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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Partial Discharges and Noise Discrimination Using Magnetic Antennas the Cross Wavelet Transform and Support Vector Machines
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Partial Discharges and Noise Discrimination Using Magnetic Antennas the Cross Wavelet Transform and Support Vector Machines

机译：使用电磁天线交叉小波变换和支持向量机的局部放电和噪声识别

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摘要

This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external disturbances coming from the magnetic antennas installed in the GIS compartments. The measurements were performed in a high voltage (HV) GIS containing a source of PD and common-mode external disturbances, where the external disturbances were created by an electric dipole radiator placed in the middle of the GIS. The PD were created by connecting a needle to the main conductor in one of the GIS compartments. The cross wavelet transform and its local relative phase were used for feature extraction from the PD and the external noise. The features extracted formed linearly separable clusters of PD and external disturbances. These clusters were automatically classified by a support vector machine (SVM) algorithm. The SVM presented an error rate of 0.33%, correctly classifying 99.66% of the signals. The technique is intended to reduce the PD false positive indications of the common-mode signals created by an electric dipole. The measuring system fundamentals, the XWT foundations, the features extraction, the data analysis, the classification algorithm, and the experimental results are presented.
机译：本文提出了一种小波分析技术以及支持向量机（SVM），以区分基于电磁天线的GIS PD测量系统中的局部放电（PD）与外部干扰（电磁噪声）。该技术使用交叉小波变换（XWT）来处理PD信号和来自安装在GIS隔室中的电磁天线的外部干扰。在包含PD和共模外部干扰源的高压（HV）GIS中进行测量，其中外部干扰是由放置在GIS中间的电偶极辐射器产生的。 PD是通过将针连接到GIS隔室之一中的主导体上而创建的。交叉小波变换及其局部相对相位用于从局部放电和外部噪声中提取特征。提取的特征形成PD和外部干扰的线性可分离簇。这些群集通过支持向量机（SVM）算法自动分类。 SVM的错误率为0.33％，正确分类了99.66％的信号。该技术旨在减少由电偶极子产生的共模信号的PD误报。介绍了测量系统的基础知识，XWT的基础，特征提取，数据分析，分类算法以及实验结果。

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