CN107728018A - A kind of noise-reduction method of power cable scene local discharge signal - Google Patents
A kind of noise-reduction method of power cable scene local discharge signal Download PDFInfo
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- 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
- G01R31/1263—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 solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—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 solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
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Abstract
本发明公开了一种电力电缆现场局部放电信号的降噪方法，包括如下步骤，步骤S1：现场采集电力电缆局部放电信号，经过数字处理，形成现场局部放电信号的时域信号f(t)；步骤S2：利用快速傅里叶变换将时域信号f(t)转换为对应频域函数F(ω)，并对频域函数F(ω)进行数学处理，获得快速傅里叶功率谱步骤S3：使用AGFCM算法对含噪信号功率谱数据分为两类；步骤S4：定位窄带干扰峰，并对定位到的窄带干扰峰进行压缩处理，对滤除窄带干扰峰后的P(ω)进行快速傅里叶反变换，得到去除窄带噪声信号g(t)；步骤S5：对g(t)进行小波变换分析，选取母小波，确定阈值，滤除白噪声，得到最终去噪信号。本发明可以抑制周期性窄带噪声，减小了波形畸变率，提高了去噪结果的信噪比。
The invention discloses a noise reduction method for on-site partial discharge signals of power cables, comprising the following steps, step S1: collecting the partial discharge signals of power cables on site, and forming a time-domain signal f(t) of the on-site partial discharge signals through digital processing; Step S2: Convert the time-domain signal f(t) into the corresponding frequency-domain function F(ω) by fast Fourier transform, and perform mathematical processing on the frequency-domain function F(ω) to obtain the fast Fourier power spectrum Step S3: Use the AGFCM algorithm to divide the power spectrum data of the noisy signal into two categories; Step S4: Locate the narrow-band interference peak, and compress the located narrow-band interference peak, and filter out the narrow-band interference peak P(ω) Perform inverse fast Fourier transform to obtain the narrowband noise-removed signal g(t); step S5: perform wavelet transform analysis on g(t), select the mother wavelet, determine the threshold, filter out white noise, and obtain the final denoising signal. The invention can suppress the periodic narrow-band noise, reduce the waveform distortion rate, and improve the signal-to-noise ratio of the denoising result.
Description
Technical Field
The invention relates to the technical field of power detection, in particular to a noise reduction method for a power cable on-site partial discharge signal.
Background
Partial Discharge (PD) is a common fault in the safe operation of a power cable, and if a PD signal cannot be detected in time, a potential safety hazard will be caused, and a power utilization accident may be caused in a serious case. PD detection is one of the effective methods for insulation state evaluation at present, however, due to the influence of the operating environment, in the actual field PD signal acquisition process, the PD signal acquired by the transformer may contain various large amounts of interference and noise. Such as white noise caused by thermal noise of electrical equipment, periodic narrow-band interference caused by system carrier communication or higher harmonics, random pulse interference caused by the operation of a switching device such as a thyristor, and the like. When processing the original signal, some important features of the PD signal are revealed only after the aliasing noise is removed or suppressed. Therefore, how to extract the PD signal from the actual signal containing noise becomes a problem of practical significance.
The current denoising algorithm mainly comprises a Fourier analysis method, a waveform parameter direct extraction method, a wavelet analysis method and the like. Fourier analysis has good resolution in a frequency domain and can process narrow-band noise. The waveform parameter direct extraction method can extract characteristic quantities such as the leading edge time, the trailing edge time, the pulse width, the waveform existing time and the like of the discharge pulse, and further identify the PD pulse. A method currently being studied more in terms of suppression of noise interference is wavelet transform. The wavelet transformation has the characteristic of multi-resolution analysis, so that a good effect can be obtained when processing the unstable signals such as partial discharge, but when denoising is carried out aiming at periodic narrow-band noise, waveform distortion or incomplete denoising is often caused, and meanwhile, the traditional wavelet analysis denoising algorithm also has the problems of low signal-to-noise ratio, large waveform distortion rate and low reduction accuracy, so that a more advanced denoising algorithm is needed for field noise.
Disclosure of Invention
The invention aims to solve the technical problems of providing a noise reduction method for a local discharge signal on a power cable site, and solving the problems of low signal-to-noise ratio, large waveform distortion rate and low restoration accuracy of the traditional wavelet analysis denoising algorithm.
In order to solve the technical problems, the invention adopts the following technical scheme: a noise reduction method for a power cable on-site partial discharge signal comprises the following steps,
step S1: acquiring a partial discharge signal of the power cable on site, and forming a time domain signal f (t) of the partial discharge signal on site through digital processing;
step S2: converting the time domain signal F (t) into a corresponding frequency domain function F (omega) by using fast Fourier transform, and performing mathematical processing on the frequency domain function F (omega) to obtain a fast Fourier power spectrum
Step S3: dividing the power spectrum data of the noise-containing signals into two types by using an AGFCM algorithm, and selecting the maximum value of one type which is less affected by narrow-band interference as a threshold T;
step S4: positioning a narrow-band interference peak, compressing the positioned narrow-band interference peak, and performing inverse fast Fourier transform on the P (omega) after the narrow-band interference peak is filtered to obtain a signal g (t) with narrow-band noise removed;
step S5: and g (t) performing wavelet transformation analysis, selecting a mother wavelet, determining a threshold, filtering white noise, and obtaining a final de-noised signal.
Preferably, the AGFCM algorithm in step S3 specifically includes: sampling the global data set in proportion to obtain a subset of the global data set, performing hierarchical clustering by setting a clustering number C until C fuzzy C mean final clustering centers are generated, then using the C fuzzy C mean clustering centers as initial populations of a genetic algorithm, performing selection, crossing and mutation operations on the C population individuals, and after M iterations, realizing optimization of data clustering; n sample data X ═ X1,x2,...,xn) Dividing into c groups, and calculating the clustering center v of each groupi(i ═ 1, 2.., c), whose objective function is defined as:
μijrepresenting the membership degree of the jth data point to the ith class; dijRepresenting the Euclidean distance between the jth data point and the ith cluster center; m is in the range of [1, ∞]Is the fuzzy clustering index.
Preferably, in step S4, the power spectrum elements of the original signal are divided into two types, the first type is a part with less narrow-band interference, and the second type is a periodic narrow-band interference part, which is takenThe maximum value of the first type element, the interference peak threshold value isα is margin coefficient, and is used for compressing the FFT transform coefficient of the point in the signal power spectrum larger than the threshold value, processing the FFT transform coefficient of the original signal, and using the equation
F (omega) and F' (omega) in the equation are FFT transformation coefficients of the original signal before and after threshold processing respectively; lambda is compression ratio selected according to the following formula
Wherein,respectively representing the maximum value and the average value in the power spectrum of the noise-contaminated signal;
finally, the frequency domain signal part larger than T is treated as an interference peak by using an interference peak threshold value T and an F' (omega) formula.
Preferably, the specific method for performing wavelet transform analysis on g (t) in step S5 is as follows:
let the function ψ (t) be e.L2(R) if the fourier transform ψ (ω) satisfies the allowable condition:
psi (t) is the base wavelet, and the wavelet function is formed by shifting and scaling the base wavelet, and if the scaling factor is a and the shifting factor is b, the wavelet function can be expressed as:
for any g (t) epsilon L2(R), continuous wavelet transform:and selecting a mother wavelet by using the function, determining a threshold, filtering white noise, and obtaining a final de-noised signal.
By adopting the technical scheme, the AGFCM clustering algorithm is utilized to perform threshold value optimization selection on the fast Fourier transform, so that the periodic narrow-band noise is suppressed, and the waveform distortion rate is reduced. Then, white noise is removed through wavelet transformation, and the signal-to-noise ratio of a denoising result is improved.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of a noise reduction device for a power cable on-site partial discharge signal.
Detailed Description
The invention provides a more advanced denoising algorithm for denoising field noise, aiming at the problems that the traditional wavelet analysis denoising algorithm can cause waveform distortion or incomplete denoising, and has low signal-to-noise ratio, large waveform distortion rate and low restoration accuracy.
As shown in fig. 1, a method for reducing noise of a local discharge signal in a power cable field includes the following steps,
s1: the method comprises the steps of collecting power cable partial discharge signals on site, and forming a site partial discharge signal time domain signal f (t) containing interference such as periodic narrow-band noise, white noise and the like through digital processing.
S2: let the fast Fourier transform of the signal F (t) be F (ω), i.e.Having an FFT power spectrum of
S3: and sampling the global data set in proportion to obtain a subset of the global data set, and performing hierarchical clustering by setting the clustering number c until c final clustering centers of the FCMs are generated. The c FCM cluster centers are then used as the initial population of the genetic algorithm. And then, carrying out selection, crossing and variation operations on the c population individuals, and after M iterations, dividing noise-contaminated signal power spectrum elements into two types, wherein the first type is a part with small narrowband interference, and the second type is a periodic narrowband interference part.
S4: getThe maximum value of the first type element, the interference peak threshold value isα is a margin coefficient to protect the power spectrum part with less narrow-band interference, the FFT transform coefficient of the original signal is processed by the following mathematical formula:
in the formula, F (omega) and F' (omega) are FFT transformation coefficients of the original signal before and after threshold processing respectively; λ is the compression ratio. The above equation compresses the FFT transform coefficients of the points in the noise-contaminated signal power spectrum that are larger than the threshold. CompressionWhereinRespectively the maximum value and the average value in the power spectrum of the noise-contaminated signal. And performing FFT inverse transformation on the P (omega) with the narrow-band interference peak removed to obtain a signal g (t) with the narrow-band noise removed.
S5: performing wavelet transform processing, and setting function psi (t) epsilon L2(R) if the fourier transform ψ (ω) satisfies the allowable condition:
psi (t) is the base wavelet. The wavelet function is formed by translating and scaling the base wavelet, and if a scaling factor is a and a translation factor is b, the wavelet function can be expressed as:
and selecting a mother wavelet, determining a threshold, and filtering white noise to obtain a final de-noised signal.
Claims (4)
1. A noise reduction method for a power cable on-site partial discharge signal is characterized by comprising the following steps,
step S1: acquiring a partial discharge signal of the power cable on site, and forming a time domain signal f (t) of the partial discharge signal on site through digital processing;
step S2: converting the time domain signal F (t) into a corresponding frequency domain function F (omega) by using fast Fourier transform, and performing mathematical processing on the frequency domain function F (omega) to obtain a fast Fourier power spectrum
Step S3: dividing the power spectrum data of the signal containing the noise into two types by using an AGFCM algorithm, and selecting the maximum value of one type which is less affected by narrow-band interference as a threshold T;
step S4: positioning a narrow-band interference peak, compressing the positioned narrow-band interference peak, and performing inverse fast Fourier transform on the P (omega) with the narrow-band interference peak removed to obtain a signal g (t) with narrow-band noise removed;
step S5: and g (t) performing wavelet transformation analysis, selecting a mother wavelet, determining a threshold, filtering white noise, and obtaining a final de-noised signal.
2. The method for reducing noise of a power cable in-situ partial discharge signal according to claim 1, wherein the AGFCM algorithm in step S3 includes: sampling the global data set in proportion to obtain a subset of the global data set, performing hierarchical clustering by setting a clustering number C until C fuzzy C mean final clustering centers are generated, then using the C fuzzy C mean clustering centers as initial populations of a genetic algorithm, performing selection, crossing and variation operations on the C population individuals, and after M iterations, realizing optimization of data clustering; n sample data X ═ X1,x2,...,xn) Dividing into c groups, and calculating the clustering center v of each groupi(i ═ 1, 2.., c), whose objective function is defined as:
<mrow> <msub> <mi>J</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>U</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>c</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msubsup> <mi>&mu;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mi>m</mi> </msubsup> <msubsup> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mn>2</mn> </msubsup> </mrow>
<mrow> <mtable> <mtr> <mtd> <mrow> <mi>s</mi> <mo>.</mo> <mi>t</mi> <mo>.</mo> </mrow> </mtd> <mtd> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>&mu;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> <mo>,</mo> <mo>&ForAll;</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mi>c</mi> </mrow>
μijrepresenting the membership degree of the jth data point to the ith class; dijRepresenting the Euclidean distance between the jth data point and the ith cluster center; m is in the range of [1, ∞]Is the fuzzy clustering index.
3. The method for reducing noise of power cable in-situ partial discharge signal according to claim 1, wherein in step S4, the power spectrum elements of the original signal are divided into two types, the first type is a portion with less narrow-band interference, and the second type is a periodic narrow-band interference portion, which is takenThe maximum value of the first type element, the interference peak threshold value isα is margin coefficient, and is used for compressing the FFT transform coefficient of the point in the signal power spectrum larger than the threshold value, processing the FFT transform coefficient of the original signal, and using the equation
<mrow> <msup> <mi>F</mi> <mo>&prime;</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mi>F</mi> <mo>(</mo> <msub> <mi>&omega;</mi> <mi>j</mi> </msub> <mo>)</mo> <mo>/</mo> <mi>&lambda;</mi> <mo>,</mo> <mi>P</mi> <mo>(</mo> <msub> <mi>&omega;</mi> <mi>j</mi> </msub> <mo>)</mo> <mo>&GreaterEqual;</mo> <mi>T</mi> </mtd> </mtr> <mtr> <mtd> <mi>F</mi> <mo>(</mo> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <mo>)</mo> <mo>,</mo> <mi>P</mi> <mo>(</mo> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <mo>)</mo> <mo><</mo> <mi>T</mi> </mtd> </mtr> </mtable> </mfenced> </mrow>
F (omega) and F' (omega) in the equation are FFT transformation coefficients of the original signal before and after threshold processing respectively; lambda is compression ratio selected according to the following formula
Wherein,respectively representing the maximum value and the average value in the power spectrum of the noise-contaminated signal;
finally, the frequency domain signal part larger than T is treated as an interference peak by using an interference peak threshold value T and an F' (omega) formula.
4. The method for reducing noise of the local discharge signal of the power cable in the field according to claim 1, wherein the specific method for performing wavelet transform analysis on g (t) in step S5 is as follows:
let the function ψ (t) be e.L2(R) if the fourier transform ψ (ω) satisfies the allowable condition:
<mrow> <msub> <mi>C</mi> <mi>&psi;</mi> </msub> <mo>=</mo> <munderover> <mo>&Integral;</mo> <mrow> <mo>-</mo> <mi>&infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&infin;</mi> </mrow> </munderover> <mo>|</mo> <mi>&omega;</mi> <msup> <mo>|</mo> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>|</mo> <mi>&psi;</mi> <mrow> <mo>(</mo> <mi>&omega;</mi> <mo>)</mo> </mrow> <msup> <mo>|</mo> <mn>2</mn> </msup> <mi>d</mi> <mi>&omega;</mi> <mo><</mo> <mi>&infin;</mi> </mrow>
psi (t) is the base wavelet, and the wavelet function is formed by shifting and scaling the base wavelet, and if the scaling factor is a and the shifting factor is b, the wavelet function can be expressed as:
<mrow> <msub> <mi>&psi;</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>b</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <msqrt> <mi>a</mi> </msqrt> </mfrac> <mi>&psi;</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>t</mi> <mo>-</mo> <mi>b</mi> </mrow> <mi>a</mi> </mfrac> <mo>)</mo> </mrow> <mo>,</mo> <mi>a</mi> <mo>&Element;</mo> <mi>R</mi> <mo>,</mo> <mi>b</mi> <mo>&Element;</mo> <mi>R</mi> </mrow>
for any g (t) epsilon L2(R), continuous wavelet transform:
and selecting a mother wavelet by using the function, determining a threshold, filtering white noise, and obtaining a final de-noised signal.
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