CN113705347B - Space charge noise suppression method and device based on time-frequency analysis - Google Patents
Space charge noise suppression method and device based on time-frequency analysis Download PDFInfo
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Abstract
A space charge noise suppression method and device based on time-frequency analysis, the suppression method includes the following steps: decomposing the signals to different scales through wavelet decomposition, performing self-adaptive filtering on each scale, and then performing signal synthesis; and correcting the signal through short-time Fourier transformation to obtain the signal with suppressed noise. The invention also provides a space charge noise suppression system based on time-frequency analysis, a terminal and a computer readable storage medium. The invention can effectively extract the charge waveform from the environmental noise, and simultaneously overcomes the problem of high frequency introduction after the traditional signal correction, and finally, the pure and accurate space charge signal is obtained, thereby providing effective basis for the state monitoring of the cable insulating layer. The invention has better filtering property on environmental noise, can extract weak signals from loud noise, and has a certain filtering effect on noise which occurs in some anomalies.
Description
Technical Field
The invention belongs to the field of space charge signal processing, and particularly relates to a space charge noise suppression method and equipment based on time-frequency analysis.
Background
The electroacoustic pulse method is a method for detecting space charge of insulating materials, which is widely used at present, but is influenced by impact noise caused by electric pulse and other electromagnetic interference in the environment, and a weak space charge acquisition signal is extremely easy to submerge in the noise, so that great difficulty is brought to signal extraction and recovery. The current common means is to increase the electrode thickness and use it as a signal delay device, so as to avoid the huge oscillation noise caused by the pulse. And meanwhile, the environmental noise is averaged and subtracted from the finally acquired noise-containing waveform, so that the influence of noise is eliminated. Wavelet theory (Wavelet theory) is considered a major breakthrough in mathematical analysis and methods in recent years, which is very effective in analyzing non-stationary signals, and is now widely used in various fields of signal processing: such as speech signal processing, digital image processing, nonlinear signal processing, etc. Wherein the wavelet has good frequency division characteristics, and can be applied to multi-resolution analysis, namely, the signal C is 0 At L 2 2 orthogonal subspaces of (R) are decomposed step by step, each level of input is decomposed into a high-frequency detail part and a low-frequency approximate 2 part, the output sampling rate is halved, and the corresponding relation is as follows:
in the method, in the process of the invention,an approximate output of the signal at the j-th stage; />The detail output at the j-th level for the signal.
The adaptive filter plays an important role in the fields of communication such as echo cancellation and automatic equalization, and in the fields of signal processing such as noise suppression and spectrum estimation. The core problems to be solved by the adaptive filter are as follows: the original pure signal is estimated and recovered from the mixed signal filled with interference and noise during signal processing. Over time, the adaptive filter automatically adjusts its own parameters to accommodate changes in the external environment.
In the last decades, digital signal processors have been greatly developed, and the speed, complexity and power consumption of digital signal processors have all been improved, so that higher requirements are put on adaptive filtering methods in the communication field. In the field of adaptive filters, as research goes deep, the technical theory and practical operation of the adaptive filter are also becoming mature.
Short-time fourier transform (Short Time Fourier Transform, STFT) is one of the most commonly used time-frequency analysis methods, and a signal characteristic at a certain moment is represented by a signal in a time window. The method effectively overcomes the defect that time cannot be observed in spectrum analysis, can analyze the frequency characteristics of signals in corresponding time areas in a targeted manner, and is commonly used for spectrum analysis of time-varying signals.
The prior art also has the following drawbacks and disadvantages: as a signal delay device, the increase of the electrode thickness increases the volume of the whole measuring device, and especially when measuring the space charge of a cable, the whole device is complicated to install and detach. On the other hand, when the noise amplitude is large or the fluctuation is obvious, the signal is difficult to extract by adopting a time domain subtraction method, which causes obstruction to the recovery and analysis of the final signal. Meanwhile, in the case of performing space charge correction, a common method is a frequency domain deconvolution technique based on fourier transform. Although the method can obtain the distribution condition of space charges, high-frequency components can be introduced in the whole time domain due to the fact that the Fourier transform can not locate the time domain, and signal fluctuation is caused, as shown in fig. 4.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provide a space charge noise suppression method and equipment based on time-frequency analysis, which can effectively extract charge waveforms from environmental noise to finally obtain pure and accurate space charge signals and provide effective basis for monitoring the state of a cable insulating layer.
In order to achieve the above purpose, the present invention has the following technical scheme:
a space charge noise suppression method based on time-frequency analysis comprises the following steps:
decomposing the signals to different scales through wavelet decomposition, performing self-adaptive filtering on each scale, and then performing signal synthesis;
and correcting the signal through short-time Fourier transformation to obtain the signal with suppressed noise.
As a preferred scheme of the space charge noise suppression method, the environmental noise N (N) when pulse is added and the noise-containing signal S (N) after direct current high voltage is added are respectively collected in signal collection, the signals are decomposed to different scales through wavelet decomposition, the signals are decomposed to different frequency bands, the adaptive filtering on each scale means that the adaptive filtering is applied to the signals of each frequency band, and the signal synthesis means that the signals of each frequency band after the filtering are synthesized.
As a preferable scheme of the space charge noise suppression method, daubechies wavelets are adopted for the wavelet decomposition, and the decomposition layer number is preferentially selected according to the specific signal and noise characteristics.
As a preferable scheme of the space charge noise suppression method, the adaptive filtering adopts a recursive least square adaptive filter.
As a preferable mode of the space charge noise suppression method of the present invention, the correction of the signal by the short-time fourier transform specifically includes the steps of:
measuring space charge test output signal y at reference voltage REF (t) setting the corresponding input pulse signal x according to the unattenuated charge waveform REF (t) performing STFT transformation to obtain a time-frequency distribution matrix according to the following formula:
X REF (t，f)＝STFT(x REF )
Y REF (t，f)＝STFT(y REF )
the transfer matrix of the signal is calculated as follows:
the input signal is obtained from the measured signal as follows:
ISTFT conversion is carried out on the input signal to obtain a time domain waveform x 1 (t)。
As a preferable scheme of the space charge noise suppression method of the invention, the space charge test output signal y is measured by a PEA test system REF (t) the unattenuated charge waveform is a lower electrode interface charge waveform.
As a preferable mode of the space charge noise suppression method of the invention, the space charge test output signal y at the measurement reference voltage REF (t) is a signal at low field strength or continuous acquisitionInitial signals of the set; the field strength E of the low field strength dc And < 5kV/mm, and the acquisition time T of the initial signal is=0s.
The invention also provides a space charge noise suppression system based on time-frequency analysis, which comprises:
the wavelet decomposition and filtering module is used for decomposing the signals to different scales through wavelet decomposition, and performing self-adaptive filtering on each scale and then synthesizing the signals;
and the correction module is used for correcting the signal through short-time Fourier transform to obtain a signal with suppressed noise.
The invention also provides a terminal device which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the space charge noise suppression method based on time-frequency analysis when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the space charge noise suppression method based on time-frequency analysis.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the cable space charge detection waveform is taken as a research object, the charge waveform can be effectively extracted from the environmental noise by matching with the self-adaptive filtering based on a time-frequency analysis means, the problem of high frequency introduction after the traditional signal correction is overcome, and a pure and accurate space charge signal is finally obtained, so that an effective basis is provided for the state monitoring of the cable insulating layer. The invention has better filtering property on environmental noise, can extract weak signals from loud noise, has a certain filtering effect on noise which occurs in some anomalies, and greatly improves electromagnetic tolerance. The invention adopts a time-frequency analysis means, overcomes the defect that the time cannot be observed in frequency spectrum analysis, can pointedly analyze the frequency characteristics of signals in corresponding time areas, and provides support for accurately analyzing the health state of the cable insulation layer. The method can be applied to a space charge detection system in a matching way, reduces the thickness and the volume of a measuring device, can still obtain better charge waveforms through the algorithm, and provides technical support for design schemes of industrial production miniaturized detection equipment.
Furthermore, the invention has wide application, can be applied to waveform extraction of other noise-containing signals, and can change wavelet types and decomposition layer numbers to meet the filtering treatment of different types of signals and noise.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention, and that other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of adaptive filtering based on wavelet decomposition in accordance with the present invention;
FIG. 2 is a typical adaptive filter denoising flow chart;
FIG. 3 is a graph showing the signal-to-noise ratio of the present invention compared to the signal-to-noise ratio of different denoising algorithms;
FIG. 4 is a prior art space charge waveform diagram after FFT-based transform;
fig. 5 is a space charge correction waveform based on short-time fourier transform according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, one of ordinary skill in the art may also obtain other embodiments without undue burden.
The space charge noise suppression method based on time-frequency analysis comprises the following steps:
(1) Adaptive filtering based on wavelet decomposition, as shown in fig. 1, comprises the following steps:
and step 1, respectively collecting the environmental noise N (N) during pulse adding and the noise-containing signal S (N) after direct current high voltage adding.
And 2, decomposing the signals to different frequency bands by utilizing the frequency division characteristic of the wavelet. The invention adopts Daubechies wavelet, the decomposition layer number is 4, and the optimal solution can be found by selecting according to the specific characteristics of signals and noise.
And 3, performing adaptive filtering on signals of each frequency band. The invention adopts a Recursive Least Square (RLS) adaptive filter, which has better adaptability to non-stationary signals and high convergence rate.
And 4, synthesizing the signals of the frequency bands after filtering to obtain the final denoised signals.
(2) Correcting the final denoised signal obtained in the (1) step, comprising the steps of:
step 1, measuring output signal y of PEA test system under reference voltage REF (t) setting a corresponding input pulse signal x according to the unattenuated lower electrode interface charge waveform REF (t) and performing STFT transformation to obtain a time-frequency distribution matrix, namely:
X REF (t，f)＝STFT(x REF )
Y REF (t，f)＝STFT(y REF )
step 2, calculating a transmission matrix of the signals:
step 3, obtaining an input signal according to the actual measurement signal, and setting the actual measurement signal as Y 1 (t, f), the input signal is:
step 4, ISTFT conversion is carried out on the input signal to obtain a time domain waveform x 1 (t)。
Referring to fig. 2, a typical adaptive filter is shownIn the noise flow, N 1 (N) and N 2 (n) strong correlations, typically better for stationary signals, can be cancelled by adaptive filtering. Referring to fig. 3, line 1 in the figure is a statistics result obtained by subtracting the existing noise from the average time domain, line 2 is a statistics result obtained by denoising through adaptive filtering, line 3 is a statistics result obtained by denoising through the RLS adaptive filtering based on wavelet frequency division, and comparing signal to noise ratios under different case numbers, it can be seen that the signal to noise ratio of the invention is obviously smaller than that of other two denoising algorithms.
Referring to fig. 4, signal fluctuation can be obviously seen in the existing space charge waveform diagram based on FFT, and compared with the space charge correction waveform diagram based on short-time fourier transform referring to fig. 5, the space charge correction waveform diagram based on short-time fourier transform has no signal fluctuation, so that a pure and accurate space charge signal is obtained, and an effective basis can be provided for monitoring the state of a cable insulation layer.
The invention also provides a space charge noise suppression system based on time-frequency analysis, which comprises:
the wavelet decomposition and filtering module is used for decomposing the signals to different scales through wavelet decomposition, and performing self-adaptive filtering on each scale and then synthesizing the signals;
and the correction module is used for correcting the signal through short-time Fourier transform to obtain a signal with suppressed noise.
Firstly, recording original waveforms, namely an environment noise N (N) when pulse is added and a noise-containing signal S (N) after direct current high voltage is added, and obtaining an ideal signal after denoising after calculation in a system. The operator can select proper decomposition layer number and wavelet type in the wavelet decomposition and filtering module to obtain optimal filtering effect. And the denoised signal enters a correction module to be further corrected, so that a final space charge signal is obtained. Regarding the selection of the reference signal, the operator can select himself, and can regard the signal at low field strengths (Edc < 5kV/mm, when no charge injection is considered) as the reference signal; or an initial signal of continuous acquisition (i.e., acquisition time t=0s, which is considered to be less than charge injection) is taken as a reference signal. And carrying out waveform correction on the actual measurement signal by taking the actual measurement signal as a reference to obtain a final space charge signal. According to the actual requirements, other required physical quantities such as field intensity distortion, charge mobility and the like can be calculated according to the measurement result of space charge, so that the health state of the insulating material can be further evaluated in an auxiliary manner.
The invention also provides a terminal device which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the space charge noise suppression method based on time-frequency analysis when executing the computer program.
The invention also proposes a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the space charge noise suppression method based on time-frequency analysis of the invention.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to perform the space charge noise suppression method based on time-frequency analysis of the present invention.
The terminal can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices, and can also be a processor and a memory. The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The memory may be used to store computer programs and/or modules that, by running or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory, implement the various functions of the space charge noise suppression system based on time-frequency analysis of the present invention.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (6)
1. The space charge noise suppression method based on time-frequency analysis is characterized by comprising the following steps of:
decomposing the signals to different scales through wavelet decomposition, performing self-adaptive filtering on each scale, and then performing signal synthesis;
correcting the signal through short-time Fourier transform to obtain a signal with suppressed noise;
the method comprises the steps of collecting environmental noise N (N) when pulse is added and noise-containing signals S (N) after direct current high voltage is added respectively in signal collection, decomposing the signals to different scales through wavelet decomposition to decompose the signals to different frequency bands, performing adaptive filtering on each scale to perform adaptive filtering on the signals of each frequency band, and performing signal synthesis on the signals of each frequency band after filtering;
the correction of the signal by short-time fourier transform specifically comprises the following steps:
measuring space charge test output signal y at reference voltage REF (t) setting the corresponding input pulse signal x according to the unattenuated charge waveform REF (t) performing STFT transformation to obtain a time-frequency distribution matrix according to the following formula:
X REF (t，f)＝STFT(x REF )
Y REF (t，f)＝STFT(y REF )
the transfer matrix of the signal is calculated as follows:
the input signal is obtained from the measured signal as follows:
ISTFT conversion is carried out on the input signal to obtain a time domain waveform x 1 (t)；
Measuring space charge test output signal y by PEA test system REF (t) the unattenuated charge waveform is a lower electrode interface charge waveform;
the space charge test output signal y at the measurement reference voltage REF (t) is a signal at low field strength or a continuously acquired initial signal; the field strength E of the low field strength dc <The acquisition time t=0s of the initial signal is 5 kV/mm.
2. The space charge noise suppression method based on time-frequency analysis according to claim 1, wherein: the wavelet decomposition adopts Daubechies wavelet, and the decomposition layer number is preferentially selected according to the specific signal and noise characteristics.
3. The space charge noise suppression method based on time-frequency analysis according to claim 1, wherein: the adaptive filtering adopts a recursive least square adaptive filter.
4. A space charge noise suppression system based on time-frequency analysis, comprising:
the wavelet decomposition and filtering module is used for decomposing the signals to different scales through wavelet decomposition, and performing self-adaptive filtering on each scale and then synthesizing the signals;
the correction module is used for correcting the signal through short-time Fourier transform to obtain a signal with suppressed noise;
the method comprises the steps of collecting environmental noise N (N) when pulse is added and noise-containing signals S (N) after direct current high voltage is added respectively in signal collection, decomposing the signals to different scales through wavelet decomposition to decompose the signals to different frequency bands, performing adaptive filtering on each scale to perform adaptive filtering on the signals of each frequency band, and performing signal synthesis on the signals of each frequency band after filtering;
the correction of the signal by short-time fourier transform specifically comprises the following steps:
measuring space charge test output signal y at reference voltage REF (t) setting the corresponding input pulse signal x according to the unattenuated charge waveform REF (t) performing STFT transformation to obtain a time-frequency distribution matrix according to the following formula:
X REF (t，f)＝STFT(x REF )
Y REF (t，f)＝STFT(y REF )
the transfer matrix of the signal is calculated as follows:
the input signal is obtained from the measured signal as follows:
ISTFT conversion is carried out on the input signal to obtain a time domain waveform x 1 (t)；
Measuring space charge test output signal y by PEA test system REF (t) the unattenuated charge waveform is a lower electrode interface charge waveform;
the space charge test output signal y at the measurement reference voltage REF (t) is a signal at low field strength or a continuously acquired initial signal; the field strength E of the low field strength dc <The acquisition time t=0s of the initial signal is 5 kV/mm.
5. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements the steps of the space charge noise suppression method based on time-frequency analysis as claimed in any one of claims 1 to 3.
6. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the space charge noise suppression method based on time-frequency analysis as claimed in any one of claims 1 to 3.
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