Muñoz-Muñoz Fabio, Rodrigo-Mor Armando
Electrical Sustainable Energy, Delft University of Technology, Delft 2600 GA, The Netherlands.
Sensors (Basel). 2020 Jun 3;20(11):3180. doi: 10.3390/s20113180.
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.
本文提出了一种小波分析技术，并结合支持向量机（SVM），用于在基于磁天线的气体绝缘开关设备（GIS）局部放电（PD）测量系统中，区分局部放电与外部干扰（电磁噪声）。该技术使用交叉小波变换（XWT）来处理来自安装在GIS隔室中的磁天线的局部放电信号和外部干扰。测量是在一个含有局部放电源和共模外部干扰的高压（HV）GIS中进行的，其中外部干扰由放置在GIS中间的电偶极辐射器产生。局部放电是通过将一根针连接到GIS其中一个隔室的主导体上产生的。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。提取的特征形成了局部放电和外部干扰的线性可分簇。这些簇由支持向量机（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产生的共模信号的局部放电误报。文中介绍了测量系统的基本原理、交叉小波变换的基础、特征提取、数据分析、分类算法以及实验结果。
