CN114757233A - ICEEMDAN partial discharge denoising method based on Pearson correlation coefficient - Google Patents

ICEEMDAN partial discharge denoising method based on Pearson correlation coefficient Download PDF

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CN114757233A
CN114757233A CN202210436929.3A CN202210436929A CN114757233A CN 114757233 A CN114757233 A CN 114757233A CN 202210436929 A CN202210436929 A CN 202210436929A CN 114757233 A CN114757233 A CN 114757233A
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partial discharge
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signal
imf
correlation coefficient
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CN114757233B (en
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易孝波
杨开
吴建明
林海君
张华�
方来金
龚鹏
陈浪
蒋伟
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Zhuhai Electac High Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G01R31/1254—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Abstract

The invention discloses an ICEEMDAN partial discharge denoising method based on a Pearson correlation coefficient, which comprises the following steps: s1, outputting a partial discharge signal, a plurality of common noise signals and a noise staining signal through simulation; s2, decomposing the noise-contaminated partial discharge signal by using improved adaptive noise complete set empirical mode decomposition to obtain a plurality of IMF components; s3, calculating the Pearson correlation coefficient of each IMF component, and screening effective components according to a coefficient criterion; s4, reconstructing the effective component; and S5, removing residual noise through a wavelet-like function to obtain an effective partial discharge signal. The invention can well inhibit two types of interference of period narrow band and white noise, reduce the distortion of partial discharge signals, keep the characteristics of the partial discharge signals and facilitate the more accurate detection of the partial discharge signals.

Description

ICEEMDAN partial discharge denoising method based on Pearson correlation coefficient
Technical Field
The invention belongs to the technical field of partial discharge online monitoring, and particularly relates to an ICEEMDAN partial discharge denoising method based on a Pearson correlation coefficient.
Background
GIS insulation defects appear in the form of partial discharge, aging of equipment is accelerated, the GIS insulation defects are difficult to identify due to the fact that a fault mechanism is complex, a partial discharge signal is a nonlinear time-varying pulse signal, when detection is conducted on a partial discharge site, strong noise interference exists around the partial discharge signal, the partial discharge signal is weak, duration is short, the partial discharge signal is often submerged in noise, and diagnosis results of the partial discharge are affected.
At present, various denoising methods mainly adopt a model-based method, a transform domain method and an adaptive filtering method, however, the methods rely on the key assumption of stationary signals, and filter parameters cannot be changed, so that the limitations are overcome, and a nonlinear wavelet shrinkage method is provided to have very important significance for denoising nonlinear and non-stationary signals. The problem of wavelet basis selection is avoided by empirical mode decomposition in the prior art, but the method lacks a strict theoretical basis, and the decomposed IMF component has a serious mode mixing problem.
Disclosure of Invention
Aiming at the technical problem, the invention provides an ICEEMDAN partial discharge denoising method based on the Pearson correlation coefficient, which fully utilizes the correlation among IMF components obtained by ICEEMDAN decomposition to extract the effective information of partial discharge and reduce the waveform distortion.
In order to achieve the above object, the technical solution adopted by the present invention is an iceemda partial discharge denoising method based on pearson correlation coefficient, which specifically includes the following steps:
s1, inputting a partial discharge signal, a common noise signal and a noise staining signal;
s2, decomposing the noise-contaminated partial discharge signal by using improved adaptive noise complete set empirical mode decomposition to obtain a plurality of IMF components;
s3, calculating the Pearson correlation coefficient of each IMF component, and screening effective IMF components according to the correlation coefficient criterion;
s4, reconstructing effective IMF components to obtain a partial discharge signal subjected to preliminary denoising;
and S5, removing residual noise through a wavelet-like function to obtain an effective partial discharge signal.
As a preferred technical solution, in S2, decomposing the noisy partial discharge signal by using icemdan algorithm includes the following steps:
s21, performing EMD iterative decomposition on the original signal x for I times, defining an operator M (x) for calculating a local mean value, and then calculating a residual error r1And IMF1：
Figure BDA0003612230720000021
IMF1＝x-r1
Wherein, ω isiIs white Gaussian noise with zero mean of unit variance, epsilon0The desired signal-to-noise ratio added at the beginning of the decomposition;
s22, continuing decomposition to realize residual r2And define IMF2：
Figure BDA0003612230720000022
IMF2＝r1-r2
Wherein E is2For the 2 nd component of EMD decomposition, ε1The expected signal-to-noise ratio coefficient added for the 2 nd calculation of the IMF component;
s23, calculating k-order residual error and IMFk：
Figure BDA0003612230720000023
IMFk＝rk-1-rk
Wherein K is 3, …, K, EkFor the k component, ε, of the EMD decompositionk-1The expected signal-to-noise ratio coefficient added when the IMF component is calculated for the kth time;
s24, continuously screening to obtain a final residual error R:
Figure BDA0003612230720000024
and S25, repeating the S24 to obtain all residual numbers and IMF components.
Compared with the prior art, the invention has the following beneficial effects:
the ICEEMDAN local discharge denoising method based on the Pearson correlation coefficient is suitable for denoising nonlinear non-stationary signals, can avoid parameter selection, has self-adaptability, overcomes the serious modal mixing problem existing in modal decomposition, is not easily interfered by noise, can well inhibit two types of interference of periodic narrow band and white noise, reduces distortion of local discharge signals, reserves the characteristics of the local discharge signals, and is convenient for more accurately detecting the local discharge signals.
Drawings
FIG. 1 is a flowchart of the overall steps of an ICEEMDAN partial discharge denoising method based on Pearson correlation coefficient according to the present invention;
FIG. 2 is a time domain diagram of a noise-contaminated signal and a partial discharge signal of the present invention;
FIG. 3 is the result of the ICEEMDAN algorithm decomposition of the present invention;
FIG. 4 is a diagram of the denoising result of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Referring to fig. 1, the present invention provides an icemdan partial discharge denoising method based on pearson correlation coefficient, which is specifically implemented by performing the following steps:
s1, inputting a partial discharge signal, a common noise signal and a noise staining signal;
s2, decomposing the noise-contaminated partial discharge signal by using improved adaptive noise complete set empirical mode decomposition to obtain a plurality of IMF components;
s3, calculating the Pearson correlation coefficient of each IMF component, and screening effective IMF components according to the correlation coefficient criterion;
s4, reconstructing effective IMF components to obtain a partial discharge signal subjected to preliminary denoising;
and S5, removing residual noise through a wavelet-like function to obtain an effective partial discharge signal.
Further, the noise signal in step S1 includes white noise, the white noise is normally distributed white noise n (t) with a mean value of 0 and a variance of 0.001, and the various types of signals input in step S1 are as follows:
partial discharge signals1(t) and s2(t)：
Figure BDA0003612230720000041
Figure BDA0003612230720000042
Periodic narrow-band interference noise p (t):
Figure BDA0003612230720000043
noise signal g (t):
g(t)＝s1(t)+s2(t)+p(t)+n(t)
where A is the amplitude of the partial discharge signal, τ is the decay constant of the signal, t0For the start of the discharge pulse, fcIs the oscillation frequency of the signal, B is the amplitude of the periodic narrow-band interference, fiThe frequencies of the interference are respectively 10kHz, 300kHz, 550kHz, 1MHz and 2 MHz.
Specifically, the partial discharge signal and the noise signal input in the above-described step S1 are referred to as shown in fig. 2(a) and referred to as fig. 2 (b).
Further, in the step S2, the decomposing the noisy partial discharge signal by using the iceemda algorithm includes the following steps:
s21, performing EMD iterative decomposition on the original signal x for I times, defining an operator M (x) for calculating a local mean value, and then calculating a residual error r1And IMF1：
Figure BDA0003612230720000051
IMF1＝x-r1
Wherein, ω isiIs zero mean white Gaussian noise of unit variance, epsilon0The desired signal-to-noise ratio added at the beginning of the decomposition;
s22, continuously decomposing to realize residual error r2And define IMF2：
Figure BDA0003612230720000052
IMF2＝r1-r2
Wherein E is2For the 2 nd component of EMD decomposition, ε1The expected signal-to-noise ratio coefficient added for the 2 nd calculation of the IMF component;
s23, calculating k-order residual error and IMFk：
Figure BDA0003612230720000053
IMFk＝rk-1-rk
Wherein K is 3, …, K, EkFor the k component, ε, of the EMD decompositionk-1The expected signal-to-noise ratio coefficient added when the IMF component is calculated for the kth time;
s24, continuously screening to obtain a final residual error R:
Figure BDA0003612230720000054
s25, repeating the above S24, obtaining all residual numbers and IMF components.
Specifically, the noise-contaminated local discharge signal is decomposed by using an icemdan algorithm, and a plurality of IMF components are obtained as shown in fig. 3.
Further, in the above step S3, the pearson correlation coefficient represents the product of the covariance of the two variables divided by the standard deviation thereof, and the translational invariance can be realized by measuring the linear correlation between the two variables x and y and giving a value between-1 and 1, and the formula for specifically calculating the pearson correlation coefficient ρ (x, y) of each IMF component is as follows, and the effective IMF component is selected by comparing the pearson correlation coefficients of each IMF component:
Figure BDA0003612230720000061
wherein x isiIs a sample of the variable x and,
Figure BDA0003612230720000062
is a sample xiMean value of (a), yiIs a sample of the variable y and,
Figure BDA0003612230720000063
is a sample yiOf the average value of (a).
Further, in the step S5, the residual noise is removed through the wavelet-like function to obtain the effective partial discharge signal, the wavelet-like function
Figure BDA0003612230720000064
The following were used:
Figure BDA0003612230720000065
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003612230720000066
α is the standard deviation of the noise, wj,kThe coefficients are wavelet estimated for the decomposition scale j, with λ being the threshold.
Specifically, based on the reconstructed IMF component after screening, a primary denoised partial discharge signal is obtained, a small amount of residual noise exists in the signal, the residual noise is continuously removed through a wavelet-like function, an effective partial discharge signal is obtained, and referring to fig. 4, it can be seen from the figure that the loss of an oscillation part in a denoising result is large, and the original waveform characteristics are kept.
In summary, the present invention discloses an ICEEMDAN local discharge denoising method based on pearson correlation coefficient, which includes: firstly, outputting a partial discharge signal, various common noise signals and a noise-contaminated signal through simulation, then, decomposing the noise-contaminated partial discharge signal by using improved adaptive noise complete set empirical mode decomposition to obtain a plurality of IMF components, secondly, calculating a Pearson correlation coefficient of each IMF component, screening effective components according to a coefficient criterion, reconstructing the effective components, and finally, removing residual noise through a wavelet-like function to obtain the effective partial discharge signal. The method separates the dominant mode of the effective partial discharge signal and the dominant mode of the narrow-band interference by using an ICEEMDAN algorithm and the Pearson correlation coefficient of the IMF component of the noise-contaminated signal, can self-adaptively select the dominant mode of the partial discharge signal by the method, directly carries out threshold denoising on the dominant mode of the partial discharge signal, avoids the complexity of an algorithm of reconstructing firstly and then denoising, removes residual denoising by using a wavelet-like function, reserves the characteristics of more partial discharge signals, and reduces waveform distortion.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. An ICEEMDAN partial discharge denoising method based on Pearson correlation coefficient is characterized by comprising the following steps:
s1, inputting a partial discharge signal, a common noise signal and a noise-staining signal;
s2, decomposing the noise-contaminated partial discharge signal by using improved adaptive noise complete set empirical mode decomposition to obtain a plurality of IMF components;
s3, calculating the Pearson correlation coefficient of each IMF component, and screening effective IMF components according to the correlation coefficient criterion;
s4, reconstructing effective IMF components to obtain a partial discharge signal subjected to preliminary denoising;
and S5, removing residual noise through a wavelet-like function to obtain an effective partial discharge signal.
2. The ICEEMDAN partial discharge denoising method based on the Pearson' S correlation coefficient as claimed in claim 1, wherein the noise signal in S1 comprises white noise, the white noise is normally distributed white noise n (t) with mean 0 and variance 0.001, the input signals in S1 are as follows:
partial discharge signal s1(t) and s2(t)：
Figure FDA0003612230710000011
Figure FDA0003612230710000012
Periodic narrow-band interference noise p (t):
Figure FDA0003612230710000013
noise signal g (t):
g(t)＝s1(t)+s2(t)+p(t)+n(t)
where A is the amplitude of the partial discharge signal, τ is the decay constant of the signal, t0For the start of the discharge pulse, fcIs the oscillation frequency of the signal, B is the amplitude of the periodic narrow-band interference, fiFor interferenceFrequencies of 10kHz, 300kHz, 550kHz, 1MHz and 2MHz respectively.
3. The ICEEMDAN partial discharge denoising method based on the pearson correlation coefficient as claimed in claim 1, wherein the ICEEMDAN algorithm is used to decompose the noisy partial discharge signal in S2, comprising the following steps:
s21, performing EMD iterative decomposition on the original signal x for I times, defining an operator M (x) for calculating a local mean value, and then calculating a residual error r1And IMF1：
Figure FDA0003612230710000021
IMF1＝x-r1
Wherein, ω isiIs zero mean white Gaussian noise of unit variance, epsilon0The desired signal-to-noise ratio added at the beginning of the decomposition;
s22, continuously decomposing to realize residual error r2And define IMF2：
Figure FDA0003612230710000022
IMF2＝r1-r2
Wherein E is2For the 2 nd component of EMD decomposition, ε1The expected signal-to-noise ratio coefficient added when the IMF component is calculated for the 2 nd time;
s23, calculating k-order residual error and IMFk：
Figure FDA0003612230710000023
IMFk＝rk-1-rk
Wherein K is 3, …, K, EkFor the k component, ε, of the EMD decompositionk-1Computing IMF component for kA desired signal-to-noise ratio coefficient of the time-addition;
s24, continuously screening to obtain a final residual error R:
Figure FDA0003612230710000024
and S25, repeating the S24 to obtain all residual numbers and IMF components.
4. The method of claim 1, wherein in S3, the pearson correlation coefficient represents a product of a covariance of two variables divided by a standard deviation thereof, and a translational invariance is achieved by measuring a linear correlation between the two variables x and y and providing a value between-1 and 1, and the formula for specifically calculating the pearson correlation coefficient p (x, y) of each IMF component is as follows, and the effective IMF component is selected by comparing the pearson correlation coefficients of each IMF component:
Figure FDA0003612230710000031
wherein x isiIs a sample of the variable x and,
Figure FDA0003612230710000032
is a sample xiMean value of (a), yiIs a sample of the variable y and,
Figure FDA0003612230710000033
as a sample yiOf the average value of (a).
5. The ICEEMDAN partial discharge denoising method based on the Pearson correlation coefficient as claimed in claim 1, wherein in S5, the residual noise is removed by a wavelet-like function to obtain the effective partial discharge signal, the wavelet-like function
Figure FDA0003612230710000034
The following were used:
Figure FDA0003612230710000035
wherein the content of the first and second substances,
Figure FDA0003612230710000036
α is the standard deviation of the noise, wj,kFor the decomposition scale j wavelet estimation coefficients, λ is the threshold.
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