 2.The method of claim 1, wherein the generating a wavelet library of adaptive scale based on a preset wavelet basis comprises: discretizing the wavelet basis; where T s = 1 / f s is the sampling period, K is the discrete length of the mother wavelet, f s is the sampling frequency, k is the sampling point, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet base. scaling the discretized wavelet basis; when α>1, when 0<α<1, wherein h α [k] is a scale wavelet coefficient, a is a scale factor, is a scale integer decimation coefficient, is a rounding step. 3.The method of claim 2, wherein the performing single-layer discrete wavelet decomposition on an independent fault signal based on each scale wavelet in the wavelet library to extract high-frequency coefficients corresponding to each scale comprises: Based on each scale α wavelet, the single-layer discrete wavelet decomposition is performed on the independent fault signal, and the high-frequency coefficient d corresponding to each scale is extracted α [m]: where h[k] is the kth sample coefficient, x is the sampling point of the fault signal, 2m+k is the sampling point index, m is the high frequency coefficient index, N is the length of the fault signal, L α is the effective length of the scale adaptive wavelet filter. 4.The method of claim 3, wherein the calculating an energy-entropy comprehensive evaluation index corresponding to each scale based on the high-frequency coefficients comprises: wherein E(α) is the energy, M is the total number of high frequency coefficients, p m (α) is the energy probability of the mth high frequency coefficient, H(α) is the scale entropy, w E is the energy weight coefficient, w H is the entropy weight coefficient, and J(α) is the comprehensive evaluation index. 5.The method of claim 4, wherein the determining an optimal scale based on the energy-entropy comprehensive evaluation index and calculating fault distance by using the optimal scale comprises: d=v(Δt) / 2. wherein α * is the optimal scale, is the optimal scale solution factor, v is the wave velocity, and Δt is the time difference between the arrival times of the fault wave at the two end-of-line distance-measuring devices. 6.A system for fault location based on adaptive scale wavelet, the system comprising: a generating unit configured to generate a wavelet library of adaptive scale based on discretization of a preset wavelet basis; an extracting unit configured to perform single-layer discrete wavelet decomposition on an independent fault signal based on each scale wavelet in the wavelet library to extract high-frequency coefficients corresponding to each scale; a calculating unit configured to calculate an energy-entropy comprehensive evaluation index corresponding to each scale based on the high-frequency coefficients; a result unit configured to determine an optimal scale based on the energy-entropy comprehensive evaluation index and calculate fault distance by using the optimal scale. 7.The system of claim 6, wherein the generating unit configured to generate a wavelet library of adaptive scale based on a preset wavelet basis comprises: discretizing the wavelet basis; where T s = 1 / f s is the sampling period, K is the discrete length of the mother wavelet, f s is the sampling frequency, k is the sampling point, ψ() is the continuous wavelet function, and h0[k] is the discrete coefficient of the wavelet base. scaling the discretized wavelet basis; when α>1, when 0<α<1, wherein h α [k] is a scaling wavelet coefficient, a is a scaling factor, is a scaling integer decimation coefficient, is a scaling step after rounding up. 8.The system of claim 7, wherein the extracting unit configured to perform single-layer discrete wavelet decomposition on an independent fault signal based on each scale wavelet in the wavelet library to extract high-frequency coefficients corresponding to each scale comprises: Based on each scale α wavelet, the single-layer discrete wavelet decomposition is performed on the independent fault signal, and the high-frequency coefficient d corresponding to each scale is extracted α [m]: where h[k] is the kth sample coefficient, x is the fault signal sample point, 2m+k is the sample point index, m is the high frequency coefficient index, N is the length of the fault signal, L α is the effective length of the scale adaptive wavelet filter. 9.The system of claim 8, wherein the calculating unit configured to calculate an energy-entropy comprehensive evaluation index corresponding to each scale based on the high-frequency coefficients comprises: wherein E(a) is the energy, M is the total number of high frequency coefficients, p m (a) is the energy probability of the mth high frequency coefficient, H(a) is the scale entropy, w E is the energy weight coefficient, w H is the entropy weight coefficient, and J(a) is the comprehensive evaluation index. 10.The system of claim 9, wherein the result unit configured to determine an optimal scale based on the energy-entropy comprehensive evaluation index and calculate fault distance by using the optimal scale comprises: d=v(Δt) / 2. wherein a * is the optimal scale, is the optimal scale solution factor, v is the wave velocity, and Δt is the time difference between the arrival times of the fault wave at the two end-of-line distance-measuring devices.

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