CN117723894A - Fault detection method and device based on weak traveling wave signal feature extraction - Google Patents
Fault detection method and device based on weak traveling wave signal feature extraction Download PDFInfo
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Abstract
The application relates to a fault detection method and device based on weak traveling wave signal feature extraction. The method comprises the following steps: singular value decomposition is carried out on the weak traveling wave signals corresponding to the target transmission line, so that a decomposition result is obtained, and the weak traveling wave signals are converted into noise-reduced signals according to the decomposition result; converting the noise-reduced signal into a frequency domain signal based on Fourier transform, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal; determining signal characteristic information corresponding to a signal to be detected according to a preset energy operator; and determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information. The method can be used for carrying out singular value decomposition and empirical wavelet transformation on the weak traveling wave signals of the power transmission line, and determining the signal characteristics of the traveling wave signals by combining an energy operator, so that the fault detection result of the power transmission line is accurately analyzed based on the signal characteristics of the traveling wave signals, and the accuracy of the fault detection result of the power transmission line is improved.
Description
Technical Field
The present disclosure relates to the field of power protection technologies, and in particular, to a fault detection method, apparatus, computer device, storage medium, and computer program product based on weak traveling wave signal feature extraction.
Background
In the electric power field, a power distribution network commonly adopts an inefficient grounding mode such as non-grounding of a neutral point, grounding through an arc suppression coil and the like. The power distribution network goes deep into a user terminal, the operation and fault conditions are complex and changeable, particularly, for high-resistance ground faults (such as branch line, cross arm ground, asphalt or concrete pavement falling of wires and the like) frequently occurring in the power distribution network, the fault characteristics are very weak, and the detection and treatment of the high-resistance ground faults are influenced by factors such as unstable electric arcs, load harmonic interference and the like.
The traditional technology mainly utilizes a certain scale after VMD or EMD decomposition to calibrate a fault traveling wave head, and then utilizes a wavelet mode maximum value or TEO calibrated traveling wave head to bring the arrival time to the accurate position of a calculated fault point, however, the traditional technology has poor self-adaptability, the wavelet basis function types and different decomposition scales can bring great difference to the calculated result, if the traveling wave signal contains noise interference, the accuracy of the detection result obtained by the traditional technology is reduced, and the accuracy of the transmission line fault detection result is not improved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a fault detection method, apparatus, computer device, computer-readable storage medium, and computer program product based on weak traveling wave signal feature extraction that can improve accuracy of transmission line fault detection results.
In a first aspect, the present application provides a fault detection method based on weak traveling wave signal feature extraction, including:
singular value decomposition is carried out on a weak traveling wave signal corresponding to a target power transmission line, a decomposition result is obtained, and the weak traveling wave signal is converted into a noise-reduced signal according to the decomposition result;
converting the noise-reduced signal into a frequency domain signal based on Fourier transform, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal;
determining signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information comprises frequency information and amplitude information;
and determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
In one embodiment, performing singular value decomposition on the weak traveling wave signal corresponding to the target power transmission line to obtain a decomposition result includes:
Converting the weak traveling wave signals into a matrix to be decomposed according to a preset matrix conversion mode;
and performing singular value decomposition on the matrix to be decomposed to obtain the decomposition result.
In one embodiment, the converting the weak traveling wave signal into the noise-reduced signal according to the decomposition result includes:
determining a Hanker matrix corresponding to the weak traveling wave signal according to the decomposition result;
removing a Hanker matrix of the noise signal from the Hanker matrix to obtain a de-noised Hanker matrix corresponding to the weak traveling wave signal;
and converting the de-noised Hank matrix into the de-noised signal according to a preset matrix conversion mode.
In one embodiment, the decomposing the noise-reduced signal into a signal to be detected according to the frequency domain signal based on the empirical wavelet transform includes:
dividing the frequency domain signal into at least one signal segment, obtaining a local minimum value of each signal segment, and determining the midpoint of the adjacent maximum value of each signal segment according to the local minimum value;
determining an empirical scale function and an empirical wavelet function corresponding to each signal segment according to the midpoint of the adjacent maximum;
Determining an approximation coefficient according to the empirical scale function and the weak traveling wave signal, and determining a detail coefficient according to the empirical wavelet function and the weak traveling wave signal;
and converting the weak traveling wave signal into the signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function.
In one embodiment, the determining the midpoint of the adjacent maxima of each signal segment according to the local minima includes:
for any signal segment in each signal segment, determining adjacent maximum values at two sides of the local minimum value in the any signal segment according to the local minimum value corresponding to the any signal segment;
taking the midpoint of the adjacent maximum value between corresponding points in any signal segment as the midpoint of the adjacent maximum value of any signal segment;
and determining the midpoint of the adjacent maximum value of each signal segment according to the midpoint of the adjacent maximum value of any signal segment.
In one embodiment, the converting the weak traveling wave signal into the signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function includes:
Determining a first product based on a product between the approximation coefficients and the empirical scale function;
determining a second product based on a product between the detail coefficient and the empirical wavelet function;
and determining the signal to be detected according to the sum between the first product and the second product.
In one embodiment, the determining, according to the signal characteristic information, a fault detection result corresponding to the weak traveling wave signal includes:
acquiring a fault traveling wave signal of the target power transmission line in a fault simulation experiment state;
analyzing the fault traveling wave signal to obtain fault analysis index information;
and comparing the fault analysis index information with the signal characteristic information to obtain a fault detection result corresponding to the weak traveling wave signal.
In a second aspect, the present application further provides a fault detection device based on weak traveling wave signal feature extraction, including:
the noise reduction module is used for carrying out singular value decomposition on the weak traveling wave signals corresponding to the target power transmission line to obtain a decomposition result, and converting the weak traveling wave signals into noise-reduced signals according to the decomposition result;
the decomposition module is used for converting the noise-reduced signal into a frequency domain signal based on Fourier transformation, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transformation according to the frequency domain signal;
The determining module is used for determining signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information comprises frequency information and amplitude information;
and the detection module is used for determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the steps of the method described above.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.
According to the fault detection method, the device, the computer equipment, the storage medium and the computer program product based on the weak traveling wave signal feature extraction, the singular value decomposition is carried out on the weak traveling wave signal corresponding to the target power transmission line to obtain a decomposition result, and the weak traveling wave signal is converted into the noise-reduced signal according to the decomposition result, so that the weak traveling wave signal corresponding to the target power transmission line is simplified through singular value decomposition, noise in the weak traveling wave signal corresponding to the target power transmission line is removed, and the influence of the noise on the fault detection accuracy is avoided; based on Fourier transform, converting the noise-reduced signal into a frequency domain signal, based on empirical wavelet transform, decomposing the noise-reduced signal into a signal to be detected according to the frequency domain signal, so that the noise is removed early, and meanwhile, decomposing the noise-reduced signal into a plurality of signal segments with local frequencies to adapt to different signal processing requirements, thereby improving the efficiency of signal analysis; according to a preset energy operator, determining signal characteristic information corresponding to the signal to be detected, so that the signal characteristic of the signal to be detected is accurately determined while denoising based on the energy operator; the signal characteristic information comprises frequency information and amplitude information; according to the signal characteristic information, a fault detection result corresponding to the weak traveling wave signal is determined, the weak traveling wave signal corresponding to the power transmission line can be subjected to noise reduction based on singular value decomposition, the weak traveling wave signal subjected to noise reduction is divided into a plurality of signal segments based on empirical wavelet transformation, the signal characteristics of the traveling wave signal are accurately determined based on an energy operator, the singular value decomposition and the empirical wavelet transformation of the weak traveling wave signal of the power transmission line are realized, the signal characteristics of the traveling wave signal are determined by combining the energy operator, and therefore the fault detection result of the power transmission line is accurately analyzed based on the signal characteristics of the traveling wave signal, and the accuracy of the fault detection result of the power transmission line is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is an application environment diagram of a fault detection method based on weak traveling wave signal feature extraction in one embodiment;
FIG. 2 is a schematic flow chart of a fault detection method based on weak traveling wave signal feature extraction in an embodiment;
FIG. 3 is a schematic diagram of an equivalent circuit of a single-phase ground fault of a distribution network connected to the ground via an arc suppression coil in one embodiment;
FIG. 4 is a schematic diagram of a distribution circuit in one embodiment;
FIG. 5 is a schematic time domain diagram of voltage traveling wave waveforms of different high-resistance ground fault points according to an embodiment;
FIG. 6 is a schematic diagram of a high-resistance ground fault model of a power distribution network in one embodiment;
FIG. 7 is an overall schematic of an arc high resistance ground fault current in one embodiment;
FIG. 8 is an enlarged schematic view of an arc high resistance ground fault current portion in one embodiment;
FIG. 9 is a schematic diagram of a signal feature in one embodiment;
FIG. 10 is a block diagram of a fault detection device based on weak traveling wave signal feature extraction in one embodiment;
FIG. 11 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The fault detection method based on weak traveling wave signal feature extraction, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein transmission line 102 communicates with server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 performs singular value decomposition on the weak traveling wave signals corresponding to the target transmission line to obtain a decomposition result, and converts the weak traveling wave signals into noise-reduced signals according to the decomposition result; the server 104 converts the noise-reduced signal into a frequency domain signal based on fourier transform, and decomposes the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal; the server 104 determines signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information comprises frequency information and amplitude information; the server 104 determines a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information. Wherein the transmission line 102 may be, but is not limited to, various distribution networks. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, a fault detection method based on weak traveling wave signal feature extraction is provided, and the method is taken as an example for describing application of a server, it is understood that the method can also be applied to a terminal, and can also be applied to a system including the terminal and the server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step S202, singular value decomposition is carried out on the weak traveling wave signals corresponding to the target transmission line, a decomposition result is obtained, and the weak traveling wave signals are converted into noise-reduced signals according to the decomposition result.
The target power transmission line may refer to a power distribution network in a power system, and in practical application, the power distribution network may include a medium voltage power distribution network.
The weak traveling wave signal may be information for representing a transmission state of a plane wave on a power transmission line, and in practical application, the weak traveling wave signal may include a weak traveling wave signal.
The singular value decomposition may refer to a matrix decomposition method in linear algebra.
The decomposition result may be information obtained by decomposing a matrix corresponding to the weak traveling wave signal.
The noise-reduced signal may be a signal obtained by reducing noise of a weak traveling wave signal corresponding to the target power transmission line according to a decomposition result.
As an example, the server determines a matrix to be decomposed corresponding to the weak traveling wave signal corresponding to the target power transmission line according to the weak traveling wave signal corresponding to the target power transmission line, the server performs singular value decomposition on the matrix to be decomposed to obtain a decomposition result corresponding to the matrix to be decomposed, and the server eliminates a noise signal from the weak traveling wave signal according to the decomposition result of the matrix to be decomposed to obtain a noise-reduced signal.
Step S204, converting the noise-reduced signal into a frequency domain signal based on Fourier transform, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal.
The frequency domain signal may be a signal obtained by performing fourier transform on the noise-reduced signal.
Among them, an Empirical Wavelet Transform (EWT) may refer to a method for signal decomposition that can decompose a signal into wavelet components of a plurality of local frequencies, thereby achieving efficient processing and analysis of the signal and adapting to different types of signals, capable of processing non-stationary signals and non-linear signals.
The signal to be detected may be a signal obtained by performing empirical wavelet transform on the noise-reduced signal.
As an example, the server converts the noise-reduced signal into a frequency domain signal based on fourier transform, the server decomposes the noise-reduced signal into a plurality of signal segments to be detected based on empirical wavelet transform, and the server generates the signal to be detected based on the plurality of signal segments to be detected.
Step S206, determining signal characteristic information corresponding to the signal to be detected according to a preset energy operator.
The preset energy operator may refer to a Teager energy operator, and in practical application, the Teager energy operator may be used for nonlinear operation of signal processing, and may extract edges and features of a signal.
The signal characteristic information may be information representing a value of the traveling wave signal in terms of frequency, amplitude, and the like, and in practical application, the signal characteristic information may include instantaneous frequency information and instantaneous amplitude information.
As an example, the server may process the signal to be detected by using an energy operator, track the energy of the weak traveling wave signal, extract an energy maximum point as the arrival time of the fault initial traveling wave head, specifically, the server converts the signal to be detected into a corresponding energy operator expression according to a preset energy operator, and determines, according to the energy operator expression, instantaneous frequency information and instantaneous amplitude information corresponding to the signal to be detected, specifically, taking the weak traveling wave signal x (t) of the target transmission line as an example, the server represents the weak traveling wave signal as:
。
The server can obtain the instantaneous frequency at the maximum energy of the weak traveling wave signal, and the instantaneous frequency can be expressed as:
。
the server can obtain the instantaneous amplitude of the weak traveling wave signal at the maximum energy, and the instantaneous amplitude can be expressed as:
。
step S208, determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
The fault detection result may refer to information indicating whether a fault exists in the power transmission line.
As an example, a server obtains a fault analysis index, and the server determines whether a fault exists in the power transmission line according to the matching degree between the signal characteristic information and the fault analysis index, so as to obtain a fault detection result corresponding to a weak traveling wave signal of the power transmission line.
In the fault detection method based on the weak traveling wave signal feature extraction, the weak traveling wave signal corresponding to the target power transmission line is subjected to singular value decomposition to obtain a decomposition result, and the weak traveling wave signal is converted into a noise-reduced signal according to the decomposition result, so that the weak traveling wave signal corresponding to the target power transmission line is simplified through singular value decomposition, noise in the weak traveling wave signal corresponding to the target power transmission line is removed, and the influence of noise on the fault detection accuracy is avoided; based on Fourier transform, converting the noise-reduced signal into a frequency domain signal, based on empirical wavelet transform, decomposing the noise-reduced signal into a signal to be detected according to the frequency domain signal, so that the noise is removed early, and meanwhile, decomposing the noise-reduced signal into a plurality of signal segments with local frequencies to adapt to different signal processing requirements, thereby improving the efficiency of signal analysis; according to a preset energy operator, determining signal characteristic information corresponding to the signal to be detected, so that the signal characteristic of the signal to be detected is accurately determined while denoising based on the energy operator; the signal characteristic information comprises frequency information and amplitude information; according to the signal characteristic information, a fault detection result corresponding to the weak traveling wave signal is determined, the weak traveling wave signal corresponding to the power transmission line can be subjected to noise reduction based on singular value decomposition, the weak traveling wave signal subjected to noise reduction is divided into a plurality of signal segments based on empirical wavelet transformation, the signal characteristics of the traveling wave signal are accurately determined based on an energy operator, the singular value decomposition and the empirical wavelet transformation of the weak traveling wave signal of the power transmission line are realized, the signal characteristics of the traveling wave signal are determined by combining the energy operator, and therefore the fault detection result of the power transmission line is accurately analyzed based on the signal characteristics of the traveling wave signal, and the accuracy of the fault detection result of the power transmission line is improved.
In an exemplary embodiment, singular value decomposition is performed on a weak traveling wave signal corresponding to a target power transmission line to obtain a decomposition result, including: converting weak traveling wave signals into a matrix to be decomposed according to a preset matrix conversion mode; singular value decomposition is carried out on the matrix to be decomposed, and a decomposition result is obtained.
The preset matrix conversion mode may refer to a mapping relationship between each data in the signal and each element in the matrix.
The matrix to be decomposed may be a matrix obtained by converting weak traveling wave signals into a matrix form according to a preset matrix conversion mode.
As an example, the weak traveling wave signal x= { x for the target transmission line 1 ，x 2 ，x 3 ，……，x n N=1, 2,3, … …, M, the server converts the weak travelling wave signal into a matrix D to be decomposed according to a preset matrix conversion mode, where the matrix D to be decomposed may be expressed as:
。
when m=m/2, singular Value Decomposition (SVD) is performed on the traveling wave signal by using a Hankel (Hankel) matrix, so that a good noise reduction effect is achieved.
The singular value decomposition of the matrix D to be decomposed is:
。
wherein A, B is an orthogonal matrix, B T For the transpose of B, Σ is a diagonal matrix, the diagonal elements of the diagonal matrix consist of singular values, and the singular values have the following relationship: beta 1 >β 2 >β 3 >… >β k >0, wherein beta k Can refer to the kth singular value, x, obtained by singular value decomposition i ，y i Is the ith column vector of matrix A, B, r is the rank of matrix D to be decomposed, and the server can compare the singular values beta k 、x i And y i As a result of the decomposition.
In this embodiment, the weak traveling wave signal is converted into the matrix to be decomposed according to a preset matrix conversion mode; singular value decomposition is carried out on the matrix to be decomposed to obtain a decomposition result, traveling wave signals of the power transmission line can be processed based on singular value decomposition, the data dimension is reduced, components with larger characteristic values are reserved, the accuracy of the decomposition result is improved, a data base is provided for noise reduction of subsequent traveling wave signals, and the accuracy of fault detection results of the power transmission line is further improved.
In some embodiments, according to the decomposition result, converting the weak traveling wave signal into a noise-reduced signal includes: determining a Hanker matrix corresponding to the weak traveling wave signal according to the decomposition result; removing the Hank matrix of the noise signal from the Hank matrix to obtain a de-noised Hank matrix corresponding to the weak traveling wave signal; and converting the de-noised Hank matrix into a de-noised signal according to a preset matrix conversion mode.
The hank matrix may refer to a matrix with equal elements on each secondary diagonal, and has wide application in the fields of digital signal processing, numerical calculation, system control, and the like.
The noise signal may be information that is used to characterize the weak traveling wave signal and is not related to the transmission line fault.
As an example, when a high-resistance fault is grounded on a power transmission line, since a fault transition resistance is as high as ten kiloohms, a generated fault initial traveling wave signal is weak, and the fault initial traveling wave signal is easily submerged in high-frequency noise, so that noise reduction treatment is required to be performed on the fault initial traveling wave signal (weak traveling wave signal of a target power transmission line), the server performs multiple singular value decomposition on the weak traveling wave signal x of the target power transmission line, and after J-1 decomposition, the approximate component pair structure is utilizedLayer J Hank matrix H J The J-th layer Hank matrix H J The matrix can be used as a Hank matrix corresponding to weak traveling wave signals, and the Hank matrix H of the J-th layer J Can be expressed as: h J =H s +H ε, where H s May refer to the hank matrix of normal/real (fault) traveling wave signals, and hε may refer to the hank matrix of noise signals, so that the server may respond to the hank matrix H from weak traveling wave signals J Removing H epsilon to obtain H s The matrix H s The de-noised hank matrix corresponding to the weak traveling wave signal can be used as the de-noised hank matrix corresponding to the weak traveling wave signal, and the server can refer to the process of converting the weak traveling wave signal into the matrix D to be decomposed according to a preset matrix conversion mode to restore the de-noised hank matrix corresponding to the weak traveling wave signal into the de-noised signal.
In the embodiment, a hanker matrix corresponding to the weak traveling wave signal is determined according to the decomposition result; removing the Hank matrix of the noise signal from the Hank matrix to obtain a de-noised Hank matrix corresponding to the weak traveling wave signal; according to a preset matrix conversion mode, the de-noised Hank matrix is converted into a de-noised signal, noise signals in the traveling wave signals can be gradually removed based on the Hank matrix, the influence of the noise signals on the subsequent signal characteristic information extraction and the power transmission line fault detection is avoided, and the accuracy of the power transmission line fault detection result is further improved.
In some embodiments, decomposing the denoised signal into a signal to be detected from the frequency domain signal based on an empirical wavelet transform comprises: dividing the frequency domain signal into at least one signal segment, obtaining local minimum values of the signal segments, and determining the midpoints of the adjacent maximum values of the signal segments according to the local minimum values; determining an empirical scale function and an empirical wavelet function corresponding to each signal segment according to the midpoints adjacent to the maximum value; determining an approximation coefficient according to the empirical scale function and the weak traveling wave signal, and determining a detail coefficient according to the empirical wavelet function and the weak traveling wave signal; and converting the weak traveling wave signal into a signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function.
The signal segment may be a signal obtained by dividing a frequency domain signal.
The local minimum may refer to a minimum of the frequency domain amplitude in each signal segment.
The midpoint near the maximum value may refer to a midpoint between points with the largest frequency amplitude in the areas on both sides of the local minimum value corresponding to each signal segment.
Where the empirical scale function may refer to a function describing the scaling of the travelling wave signal.
Where an empirical wavelet function may refer to a function that represents a traveling wave signal with an oscillating waveform of finite length or rapid decay.
The approximation coefficients may refer to information characterizing the overall trend or slow change of the traveling wave signal in the low frequency range, among other things.
The detail coefficients may refer to information characterizing the detail characteristics of the traveling wave signal in the high frequency range, among other things.
As an example, the server performs adaptive segmentation on the noise-reduced signal by using a fourier-level method to obtain a normalized fourier spectrum (frequency domain signal), the server segments the frequency domain signal into at least one signal segment, calculates a local minimum of each signal segment and an adjacent maximum midpoint ω corresponding to the local minimum, and uses the adjacent maximum midpoint ω as an edge support of the frequency domain signal, specifically, the server decomposes the frequency domain signal into N parts, and the adjacent maximum midpoint ω is a maximum midpoint of two adjacent signal segments, where ω 0 =0，ω n Pi, ω as midpoint and width T n =2τ n Each signal segment can be expressed as:
。
wherein, lambda n May refer to signal fragments.
Wherein, 。
the server then defines a bandpass filter for each signal segment by an empirical wavelet, and constructs an empirical scale function and an empirical wavelet function from the Meyer wavelet, where the empirical scale function can be expressed as:
。
wherein,may refer to an empirical scale function.
The empirical wavelet function can be expressed as:
。
wherein,may refer to an empirical wavelet function.
Wherein,。
wherein,。
the server determines an approximation coefficient according to an inner product between the empirical scale function and the weak traveling wave signal, the server determines a detail coefficient according to an inner product between the empirical wavelet function and the weak traveling wave signal, and the server converts the weak traveling wave signal into a signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function.
Wherein the approximation coefficients may be expressed as:
。
wherein,may refer to approximation coefficients.
Wherein the detail coefficients can be expressed as:
。
wherein,may refer to detail coefficients.
In this embodiment, the frequency domain signal is divided into at least one signal segment, so as to obtain local minimum values of each signal segment, and the midpoint of the adjacent maximum value of each signal segment is determined according to the local minimum values; determining an empirical scale function and an empirical wavelet function corresponding to each signal segment according to the midpoints adjacent to the maximum value; determining an approximation coefficient according to the empirical scale function and the weak traveling wave signal, and determining a detail coefficient according to the empirical wavelet function and the weak traveling wave signal; according to the approximation coefficient, the detail coefficient, the experience scale function and the experience wavelet function, the weak traveling wave signal is converted into a signal to be detected, and the frequency domain signal can be decomposed into wavelet components with a plurality of local frequencies, so that efficient processing and analysis of the signal are realized, the influence is caused on the subsequent signal characteristic information extraction and the transmission line fault detection, and the accuracy of the transmission line fault detection result is improved.
In some embodiments, determining the approximate maximum midpoint for each signal segment based on the local minima comprises: for any signal segment in each signal segment, determining the adjacent maximum values at two sides of the local minimum value in any signal segment according to the local minimum value corresponding to any signal segment; taking the midpoint of the adjacent maximum between corresponding points in any signal segment as the midpoint of the adjacent maximum of any signal segment; and determining the midpoint of the adjacent maximum value of each signal segment according to the midpoint of the adjacent maximum value of any signal segment.
As an example, taking any one of several signal segments a as an example, the signal segment a may be represented as { a } 1 ，A 2 ，A 3 ，A 4 ，A 5 ，A 6 ，A 7 If A 4 For the local minimum corresponding to signal segment A, then A 4 Signal segment a may be divided into two sets of data: { A 1 ，A 2 ，A 3 Sum { A } 5 ，A 6 ，A 7 If A 1 Is { A ] 1 ，A 2 ，A 3 Maximum value in }, A 6 Is { A ] 5 ，A 6 ，A 7 Maximum value in }, A 1 And A 6 Can be used as A 4 The server will A 1 And A 6 The midpoint between the corresponding points in the signal segment a is taken as the midpoint of the adjacent maximum value of the signal segment a, and the server can determine the midpoint of the adjacent maximum value of each signal segment by adopting the method.
In this embodiment, for any one of the signal segments, adjacent maximum values on both sides of the local minimum value in the any one signal segment are determined according to the local minimum value corresponding to the any one signal segment; taking the midpoint of the adjacent maximum between corresponding points in any signal segment as the midpoint of the adjacent maximum of any signal segment; and determining the adjacent maximum midpoints of each signal segment according to the adjacent maximum midpoints of any signal segment, analyzing each signal segment, determining the corresponding adjacent maximum midpoints of each signal segment, and providing a data basis for subsequent empirical wavelet transformation.
In some embodiments, converting the weak traveling wave signal into the signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function, and the empirical wavelet function includes: determining a first product based on the product between the approximation coefficient and the empirical scale function; determining a second product based on the product between the detail coefficient and the empirical wavelet function; and determining a signal to be detected according to the sum between the first product and the second product.
As an example, the server calculates a product between the approximation coefficient and the empirical scale function according to the approximation coefficient and the empirical scale function to obtain a first product, the server calculates a product between the detail coefficient and the empirical wavelet function according to the detail coefficient and the empirical wavelet function to obtain a second product, and the server sums the first product and the second product to obtain a signal to be detected, and specifically, the signal to be detected may be expressed as:
。
in this embodiment, the first product is determined by taking the product between the approximation coefficient and the empirical scale function; determining a second product based on the product between the detail coefficient and the empirical wavelet function; according to the sum of the first product and the second product, the signal to be detected is determined, the signal to be detected can be accurately determined based on the product between the approximate coefficient and the empirical scale function and the product between the detail coefficient and the empirical wavelet function, the accuracy of the signal to be detected is improved, the influence is caused on the subsequent signal characteristic information extraction and the power transmission line fault detection, and the accuracy of the power transmission line fault detection result is further improved.
In some embodiments, determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information includes: acquiring a fault traveling wave signal of a target power transmission line in a fault simulation experiment state; analyzing the fault traveling wave signal to obtain fault analysis index information; and comparing the fault analysis index information with the signal characteristic information to obtain a fault detection result corresponding to the weak traveling wave signal.
The fault simulation experiment state can be a single-phase grounding fault state of the distribution network which is grounded through the arc suppression coil.
The fault traveling wave signal may refer to a traveling wave signal corresponding to the power transmission line when the target power transmission line simulates the power transmission line fault in a fault simulation experiment state.
The fault analysis index information may be information for determining whether the power transmission line has a fault, and in practical application, the fault analysis index information may be used as a reference for fault analysis of the power transmission line, for example: when certain data of the power transmission line does not meet the corresponding fault analysis index information, the power transmission line can be judged to have faults.
As an example, a high-resistance ground fault is usually accompanied by the generation of a nonlinear arc, in order to simultaneously characterize the nonlinearity and high-resistance characteristics of the fault, the fault branch transition resistance can be regarded as a series connection of a nonlinear arc resistance and a fixed resistance, the arc resistance is given by a nonlinear arc model, the fixed resistance reflects a tower-based fixed resistance, as shown in fig. 3, a single-phase ground fault equivalent circuit schematic diagram of a power distribution network grounded through an arc suppression coil is provided, wherein u f =u m sin (ωt+φ) is a virtual power supply of a fault point, (u) m For the amplitude of fault phase voltage when the system operates normally, omega is the power frequency angular frequency, phi is the initial phase angle of fault), u c 、u0 f 、u arc C is the capacitance voltage to ground, the voltage at two ends of arc suppression coil and the arc voltage respectively j (j=1, 2,3, … …, n) is the j-th line zero-order distributed capacitance to ground,sum of zero-order distributed capacitances for all lines to ground, i cj And i 0n Zero mode current of the j-th line capacitance-to-ground current and the fault line outlet, i 0f 、i L The zero mode current of the fault point and the zero mode current flowing through the arc suppression coil are respectively, L is the inductance of the arc suppression coil, and the full response in the fault is the sum of zero state response (k 3 closed) under the excitation of an external virtual power supply and zero input (k 4 closed) response which does not consider that the virtual power supply is only influenced by capacitance and inductance energy storage before the fault. Depending on the nature of the transition resistance, faults can be divided into two states: 1) The transition resistance is equivalent to only one linear fixed resistance R1, namely the state when the switch k1 is closed; 2) Considering the influence of the nonlinear arc, the series equivalent transition resistance of the tower base fixed resistor R1 and the nonlinear arc resistor R2, namely the state when the switch k2 is closed, is adopted, and the fault state is closer to the actual fault working condition. In the k1 state without considering the nonlinear arc resistance, according to the zero state response sum The zero input response process can write the following fault state equations respectively:
。
the formulas (1) and (2) are second-order linear non-homogeneous equations and homogeneous equations respectively, and the analytic expressions of electric quantities such as current flowing through the arc suppression coil, zero-mode voltage at the outlet of the circuit, zero-mode current and the like can be obtained by solving the equations. However, when the arc resistance is represented by a logarithmic arc model in the k2 state in which the nonlinear arc resistance is considered, the equations (1) and (2) are respectively represented by the equations (3) and (4):
。
in order to accurately detect a fault of the power transmission line based on signal characteristics of the power transmission line traveling wave signals, the fault traveling wave signals of the power transmission line in a fault simulation experiment state can be analyzed, first, frequency domain analysis can be performed on weak fault traveling wave signals of any power transmission line, as shown in fig. 4, a schematic diagram of a power distribution line is provided, and C s Z is the stray capacitance of the bus system to the ground c For line wave impedance, Z t As known from superposition theory, the high-resistance ground fault of the line is equivalent to adding a voltage source at the fault point, the generated traveling wave propagates from the fault point to two ends along the line, refraction and reflection are generated at the discontinuous position of the wave impedance, and the high-resistance ground fault traveling wave has wide frequency band gamma (omega) and wave impedance Z from the frequency domain analysis c Transfer function A (ω), refractive index H Z (ω) and reflectance K f (ω) is a function of frequency, wherein the wave impedance Z c Can be expressed as:
。
the broad frequency band γ (ω) can be expressed as:
。
the transfer function a (ω) can be expressed as:
。
refractive index H Z (ω) can be expressed as:
。
reflection coefficient K f (ω) can be expressed as:
。
the attenuation degree of the waveform amplitude of different frequency components is different, and the phase change is different; the attenuation of the high-frequency component is fast, and the attenuation of the low-frequency component is slow; the farther the transmission distance, the greater the attenuation; in the case of the discontinuous wave impedance points, the refraction and reflection degrees of different frequency components are different, so that the frequency distribution of the traveling wave detected by the detection points is different for different fault points, the traveling wave waveform is analyzed from the time domain, after the initial traveling wave generated by the fault points is refracted and reflected at the discontinuous wave impedance points, each traveling wave surge is overlapped according to a certain time sequence, the fault points are different in positions, the transmission paths are different, refraction and reflection processes are different, the overlapped time sequences are different, so that the traveling wave waveforms have great differences, as shown in fig. 5, a time domain schematic diagram of the voltage traveling wave waveform of the different high-resistance grounding fault points is provided, therefore, the server can obtain fault analysis index information by analyzing the fault traveling wave signals, and can obtain fault detection results corresponding to weak traveling wave signals by comparing the fault analysis index information with the signal characteristic information, specifically, when the signal characteristic information of the traveling wave signals is matched with the corresponding fault analysis index information (for example, the value of the signal characteristic information does not exceed the threshold value specified by the corresponding fault analysis index information, and the like), and the server judges that the fault detection results corresponding to the weak traveling wave signals are not have faults.
In the embodiment, a fault traveling wave signal of a target power transmission line in a fault simulation experiment state is obtained; analyzing the fault traveling wave signal to obtain fault analysis index information; the fault analysis index information and the signal characteristic information are compared to obtain a fault detection result corresponding to the weak traveling wave signal, the power transmission line in the fault state can be simulated, the traveling wave signal in the fault state is obtained, and therefore accurate fault analysis index information is determined, data reference is provided for fault detection, and accuracy of the power transmission line fault detection result is improved.
In some embodiments, as shown in fig. 6, a schematic diagram of a high-resistance ground fault model of a power distribution network is provided, where the fault model includes 1 cable line, 1 cable mixed line, 1 overhead line, and L1, L2, and L3 are loads of the lines respectively; the fault point adopts a mode that an arc resistor Rarc and a grounding resistor Rg are connected in series to simulate a transition resistor, and in order to compare the characteristics of each arc model, the arc model adopts a Mayr arc model, a Cassie arc model and a nonlinear dynamic model respectively, and specific parameters of a circuit are shown in a table 1:
TABLE 1
The grounding resistance Rg is set to be 500 omega, the high-resistance arc light grounding fault is simulated, the compensation degree of the arc suppression coil is 8%, and the equivalent inductance L is as follows: l=1/1.08×1/(3ω) 2 C) =1.2h, wherein: c is the sum of the distributed capacitances of the system to ground; ω is the angular frequency. The waveform diagram of the current and voltage related to the fault line of the power distribution system is verified by simulation, the model traveling wave signal is obtained by adopting the fault detection method based on the weak traveling wave signal feature extraction through singular value decomposition and noise reduction and empirical wavelet decomposition, as shown in fig. 7, an arc light high-resistance grounding fault current overall schematic diagram is provided, and as can be seen from fig. 7: since the ground resistance is set to 500 Ω, belonging to high-resistance ground, the fault phase current waveform remains approximately sinusoidal, the fault current exhibits small amplitude distortion,has obvious zero-break characteristic; as shown in fig. 8, a schematic diagram of local amplification of arc high-resistance ground fault current is provided, after the server calibrates/converts the content shown in fig. 8 by an energy operator, signal characteristic information is obtained, as shown in fig. 9, a schematic diagram of signal characteristics is provided, in a specific implementation, fig. 9 may represent an initial traveling wave head, and as can be seen in fig. 9, the initial traveling wave head reaches a bus measurement point of t=134 us. When high-resistance ground faults occur, accurate calibration of weak initial moving wave head time can be achieved based on noisy singular value decomposition-empirical wavelet transformation-energy operator transformation, and position errors are smaller than 200m.
In this embodiment, by modeling a high-resistance ground fault of a power distribution network, analyzing the time-frequency domain characteristics of a weak traveling wave signal generated after the high-resistance ground fault occurs, because the generated traveling wave signal is easily submerged by noise, the fault traveling wave signal is noise-reduced by using the good denoising capability of SVD, the signal is decomposed into wavelet components with multiple local frequencies by EWT, and finally, the initial traveling wave head is calibrated by using TEO for the decomposed fault traveling wave signal, thereby determining accurate initial traveling wave head information and improving the accuracy of the power transmission line fault detection result.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a fault detection device based on weak traveling wave signal feature extraction, which is used for realizing the fault detection method based on weak traveling wave signal feature extraction. The implementation scheme of the device for solving the problem is similar to that described in the above method, so the specific limitation in the embodiments of the fault detection device based on the weak traveling wave signal feature extraction provided below can be referred to the limitation of the fault detection method based on the weak traveling wave signal feature extraction hereinabove, and will not be repeated here.
In an exemplary embodiment, as shown in fig. 10, there is provided a fault detection device based on weak traveling wave signal feature extraction, including: m module, N module and L module, wherein:
the noise reduction module 1002 is configured to perform singular value decomposition on a weak traveling wave signal corresponding to a target power transmission line, obtain a decomposition result, and convert the weak traveling wave signal into a noise-reduced signal according to the decomposition result.
The decomposing module 1004 is configured to convert the noise-reduced signal into a frequency domain signal based on fourier transform, and decompose the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal.
A determining module 1006, configured to determine signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information includes frequency information and amplitude information.
And the detection module 1008 is configured to determine a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
In an exemplary embodiment, the noise reduction module 1002 is specifically further configured to convert the weak traveling wave signal into a matrix to be decomposed according to a preset matrix conversion manner; and performing singular value decomposition on the matrix to be decomposed to obtain the decomposition result.
In an exemplary embodiment, the noise reduction module 1002 is specifically further configured to determine a hanker matrix corresponding to the weak traveling wave signal according to the decomposition result; removing a Hanker matrix of the noise signal from the Hanker matrix to obtain a de-noised Hanker matrix corresponding to the weak traveling wave signal; and converting the de-noised Hank matrix into the de-noised signal according to a preset matrix conversion mode.
In an exemplary embodiment, the decomposition module 1004 is specifically further configured to divide the frequency domain signal into at least one signal segment, obtain a local minimum value of each signal segment, and determine a midpoint of an adjacent maximum value of each signal segment according to the local minimum value; determining an empirical scale function and an empirical wavelet function corresponding to each signal segment according to the midpoint of the adjacent maximum; determining an approximation coefficient according to the empirical scale function and the weak traveling wave signal, and determining a detail coefficient according to the empirical wavelet function and the weak traveling wave signal; and converting the weak traveling wave signal into the signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function.
In an exemplary embodiment, the decomposition module 1004 is specifically further configured to determine, for any signal segment in each signal segment, an adjacent maximum value on both sides of the local minimum value in the any signal segment according to the local minimum value corresponding to the any signal segment; taking the midpoint of the adjacent maximum value between corresponding points in any signal segment as the midpoint of the adjacent maximum value of any signal segment; and determining the midpoint of the adjacent maximum value of each signal segment according to the midpoint of the adjacent maximum value of any signal segment.
In an exemplary embodiment, the decomposition module 1004 is specifically further configured to determine a first product according to a product between the approximation coefficient and the empirical scale function; determining a second product based on a product between the detail coefficient and the empirical wavelet function; and determining the signal to be detected according to the sum between the first product and the second product.
In an exemplary embodiment, the detection module 1008 is specifically further configured to obtain a fault traveling wave signal of the target power transmission line in a fault simulation experiment state; analyzing the fault traveling wave signal to obtain fault analysis index information; and comparing the fault analysis index information with the signal characteristic information to obtain a fault detection result corresponding to the weak traveling wave signal.
All or part of each module in the fault detection device based on weak traveling wave signal feature extraction can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an exemplary embodiment, a computer device, which may be a terminal, is provided, and an internal structure thereof may be as shown in fig. 11. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a fault detection method based on weak traveling wave signal feature extraction. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 11 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. The fault detection method based on weak traveling wave signal feature extraction is characterized by comprising the following steps:
singular value decomposition is carried out on a weak traveling wave signal corresponding to a target power transmission line, a decomposition result is obtained, and the weak traveling wave signal is converted into a noise-reduced signal according to the decomposition result;
converting the noise-reduced signal into a frequency domain signal based on Fourier transform, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transform according to the frequency domain signal;
Determining signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information comprises frequency information and amplitude information;
and determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
2. The method of claim 1, wherein performing singular value decomposition on the weak traveling wave signal corresponding to the target transmission line to obtain a decomposition result comprises:
converting the weak traveling wave signals into a matrix to be decomposed according to a preset matrix conversion mode;
and performing singular value decomposition on the matrix to be decomposed to obtain the decomposition result.
3. The method of claim 1, wherein said converting the weak traveling wave signal into a denoised signal based on the decomposition result comprises:
determining a Hanker matrix corresponding to the weak traveling wave signal according to the decomposition result;
removing a Hanker matrix of the noise signal from the Hanker matrix to obtain a de-noised Hanker matrix corresponding to the weak traveling wave signal;
and converting the de-noised Hank matrix into the de-noised signal according to a preset matrix conversion mode.
4. The method of claim 1, wherein the decomposing the denoised signal into a signal to be detected based on the empirical wavelet transform based on the frequency domain signal comprises:
dividing the frequency domain signal into at least one signal segment, obtaining a local minimum value of each signal segment, and determining the midpoint of the adjacent maximum value of each signal segment according to the local minimum value;
determining an empirical scale function and an empirical wavelet function corresponding to each signal segment according to the midpoint of the adjacent maximum;
determining an approximation coefficient according to the empirical scale function and the weak traveling wave signal, and determining a detail coefficient according to the empirical wavelet function and the weak traveling wave signal;
and converting the weak traveling wave signal into the signal to be detected according to the approximation coefficient, the detail coefficient, the empirical scale function and the empirical wavelet function.
5. The method of claim 4, wherein said determining the approximate midpoint of the maximum for each of said signal segments based on said local minima comprises:
for any signal segment in each signal segment, determining adjacent maximum values at two sides of the local minimum value in the any signal segment according to the local minimum value corresponding to the any signal segment;
Taking the midpoint of the adjacent maximum value between corresponding points in any signal segment as the midpoint of the adjacent maximum value of any signal segment;
and determining the midpoint of the adjacent maximum value of each signal segment according to the midpoint of the adjacent maximum value of any signal segment.
6. The method of claim 4, wherein said converting said weak traveling wave signal into said signal to be detected based on said approximation coefficients, said detail coefficients, said empirical scale function, and said empirical wavelet function comprises:
determining a first product based on a product between the approximation coefficients and the empirical scale function;
determining a second product based on a product between the detail coefficient and the empirical wavelet function;
and determining the signal to be detected according to the sum between the first product and the second product.
7. The method according to claim 1, wherein the determining, according to the signal characteristic information, a fault detection result corresponding to the weak traveling wave signal includes:
acquiring a fault traveling wave signal of the target power transmission line in a fault simulation experiment state;
analyzing the fault traveling wave signal to obtain fault analysis index information;
And comparing the fault analysis index information with the signal characteristic information to obtain a fault detection result corresponding to the weak traveling wave signal.
8. A fault detection device based on weak traveling wave signal feature extraction, the device comprising:
the noise reduction module is used for carrying out singular value decomposition on the weak traveling wave signals corresponding to the target power transmission line to obtain a decomposition result, and converting the weak traveling wave signals into noise-reduced signals according to the decomposition result;
the decomposition module is used for converting the noise-reduced signal into a frequency domain signal based on Fourier transformation, and decomposing the noise-reduced signal into a signal to be detected based on empirical wavelet transformation according to the frequency domain signal;
the determining module is used for determining signal characteristic information corresponding to the signal to be detected according to a preset energy operator; the signal characteristic information comprises frequency information and amplitude information;
and the detection module is used for determining a fault detection result corresponding to the weak traveling wave signal according to the signal characteristic information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN114966312A (en) * | 2022-05-18 | 2022-08-30 | 广东电网有限责任公司 | Power distribution network fault detection and positioning method and system based on migration convolutional neural network |
| CN116298657A (en) * | 2022-09-26 | 2023-06-23 | 武汉大学 | Multi-branch power distribution network fault positioning method under weak fault traveling wave |
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