Abnormal discharge signal processing method based on sound array
Through acoustic array-based signal processing technology, abnormal discharge acoustic signals in power equipment are extracted and analyzed, and the problem of difficulty in detecting and positioning abnormal discharge in the prior art is solved, and efficient fault detection and intelligent positioning are achieved.
Patent Information
- Application Number
- CN202411771018.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-05-02
AI Technical Summary
The prior art is difficult to effectively detect and locate abnormal discharges in power equipment, resulting in potential safety hazards and equipment failures.
Using an abnormal discharge signal processing method based on acoustic array, abnormal discharge acoustic signals are collected through the acoustic array, and digital signal processing technologies such as empirical modal decomposition (EMD) and cyclic stationary beamforming algorithms are used to extract signal characteristics and achieve fault location.
It realizes efficient detection and intelligent positioning of abnormal discharge of power equipment, reducing the risk of equipment failure and safety hazards.
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Figure CN119916143A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of high voltage direct current transmission, and in particular to a method and device for processing abnormal discharge signals based on an acoustic array. Background Art
[0002] Abnormal discharge in power equipment can lead to serious consequences. Therefore, how to detect abnormal discharge in power equipment is of great importance. Summary of the invention
[0003] In view of the defects in the prior art, the purpose of the present application is to provide an abnormal discharge signal processing method and device based on an acoustic array, which can detect abnormal discharge in power equipment.
[0004] To solve the above problems, the present application provides an abnormal discharge signal processing method based on an acoustic array, comprising the following steps:
[0005] S1: Use the acoustic array to collect the acoustic signal of abnormal discharge on site and enhance the sensor output signal through the signal amplifier to ensure the effective transmission of the signal;
[0006] S2: Apply the digital signal processing filtering technology - empirical mode decomposition (EMD) to the signal transmitted by the microphone, filter out the noise and extract some characteristic parameters of the signal to provide a basis for diagnosis;
[0007] S3: Utilize the signal's second-order statistical property - cyclostationarity to calculate the cyclic autocorrelation function of each microphone to extract the fault characteristics of the target sound source in the cyclic frequency domain while suppressing other unrelated noises. According to prior knowledge, slice the cyclic correlation function at the determined cyclic frequency and obtain the square envelope spectrum through Fourier transformation and integration along the f-axis. Determine whether there is a fault on the square envelope spectrum.
[0008] S4: The cyclostationary beamforming algorithm uses different cyclic frequencies α to select the abnormal discharge radiation source. According to prior knowledge, the cyclic correlation function is sliced at the determined cyclic frequency and a cyclostationary array processing model is constructed to obtain the focus point guidance vector matrix, thereby realizing intelligent positioning of the abnormal discharge.
[0009] In one embodiment, the acoustic array in step S1 is a spiral distributed array. The collected signal is adjusted in volume by adjusting the sampling point amplitude of the discrete digital signal through digital gain to achieve acoustic signal amplitude amplification.
[0010] In one embodiment, step S2 specifically includes: using EMD to decompose the collected signal into multiple intrinsic mode functions (IMFs) to obtain local characteristic signals of different time scales of the original signal. Search all local maximum points and minimum points of the collected signal and extract them, use the interpolation method to construct the upper envelope and lower envelope of the signal, calculate the mean of the upper envelope and the lower envelope, and obtain a new signal. Subtract this new signal from the original signal to obtain a residual signal. Repeat the above steps until the residual signal meets the stopping condition, that is, the decomposition is stopped when the mean of the upper envelope defined by the local maximum point and the lower envelope defined by the local minimum point is zero, and finally perform Hilbert transform to obtain a time-frequency spectrum diagram to obtain frequency characteristic information with physical significance. Select the filter band through the time-frequency spectrum information of each IMF and design the filter to achieve signal noise reduction.
[0011] A(α) is an algorithm-guided vector, and the feature extraction and filtering calculation based on the cyclic autocorrelation function specifically include:
[0012] The time-varying autocorrelation function of a cyclostationary signal can be expressed as:
[0013]
[0014] Wherein, 2(N+1)T0 is the duration of the acquisition signal.
[0015] Because R xx (t,τ) is a periodic function of time t, so it can be expanded using the Fourier series
[0016]
[0017] Among them, the Fourier coefficient for:
[0018]
[0019] In the above formula, And T = 2(N+1)T0; is the cycle frequency; and It is also called the cyclic autocorrelation function of x(t) at the cyclic frequency α.
[0020] Discrete random process {x(nT s ):n=0,±1,±2}, the cyclic autocorrelation function is defined as:
[0021]
[0022] Where, T s is the sampling interval. Considering that in nT s ±kTs There may be no sampling data at the time / 2, so the above formula uses τ = kT s and the time delay at τ = 0. Compared with the definition of the cyclic autocorrelation function in continuous signals, the cyclic autocorrelation function of discrete cyclostationary signals adds a correction link Different signals have different cyclic frequency families {α} (the cyclic frequency family of stationary signals is {0}). Therefore, the signal to be processed can be divided into two categories: signals of interest and interference, according to whether the signal has cyclic stationary characteristics at a specific cyclic frequency (that is, whether the cyclic correlation function of the signal at the cyclic frequency is not always zero). Among them, the signal with cyclic stationary characteristics at a specific cyclic frequency is the signal of interest, otherwise it is interference. When the number of sampling points is large enough, the cyclic correlation operation of the signal at a specific cyclic frequency can effectively suppress various stationary noises and interferences. In fact, this processing can not only filter out stationary interference and noise, but also filter out non-stationary signals that do not have cyclic stationary characteristics at the cyclic frequency and are not cyclically correlated with the signal of interest. At the same time, the cyclic frequency is only related to the modulation frequency and has nothing to do with the carrier, which also means that the cyclic correlation function can effectively extract the characteristics of the baseband signal.
[0023] In one embodiment, the cyclostationary direction of arrival (DOA) positioning algorithm specifically includes:
[0024] The signal received by a single channel in the acoustic array can be expressed as:
[0025]
[0026] If there are K incident signals α (K α <K) signals have cyclostationary characteristics at the cycle frequency α (that is, abnormal discharge signals), the above formula can also be expressed as:
[0027]
[0028] in, It includes interference terms and array element noise terms. Therefore, the cyclic cross-correlation function between any array element outputs is:
[0029]
[0030] From the previous assumptions, it can be seen that the interference and noise terms do not have cyclostationary characteristics at the cyclic frequency α, and are not cyclically correlated with the signal of interest, so the above formula can be simplified to:
[0031]
[0032] At the same time, since the signals of interest are not cyclically correlated with each other, we have:
[0033]
[0034] in, For k (t) is the circular correlation function.
[0035] The circular correlation matrix of the array element output is constructed using the circular correlation function of all array element outputs as follows:
[0036]
[0037] The above formula can be simplified as:
[0038]
[0039] is the pseudo-snapshot data matrix of the acoustic array. The so-called pseudo-snapshot data matrix is not the real array sampling data, but the real data after certain processing. A(α) is the algorithm guidance vector.
[0040] right Performing singular value decomposition yields:
[0041]
[0042] Among them, E s and E N The left signal subspace and the left noise subspace are respectively expanded, and V S and V N The right signal subspace and the right noise subspace are respectively formed. At this time, by using the orthogonality of the noise subspace and the signal subspace, the direction of the incoming wave of all interested signals can be estimated by the spectrum peak search method.
[0043] The embodiment of the present application also provides an abnormal discharge signal processing device based on an acoustic array, comprising: an acquisition module, used to acquire abnormal discharge acoustic signals of on-site power electronic equipment through a microphone array; a time-frequency characteristic parameter module, used to perform time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters; a cyclic autocorrelation function module, used to determine the cyclic autocorrelation function of each microphone in the microphone array; the cyclic autocorrelation function is used to extract the fault characteristics of the target sound source in the cyclic frequency domain, and suppress the noise in the abnormal discharge acoustic signal that is not related to the fault characteristics; the power electronic device includes the target sound source; a slicing module, used to slice the cyclic autocorrelation function at the cyclic frequency to obtain the sliced cyclic autocorrelation function; an integration module, used to perform Fourier transform on the sliced cyclic autocorrelation function to obtain the integrated square envelope spectrum along the frequency direction; a fault judgment module, used to determine the fault judgment result according to the square envelope spectrum.
[0044] Based on the solution provided in the embodiment of the present application, abnormal discharge phenomena in power equipment can be detected.
[0045] In one embodiment of the present application, the cyclic correlation function can be calculated for the microphone signals of each channel of the array and sliced at a fixed cyclic frequency according to prior knowledge, and then the sliced function is divided into a noise signal subspace and a noise subspace through singular value decomposition, and the maximum θ value is returned through a peak search method, which is the incoming wave direction of the target sound source, thereby completing the positioning of the abnormal discharge signal. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0047] Figure 1 A flowchart of an intelligent positioning and fault diagnosis method for abnormal discharge based on an acoustic array provided in an embodiment of the present application;
[0048] Figure 2 A schematic diagram of the arrangement of a distributed microphone array provided in an embodiment of the present application;
[0049] Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION
[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0051] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.
[0052] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.
[0053] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.
[0054] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.
[0055] The embodiment of the present application first provides an abnormal discharge information processing method based on an acoustic array, comprising:
[0056] Acquiring abnormal discharge acoustic signals of on-site power electronic equipment through microphone arrays;
[0057] Performing time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters;
[0058] Determine the cyclic autocorrelation function of each microphone in the microphone array; the cyclic autocorrelation function is used to extract the fault characteristics of the target sound source in the cyclic frequency domain, and suppress the noise in the abnormal discharge acoustic signal that is not related to the fault characteristics; the abnormal discharge acoustic signal of the power electronic device includes the target sound source;
[0059] Slicing the cyclic autocorrelation function at the cyclic frequency to obtain a sliced cyclic autocorrelation function;
[0060] After Fourier transforming the cyclic autocorrelation function of the slice, an integrated square envelope spectrum along the frequency direction is obtained;
[0061] A fault judgment result is determined according to the square envelope spectrum.
[0062] The microphone array may be an array including a plurality of microphones. A plurality of microphones may be arranged around the power electronic device. The plurality of microphones constitute a microphone array. Acoustic signals generated by abnormal discharge of the power electronic device are collected at the location of each microphone.
[0063] In an embodiment of the present application, the power electronic device may be a device with a certain preset function. The abnormal discharge acoustic signal may be an acoustic signal generated when the power electronic device is discharged for purposes other than realizing the preset function. The cyclic autocorrelation function may refer to a statistic that describes the similarity of the signal itself. The cyclic frequency may represent the frequency of periodic changes in the signal. Slicing may refer to extracting a function or sequence, for example, extracting a function corresponding to a specific independent variable, or extracting a specific value in a sequence. The cyclic autocorrelation function is discrete on the cyclic frequency axis, and the frequency cycle has a certain cyclic frequency (m / Td, Td is the fault frequency, and m is a positive integer). The cyclic autocorrelation function may demodulate the abnormal discharge signal, extract the fault cycle frequency, and the autocorrelation function has the function of suppressing other noises.
[0064] Fourier transform can refer to a transformation that converts a time domain signal into a frequency domain signal.
[0065] In the embodiment of the present application, time-frequency decomposition may refer to converting a signal from the time domain to the time-frequency domain, thereby revealing the frequency components of the signal at different time points. Time-frequency characteristic parameters may refer to parameters used in time-frequency analysis to describe the characteristics of a signal at different times and frequencies. The square envelope spectrum may refer to the envelope spectrum of a signal extracted by a square operation, and the envelope spectrum may refer to information obtained by analyzing the spectrum of the envelope.
[0066] In one embodiment, the abnormal discharge information processing method based on the acoustic array further includes:
[0067] Determining a model function of each microphone according to the cyclic autocorrelation function;
[0068] Determining the signal subspace of each microphone according to the result of performing singular value decomposition on the model function;
[0069] A peak search is performed on the signal subspace, and the spatial position of the sound source of the abnormal discharge acoustic signal is determined according to the result of the peak search.
[0070] In the embodiment of the present application, the peak value refers to the spectrum space obtained after singular value decomposition, and the spectrum peak search is performed on it to extract the maximum energy point. The above singular value decomposition (SVD) can refer to an important matrix decomposition method in linear algebra, which can decompose any real matrix into the product of three matrices.
[0071] In one embodiment, the microphone array includes a plurality of microphones, and the plurality of microphones are spirally distributed, such as Figure 2 As shown, Figure 2 The points in represent microphones. After the acoustic signal is acquired by the spiral distributed microphone array, its amplitude can be amplified by digital gain to ensure the subsequent effective transmission of the signal.
[0072] Before performing time-frequency decomposition on the abnormal discharge acoustic signal, the method further includes:
[0073] Obtaining the original signal of abnormal discharge acoustic signal;
[0074] The amplitude of the original signal at the sampling point is increased to obtain the abnormal discharge acoustic signal.
[0075] The above amplitude may refer to the amplitude of the acoustic signal.
[0076] In one embodiment, performing time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters includes:
[0077] Decomposing the abnormal discharge acoustic signal to obtain a plurality of eigenmode simulation functions;
[0078] Determining local maximum points and local minimum points according to the eigenmode simulation function;
[0079] Interpolating the local maximum value to construct an upper envelope of the intrinsic mode simulation function;
[0080] Interpolating the local minimum value to construct a lower envelope of the eigenstate simulation function;
[0081] Determining the mean of the upper envelope and the lower envelope;
[0082] A time-frequency characteristic parameter is determined according to the mean value and the abnormal discharge acoustic signal.
[0083] The above-mentioned time-frequency characteristic parameters may include time domain characteristic parameters and frequency domain characteristic parameters.
[0084] In one implementation, determining the time-frequency characteristic parameters according to the mean value and the abnormal discharge acoustic signal includes:
[0085] subtracting the mean value from the abnormal discharge acoustic signal to obtain a residual signal;
[0086] Returning to the step of decomposing the abnormal discharge acoustic signal to obtain a plurality of intrinsic mode simulation functions, until the residual signal meets a preset stop condition;
[0087] The residual signal that meets the stop condition is transformed to obtain a time-frequency spectrum diagram.
[0088] In one implementation, the stopping condition is that the mean of the upper envelope defined by the local maximum point and the lower envelope defined by the local minimum point is zero.
[0089] In one embodiment, determining the cyclic autocorrelation function of each microphone in the microphone array includes:
[0090] The time-varying cyclic autocorrelation function R of the abnormal discharge acoustic signal of each microphone is determined by the following formula: xx (t,τ),
[0091]
[0092] Wherein, N is the number of sampling points, T0 is the sampling period, 2(N+1)T0 is the duration of the collected signal, τ is the delay factor, and x(t) is the cyclostationary signal corresponding to the abnormal discharge acoustic signal of each microphone.
[0093] Perform Fourier series expansion on the time-varying autocorrelation function to obtain the Fourier coefficients of the time-varying autocorrelation function
[0094]
[0095] in, And T = 2(N+1)T0; "*" is conjugate, is the cycle frequency; Z is an integer;
[0096] According to the Fourier series, the discrete random process {x(nT s ):n=0,±1,±2} is:
[0097]
[0098] Among them, N s is the number of sampling points, T s is the sampling interval, k is a natural number used to determine the number of cycles, and j is an imaginary unit;
[0099] The cyclic autocorrelation function is determined according to the cyclic autocorrelation function of the discrete random process.
[0100] In one embodiment, the model function is:
[0101]
[0102] Among them, x m (t) is the signal s received by the mth microphone in the microphone array at time t k (t) is the kth signal received by the mth microphone at time t, n m (t) represents the power of the mth microphone σ 2 Zero-mean additive Gaussian white noise, and n m (t) is uncorrelated with the incoming signal, D is the microphone spacing, f0 is the center frequency of the signal, θ k is the azimuth;
[0103] The signal subspace is determined by the following formula:
[0104]
[0105] Among them, E s and E N Spread into left signal subspace and left noise subspace, V S and V N They are respectively formed into the right signal subspace and the right noise subspace, is the acoustic array pseudo-snapshot data matrix (also called the cyclic correlation matrix), α is the cyclic frequency,
[0106] α1, α2, ... are column vectors of the steering vector.
[0107]
[0108] is the pseudo-snapshot data matrix of the acoustic array, which is obtained based on real data, A(α) is the algorithm guidance vector, and A H (α).
[0109] like Figure 1 As shown, in one example of the present application, a method for processing abnormal discharge information based on an acoustic array is provided, comprising the following steps:
[0110] Step S1: The array selected this time is a multi-arm spiral array with 14 arms, each arm has 14 microphones, a total of 196 microphones. The distance between two adjacent microphones is about 0.05m.
[0111] In other implementations, the multi-arm spiral array may be provided with N1 arms, where N1 may be 1-100, further, N1 may be 10-50, further, N1 may be 14. The number of microphones in each arm may be M1, where M1 may be 1-100, further, M1 may be 10-50, further, M1 may be 14.
[0112] The distance between two adjacent microphones is about L meters (m), L may be 0.01-0.5, further, L may be 0.01-0.1, further, L may be 0.05.
[0113] Step S2: Use EMD to decompose the collected signal into multiple intrinsic mode functions (IMF) to obtain local characteristic signals of different time scales of the original signal. Search all local maximum and minimum points of the collected signal and extract them, use the interpolation method to construct the upper envelope and lower envelope of the signal, calculate the mean of the upper envelope and the lower envelope, and obtain a new signal. Subtract this new signal from the original signal to obtain a residual signal. Repeat the above steps until the residual signal meets the stopping condition, that is, the upper envelope defined by the local maximum point and the lower envelope defined by the local minimum point are zero, stop decomposition, and finally perform Hilbert transform to obtain a time-frequency spectrum diagram to obtain frequency characteristic information with physical significance. Select the filter band through the time-frequency spectrum information of each IMF and design the filter to achieve signal noise reduction.
[0114] Step S3: Calculate the cyclic autocorrelation function of each channel signal of the preprocessed microphone array by using the signal cyclic stationarity to extract the fault characteristics of the target sound source in the cyclic frequency domain and enhance the target signal at the same time, suppress useless signals, interference signals, and various noises (white noise, color noise, correlated noise, etc.); slice the cyclic correlation function at the determined cyclic frequency according to prior knowledge and obtain the square envelope spectrum through Fourier transform and integration along the f-axis. The specific algorithm is as follows.
[0115] The time-varying autocorrelation function of a cyclostationary signal can be expressed as:
[0116]
[0117] Wherein, 2(N+1)T0 is the duration of the acquisition signal.
[0118] Because R xx (t,τ) is a periodic function of time t, so it can be expanded using a Fourier series:
[0119]
[0120] Among them, the Fourier coefficient for:
[0121]
[0122] In the above formula, And T = 2(N+1)T0; is the cycle frequency; and It is also called the cyclic autocorrelation function of x(t) at the cyclic frequency α.
[0123] Discrete random process {x(nT s ):n=0,±1,±2}, the cyclic autocorrelation function is defined as:
[0124]
[0125] Among them, T s is the sampling interval. Considering that in nT s ±kT s There may be no sampling data at the time / 2, so the above formula uses τ = kT s and the time delay at τ = 0. Compared with the definition of the cyclic autocorrelation function in continuous signals, the cyclic autocorrelation function of discrete cyclostationary signals adds a correction link Different signals have different cyclic frequency families {α} (the cyclic frequency family of stationary signals is {0}). Therefore, the signal to be processed can be divided into two categories: signals of interest and interference, according to whether the signal has cyclic stationary characteristics at a specific cyclic frequency (that is, whether the cyclic correlation function of the signal at the cyclic frequency is not always zero). Among them, the signal with cyclic stationary characteristics at a specific cyclic frequency is the signal of interest, otherwise it is interference. When the number of sampling points is large enough, the cyclic correlation operation of the signal at a specific cyclic frequency can effectively suppress various stationary noises and interferences. In fact, this processing can not only filter out stationary interference and noise, but also filter out non-stationary signals that do not have cyclic stationary characteristics at the cyclic frequency and are not cyclically correlated with the signal of interest. At the same time, the cyclic frequency is only related to the modulation frequency and has nothing to do with the carrier, which also means that the cyclic correlation function can effectively extract the characteristics of the baseband signal.
[0126] Step S4: The cyclostationary DOA algorithm uses different cyclic frequencies α to select the abnormal discharge radiation source, slices the cyclic correlation function at the determined cyclic frequency according to prior knowledge, and constructs a cyclostationary array processing model to obtain the focus point guidance vector matrix, thereby realizing intelligent positioning of abnormal discharge. The specific steps are as follows:
[0127] Assume that the sensor array is an M-element spiral distributed array, the array element spacing is D, M is a positive integer, and D is a positive number. Without loss of generality, assume that there are K far-field narrowband signals incident in the space in the form of plane waves, and K is a positive integer. Let the array element at the origin of the coordinate system be the reference array element, then it can be known that the array element output signal is:
[0128]
[0129] If there are K incident signals (i.e. the above-mentioned far-field narrowband signals) α (K α <K) signals have cyclostationary characteristics at the cycle frequency α (that is, abnormal discharge signals), the above formula can also be expressed as:
[0130]
[0131] in, It includes interference terms and array element noise terms. Therefore, the cyclic cross-correlation function between any array element outputs is:
[0132]
[0133] From the previous assumptions, it can be seen that the interference and noise terms do not have cyclostationary characteristics at the cyclic frequency α, and are not cyclically correlated with the signal of interest, so the above formula can be simplified to:
[0134]
[0135] At the same time, since the signals of interest are not cyclically correlated with each other, we have:
[0136]
[0137] in, For k (t) is the circular correlation function.
[0138] The circular correlation matrix of the array element output is constructed using the circular correlation function of all array element outputs as follows:
[0139]
[0140] The above formula can be simplified as:
[0141]
[0142] is the pseudo-snapshot data matrix of the acoustic array. The so-called pseudo-snapshot data matrix is not the real array sampling data, but the real data after certain processing. A(α) is the algorithm guidance vector.
[0143] right Performing singular value decomposition yields:
[0144]
[0145] Among them, E s and E N The left signal subspace and the left noise subspace are respectively expanded, and V S and V N The right signal subspace and the right noise subspace are respectively formed. At this time, by using the orthogonality of the noise subspace and the signal subspace, the direction of the incoming wave of all interested signals can be estimated by the spectrum peak search method.
[0146] Based on the above calculations, the cyclic correlation function of the microphone signal of each channel of the array is calculated and sliced at a fixed cyclic frequency according to prior knowledge. The sliced function is then divided into the noise signal subspace and the noise subspace through singular value decomposition, and the maximum search result (θ value, representing the angle) is returned through the peak search method, which is the incoming wave direction of the target sound source, completing the positioning of the abnormal discharge signal.
[0147] The method provided in the embodiment of the present application can be applied to large-scale power equipment, such as substation power equipment.
[0148] Substation power equipment can transform electrical energy and can effectively receive and distribute electricity. Once a substation power equipment fails, it will inevitably cause the substation to fail to operate normally, and even cause a power outage, thus causing major accidents and property losses. Most of the substation power equipment failures are caused by insulation defects, and the cause of insulation defect failures is mainly caused by partial discharge (PD). Therefore, accurate positioning and diagnosis of substation partial discharge can ensure the safe and stable operation of the power grid system, which is of great significance.
[0149] Partial discharge is usually accompanied by the radiation of acoustic signals. Partial discharge can be detected by detecting whether there are abnormalities in the acoustic parameters in the environment, thereby determining whether partial discharge occurs here. The key technologies for positioning and fault diagnosis of partial discharge in substation power equipment involve cutting-edge technologies such as signal detection and preprocessing, positioning algorithm research, and feature parameter extraction. How to locate partial discharge signals and extract fault features in a contactless manner is a problem worth studying. Traditional spatial spectrum estimation based on beamforming can locate abnormal discharge signals, but the angular resolution is limited and the estimation accuracy needs to be improved. At the same time, the environment in which the substation power equipment is located is complex, and there may be multiple sound sources and background noise in the received sound source signal, which requires noise reduction and selection of the signal in the spatial domain.
[0150] The present application provides a method, which uses an acoustic array to collect acoustic signals of abnormal discharges on site and enhances the output signal of the sensor through a signal amplifier to ensure effective transmission of the signal; the signal transmitted by the microphone is subjected to filtering techniques of digital signal processing such as empirical mode decomposition (EMD), wavelet transform, S transform, etc., to filter out noise while extracting some characteristic parameters of the signal to provide a basis for diagnosis; the cyclic autocorrelation function of each microphone is calculated by using the second-order statistical characteristic of the signal-cyclostationarity to extract the fault characteristics of the target sound source in the cyclic frequency domain while suppressing other irrelevant noises, and the cyclic correlation function is sliced at a determined cyclic frequency according to prior knowledge and the square envelope spectrum is obtained by Fourier transform and integration along the f-axis. A cyclostationary array processing model is constructed according to the cyclic autocorrelation function, the signal subspace is obtained by singular value decomposition, and the spatial position of the abnormal discharge sound source is returned by peak search to realize intelligent positioning of the abnormal discharge. It has the characteristics of low environmental sensitivity and intelligent automatic positioning.
[0151] In the embodiment of the present application, the f-axis may refer to the frequency axis in the frequency domain diagram.
[0152] The embodiment of the present application also provides an abnormal discharge information processing device based on an acoustic array, comprising:
[0153] A collection module, used for collecting abnormal discharge acoustic signals of on-site power electronic equipment through a microphone array;
[0154] A time-frequency characteristic parameter module, used for performing time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters;
[0155] a cyclic autocorrelation function module, used to determine the cyclic autocorrelation function of each microphone in the microphone array; the cyclic autocorrelation function is used to extract the fault characteristics of the target sound source in the cyclic frequency domain, and to suppress the noise in the abnormal discharge acoustic signal that is not related to the fault characteristics; the power electronic device includes the target sound source;
[0156] A slicing module, used for slicing the cyclic autocorrelation function at the cyclic frequency to obtain a sliced cyclic autocorrelation function;
[0157] An integration module, used for performing Fourier transform on the cyclic autocorrelation function of the slice to obtain an integrated square envelope spectrum along the frequency direction;
[0158] The fault judgment module is used to determine the fault judgment result according to the square envelope spectrum.
[0159] In the embodiment of the present application, the abnormal discharge information processing device based on the acoustic array can also implement the abnormal discharge information processing method based on the acoustic array provided in any embodiment of the present application.
[0160] It should be noted that the information interaction, execution process, etc. between the above-mentioned modules and other modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.
[0161] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional modules in the embodiment can be integrated in a processing unit, or can exist physically alone, or can be integrated in a unit together with other modules. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. In other embodiments of the present application, the specific working process of the modules included in the device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.
[0162] Figure 3 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present application. Figure 3 As shown, the computer device of this embodiment includes: at least one processor 20 ( Figure 3 Only one is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, wherein the processor 20 implements the steps of any of the above-mentioned parking environment analysis method embodiments when executing the computer program 22.
[0163] The computer device may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will appreciate that Figure 3It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.
[0164] The processor 20 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0165] In some embodiments, the memory 21 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 21 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 21 may also include both an internal storage unit of the computer device and an external storage device. The memory 21 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 21 may also be used to temporarily store data that has been output or is to be output.
[0166] An embodiment of the present application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program.
[0167] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.
[0168] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.
[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.
[0170] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0171] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0172] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0173] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0174] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.
[0175] This specification uses specific examples to illustrate the principles and implementation methods of this application. The above examples are only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for processing abnormal discharge signals based on acoustic array, characterized in that: include: Acquiring abnormal discharge acoustic signals of on-site power electronic equipment through microphone arrays; Performing time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters; Determining the cyclic autocorrelation function of each microphone in the microphone array; the cyclic autocorrelation function is used to extract the fault characteristics of the target sound source in the cyclic frequency domain, and suppress the noise in the abnormal discharge acoustic signal that is not related to the fault characteristics; the power electronic device includes the target sound source of the abnormal discharge acoustic signal; Slicing the cyclic autocorrelation function at the cyclic frequency to obtain a sliced cyclic autocorrelation function; After Fourier transforming the cyclic autocorrelation function of the slice, an integrated square envelope spectrum along the frequency direction is obtained; A fault judgment result is determined according to the square envelope spectrum.
2. The method according to claim 1, characterized in that The method further comprises: Determining the cyclostationary model function of each microphone according to the cyclic autocorrelation function; Determining the signal subspace of each microphone according to the result of performing singular value decomposition on the model function; A peak search is performed on the signal subspace, and the spatial position of the sound source of the abnormal discharge acoustic signal is determined according to the result of the peak search.
3. The method according to claim 1, characterized in that The microphone array includes a plurality of microphones, and the plurality of microphones are distributed in a spiral shape.
4. The method according to claim 1, characterized in that: Before performing time-frequency decomposition on the abnormal discharge acoustic signal, the method further includes: Obtaining the original signal of abnormal discharge acoustic signal; The amplitude of the original signal at the sampling point is increased to obtain the abnormal discharge acoustic signal.
5. The method according to any one of claims 1 to 4, characterized in that: The step of performing time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters includes: Decomposing the abnormal discharge acoustic signal to obtain a plurality of eigenmode simulation functions; Determining local maximum points and local minimum points according to the eigenmode simulation function; Interpolating the local maximum value to construct an upper envelope of the intrinsic mode simulation function; Interpolating the local minimum value to construct a lower envelope of the intrinsic mode simulation function; Determining the mean of the upper envelope and the lower envelope; A time-frequency characteristic parameter is determined according to the mean value and the abnormal discharge acoustic signal.
6. The method according to claim 5, characterized in that Determining the time-frequency characteristic parameters according to the mean value and the abnormal discharge acoustic signal includes: subtracting the mean value from the abnormal discharge acoustic signal to obtain a residual signal; Returning to the step of decomposing the abnormal discharge acoustic signal to obtain a plurality of intrinsic mode simulation functions, until the residual signal meets a preset stop condition; The residual signal that meets the stop condition is transformed to obtain a time-frequency spectrum diagram.
7. The method according to claim 6, characterized in that The stopping condition is that the mean of the upper envelope defined by the local maximum point and the lower envelope defined by the local minimum point is zero.
8. The method according to claim 1, characterized in that The determining of the cyclic autocorrelation function of each microphone in the microphone array comprises: The time-varying cyclic autocorrelation function R of the abnormal discharge acoustic signal of each microphone is determined by the following formula: xx (t,τ), Wherein, N is the number of sampling points, T0 is the sampling period, 2(N+1)T0 is the duration of the collected signal, τ is the delay factor, and x(t) is the cyclostationary signal corresponding to the abnormal discharge acoustic signal of each microphone; Perform Fourier series expansion on the time-varying autocorrelation function to obtain the Fourier coefficients of the time-varying autocorrelation function in, And T=2(N+1)T0; "*" is conjugate, is the cycle frequency; Z is an integer; According to the Fourier series, the discrete random process {x(nT s ):n=0,±1,±2} is: Among them, N s is the number of sampling points, T s is the sampling interval, k is a natural number used to determine the number of cycles, and j is an imaginary unit; The cyclic autocorrelation function is determined according to the cyclic autocorrelation function of the discrete random process.
9. The method according to claim 2, characterized in that: The model function is: Among them, x m (t) is the signal s received by the mth microphone in the microphone array at time t k (t) is the kth signal received by the mth microphone at time t, n m (t) represents the power of the mth microphone σ 2 Zero-mean additive Gaussian white noise, and n m (t) is uncorrelated with the incoming signal, D is the microphone spacing, f0 is the center frequency of the signal, θ k is the azimuth; The signal subspace is determined by the following formula: Among them, E s and E N Spread into left signal subspace and left noise subspace, V S and V N They are respectively formed into the right signal subspace and the right noise subspace, is the cyclic correlation matrix, α is the cyclic frequency, α1, α2, ... are the columns in A in the steering vector; is the pseudo-snapshot data matrix of the acoustic array, which is obtained based on real data, A(α) is the algorithm guidance vector, a H (α).
10. An abnormal discharge signal processing device based on acoustic array, characterized in that: include: A collection module, used for collecting abnormal discharge acoustic signals of on-site power electronic equipment through a microphone array; A module for extracting time-frequency characteristic parameters is used to perform time-frequency decomposition on the abnormal discharge acoustic signal to obtain time-frequency characteristic parameters; a cyclic autocorrelation function module, used to determine the cyclic autocorrelation function of each microphone in the microphone array; the cyclic autocorrelation function is used to extract the fault characteristics of the target sound source in the cyclic frequency domain, and to suppress the noise in the abnormal discharge acoustic signal that is not related to the fault characteristics; the power electronic device includes the target sound source; A slicing module, used for slicing the cyclic autocorrelation function at the cyclic frequency to obtain a sliced cyclic autocorrelation function; An integration module, used for performing Fourier transform on the cyclic autocorrelation function of the slice to obtain an integrated square envelope spectrum along the frequency direction; The fault judgment module is used to determine the fault judgment result according to the square envelope spectrum.