Partial Discharge Source Classification and De-Noising in Rotating Machines Using Discrete Wavelet Transform and Directional Coupling Capacitor

This paper introduces a new method to separate PD1 from other disturbing signals present on the high voltage genera-tors and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal and external discharge pulses (e.g. on the bus bar).

Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager′s Energy Operator

To develop a measurement system for monitoring partial discharge （PD） without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager＇s energy operators was presented. Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences. Acoustic signals were collected and decomposed into I0 levels by wavelet transform into approximation and detail components. “Daubechies 25” was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals. Compared with conventional wavelet denoising method, Teager＇s energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was in creased by 20%-25% inthe experiment,without lost in energy and pulse amplitude.

Application of Complex Daubechies Wavelet in UHF Partial Discharge Measurements

On-line partial discharge(PD)detection still remains a very challenging task because of the strong electromagnetic interferences.In this paper,a new method of de-noising,using complex Daubechies wavelet(CDW)transform,has been proposed.It is a relatively recent enhancement to the real-valued wavelet transform because of tow important properties,which are nearly shift-invariant and availability of phase information.Those properties give CDW transform superiority over other real-valued wavelet transform,and then the construction algorithm of CDW is introduced in detail.Secondly,based on the real threshold algorithm of real-valued wavelet transform,complex threshold algorithm is devised.This algorithm take the different characteristics of real part and imaginary part of complex wavelet coefficients into account,it modifies the real and imaginary parts of complex wavelet coefficients respectively.Thirdly,to obtain a real de-noised signal,new combined information series is devised.By applying different combination of real part and imaginary part of de-noised complex signal,a real de-noised signal can be restored with higher peak signal-to-noise ratio(PSNR)and less distortion of original signals.Finally,On-site applications of extracting PD signals from noisy background by the optimal de-noising scheme based on CDW are illustrated.The on-site experimental results show that the optimal de-noising scheme is an effective way to suppress white noise in PD measurement.

An On-line Monitoring System for Partial Dischargefrom High Voltage Transformer

An on-line monitoring system for partial discharge from high voltage transformer is presented with structure of hardware and principle of software discussed and a new effective method combining DFT with DWT is proposed to get rid of both sinusoidal continuous noise and other external discharges.

Detection of Partial Discharge in Power Transformers Using Rogowski Coil and Multiresolution Analysis

Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools： discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation. So, partial discharge signals embedded in the electric current at ground conductor are measured using the Rogowski coil. These signals are submitted to noise suppression and the partial discharges waveforms are extracted through different ways： using discrete wavelet transform and using empirical mode decomposition. The comparison of these two methods show that the extraction with discrete wavelet transform results in a faster and simpler algorithm than the empirical mode decomposition. But this one produces more precise waveforms due its adaptive characteristic.

Recovery of Transient Signals in Noise by OptimalThresholding in Wavelet Domain

：研究用离散子波变换复原被加性高斯白噪声污染的瞬态信号．在子波域中提出了一种最佳门限方法，该方法涉及到对于波系数的假设检验，利用似然比、奈曼，皮尔逊准则和最小均方差设计该门限，计算机仿真证明，该方法在较低信噪比下复原信号的有效性．

Spatial mask filtering algorithm for partial discharge pulse extraction of large generators

A spatial mask filter algorithm （SMF） for partial discharge （PD） pulse extraction is proposed in this paper. In this algorithm, firstly, a ＇Teager＇ operator is used to strengthen wavelet coefficient local energy; then direct multiplication of coefficients at two adjacent scales is used to detect singularity points of the signal and to obtain scale based spatial mask filter; finally, an ＇ AND＇ logic operator is used in different filters to obtain the last spatial mask filter. By multiplication of wavelet coefficients with the final mask filter and wavelet reconstruction process, partial discharge pulses are extracted. The results of digital simulation and practical experiment show that this method is superior to traditional wavelet shrinkage method （TWS）. This algorithm not only can increase the signal to noise ratio （SNR）, but also can preserve the encrgy and pulse amplitude.

Detecting Deterioration Stages in Epoxy Resins Specimens under Partial Discharge Process

This paper presents a novel analysis tool based on partial discharge(PD)pulses waveform to determinate deterioration process as the fault evolves carbonizing the insulating medium in epoxy resin specimens.The PD pulses were acquired in the laboratory under controlled conditions;specially designed specimens were tested with a tip-flat electrode configuration immersed in a thermostable polymer(epoxy resin).Then,in the processing step,all acquired PD pulses were characterized with the Shannon entropy,and its variations along the deterioration process were studied.It was observed that the dispersion of this quantity increases with the deterioration of the medium,which allowed identifying different deterioration stages.The evolution of the deterioration has a correlation with the appearance of a greater number of pulses,whose entropy values diverge from average value(close to 2).These new pulses were analyzed using signal processing tools.It was found that there are important differences in the spectral content of each family,in the time-scale characteristics and in the energy distribution of the same frequency bands corresponding to each pulses family.In this work,the deterioration of epoxy resin specimens was characterized throughout the modifications observed in the characteristics of PD pulses during the entire failure process.It could be verified that there is information about the deterioration of the medium contained in the characteristics of the analyzed pulses.

A New Feature Space for Partial Discharge Signal Separation Based on DWT Coefficient Variance

In this paper, a new feature space for PD （partial discharge） signal separation is presented. Three typical PD defects were experimentally reproduced in a laboratory for obtaining independent PD sources. Signals were acquired with a digital storage oscilloscope and then post-processed with DWT （discrete Wavelet transform） for de-noising. The new feature space for PD source separation was constructed with the variance of each Wavelet coefficient vector and was compared with an established feature space for PD source separation; based on the energy of DWT coefficient vectors. After a space reduction by mean of PCA （principal components analysis）, the separation capability among them was measured by comparing the final classification error after training a neural network Results showed that with this new feature space it is possible to separate different sources of PD signals. Later, the feature space proposed was used to separate two PD sources from a real equipment tested. Further analysis on the reduced feature space has shown the band location of PD signals information for separating purpose.

Adaptive de-noising method based on wavelet and adaptive learning algorithm in on-line PD monitoring

It is an important step in the online monitoring of partial discharge (PD) to extract PD pulses from various background noises. An adaptive de-noising method is introduced for adaptive noise reduction during detection of PD pulses. This method is based on Wavelet Transform (WT), and in the wavelet domain the noises decomposed at the levels are reduced by independent thresholds. Instead of the standard hard thresholding function, a new type of hard thresholding function with continuous derivative is employed by this method. For the selection of thresholds, an unsupervised learning algorithm based on gradient in a mean square error (MSE) is present to search for the optimal threshold for noise reduction, and the optimal threshold is selected when the minimum MSE is obtained. With the simulating signals and on-site experimental data processed by this method, it is shown that the background noises such as narrowband noises can be reduced efficiently. Furthermore, it is proved that in comparison with the conventional wavelet de-noising method the adaptive de-noising method has a better performance in keeping the pulses and is more adaptive when suppressing the background noises of PD signals.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

港口高压开关柜局部放电信号自适应小波去噪方法

为了有效抑制港口高压开关柜局部放电实测信号中存在的白噪声,文中提出一种基于小波的自适应阈值去噪算法。首先对小波阈值去噪算法中的阈值选取及阈值函数进行优化,通过添加变量增加算法的适用性和灵活性,并使用改进后的粒子群算法(SPSO)对添加的变量进行最优值求解从而实现小波分解层数、小波阈值和阈值函数的自适应选取;其次对仿真信号与实测信号进行去噪。结果表明:与传统软、硬阈值函数去噪相比,使用文中所提算法去噪后的信号信噪比分别提高了5.31 dB和2.38 dB。由去噪后信号的时域图可以看出,与其他几种算法相比本文提出的算法不仅去噪效果良好,还能极大地保留信号中的有效成分。

基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法

为提高气体绝缘组合电器(Gas insulated switchgear,GIS)局部放电类型诊断的精度,提出了一种基于EWT-FE结合IHPO-SVM算法的GIS局部放电诊断方法。为深度挖掘局部放电信号内部特征,利用经验小波变换(Empirical wavelet transform,EWT)结合模糊熵(Fuzzy entropy,FE)算法对信号进行分解,并提取有效特征量;为提高支持向量机(Support vector machine,SVM)算法自适应能力与分类识别精度,提出利用经过余弦衰减计算方法以及指数下降函数改进的猎人猎物优化(Improved hunter-prey optimizer,IHPO)算法对SVM算法参数进行优化选取;搭建GIS局部放电试验模型,建立基于EWT-FE信号分析结合IHPO-SVM的局部放电识别模型,对所提算法有效性进行验证。试验结果表明,所提算法GIS局部放电类型诊断精度均大于95%,优于传统诊断算法。

基于局部放电超声信号的变压器绕组短路阻抗自动化测量技术

为了预防变压器绕组变形,延长变压器的使用寿命,提出基于局部放电超声信号的变压器绕组短路阻抗自动化测量技术。获取变压器局部放电超声信号,采用小波提取变压器局部放电超声信号的特征;通过双向二维主成分分析算法(2DPCA)对冗余的特征信息进行降维,结合特征的能量变化率,检测变压器绕组短路状态;若变压器发生绕组短路,结合变压器等效电路模型自动化测量变压器绕组短路阻抗,采用降低互感器自动化测量误差的方法完成测量结果修正。实验结果表明,该方法自动化测量变压器绕组短路阻抗时的误差较小,负载率过大不会影响该方法的变压器绕组短路阻抗的自动化测量结果,同时可依据局部放电超声信号检测变压器绕组状态。

CWNT时频尺度多步噪声抑制的轴承故障诊断

实际工况中的轴承信号常带有不同程度的噪声干扰,不利于识别轴承的健康状态,且严重影响其故障诊断的稳定性。针对噪声信号的处理难题,提出了一种CWNT时频尺度多步噪声抑制的轴承故障诊断方法。通过不同时频尺度分解,利用连续小波变换(CWT)的带通特性,可变长度时窗截取变换定位含噪信号的集中波形;从全局感受野对故障信号滤波,深层卷积非线性提取时频图像特征;在通道维度增加注意力机制,加权归一化权重至每个通道的特征,拟合通道间的复杂关联,以此达到多步噪声抑制的诊断效果。为验证所提诊断方法,向凯斯西储大学轴承数据集(CWRU)添加功率比为4.987%、12.538%、31.650%的高斯白噪声,诊断得到验证集准确率分别为97.384%、96.701%、95.407%。结果表明,CWNT方法具有较强的抗噪能力,能够提高噪声背景下故障诊断的准确性。

信号峭度不定干扰下的电缆局部放电模式识别研究

电缆不同类型缺陷引发的放电故障受信号偏斜度、峭度的影响,造成电缆的局部放电信号模式识别效果较差。为此,在信号峭度不定干扰下,提出基于卷积神经网络的电缆局部放电模式识别方法。采用小波阈值去噪方法预处理电缆局部放电信号。运用双树复小波变换提取局部放电信号特征。通过提取偏斜度和峭度刻画信号的分布特征,可以解决信号峭度不定干扰问题。采用卷积神经网络训练提取的特征,并将该特征作为分类器完成电缆局部放电模式识别。试验结果表明,该方法的识别准确率高于95%、识别时间低于5.4 s。该方法应用后具有较好的电缆局部放电模式识别性能。

基于经验小波变换与Hilbert边际谱的电缆局部放电定位方法

针对背景噪声干扰和放电信号匹配影响电缆局部放电定位精度的问题,提出一种基于经验小波变换与Hilbert边际谱的局部放电定位处理方法。通过建立振荡波电压局部放电仿真模型产生局部放电信号,经EWT分解信号,获取多分辨率分量。采用Hilbert变换对MRA分量求其边际谱能量值,计算脉冲匹配,将放电信号接收时间代入行波公式计算出故障位置。结果表明,该方法可以有效实现放电信号匹配,与传统定位方法相比,精度更高,定位误差达到0.33%。

基于小波变换的变电站放电噪声抑制研究

由于变电站放电作用产生的噪声分为高频尖峰噪声与辐射噪声两种,且两种噪声产生原因、条件与线性关系均不同,因此在抑制过程中无法通过单特征抑制算法实现最佳的抑制效果。为了增强现有抑制算法的抑制效果,根据噪声信号通性,利用小波变换算法对其噪声进行区分抑制优化,优化由建立变电站放电噪声评估模型、小波变换的高频噪声去除计算、小波变换的辐射噪声抑制三部分构成。通过对不同特征噪声参量的小波变换,改变原有抑制属性,提升该属性噪声的抑制效果,进而达到整体噪声抑制效果的最佳化。通过实验数据的对比分析证明,小波变换算法具有提升改善噪声抑制效果的作用,且整体效果稳定性与收敛性均能满足相关标准。

白噪声干扰下的超高频局部放电信号检测方法

为及时发现电力设备绝缘故障,防止电力事故发生,提出白噪声干扰下的超高频局部放电信号检测方法。选取特宽频传感器作为检测设备,设置天线的上、下限截止频率;利用复小波构造算法,通过数字滤波器确定小波基并生成函数序列,获取重构后的复高通滤波器系数,抑制白噪声;根据相关性系数、放电量因数与峰值率提取信号特征;引入激活函数建立人工神经网络模型,设置权值参数,将特征参数作为输入,经过训练完成局部放电信号检测。仿真测试表明,所提方法传感器响应灵敏,能够抵抗白噪声干扰,精准检测出不同局部放电模型特征。

基于小波包能量熵与VDM的变电站变压器局部放电在线检测方法

为了精准且及时地感知变压器局部放电等异常现象,以某变电站变压器为例,开展局部放电在线检测方法的研究。采用小波分析法,对不同种类的放电参数进行分段采集与分析;采集变压器在常态化条件下运行的原始信号,根据小波包能量熵提出的能量守恒原则,引进小波包能量熵与VDM,进行变压器局部能量熵的提取;根据变压器放电的等值分析,获取等效电容中的电压值,以此为依据,实现对变电站变压器局部放电的在线检测及放电强弱的判断。实验结果表明,设计的方法可以实现对变压器局部放电的精准识别与检测。