Application of Fuzzy Entropy to Improve Feature Selection for Defect Recognition Using Support Vector Machine in High Voltage Cable Joints

Chien-Kuo Chang; Bharath Kumar Boyanapalli; Ruay-Nan Wu

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Application of Fuzzy Entropy to Improve Feature Selection for Defect Recognition Using Support Vector Machine in High Voltage Cable Joints

机译：模糊熵在高压电缆接头中使用支持向量机改进缺陷识别特征选择的应用

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This study presents a method for defect-recognition in high voltage cable joints based on partial discharge (PD). This recognition involves three major systematic procedures. In the first procedure, the PD patterns are produced by two different laboratory models representing two types of defects in a high voltage cable. The PD data are collected from a set of experiments in the PD tests with six high voltage cable joints, including prefabricated artificial defects. The second part involves feature selection by employing a fuzzy entropy algorithm by which the entropy value of each defect is computed. Using this fuzzy entropy algorithm, the features that have the most useful characteristics for distinguishing the defects in cable joints are found. In the third part, the selected features are used for testing and training the support vector machine (SVM) model, and the accuracy testing rates are calculated in order to obtain optimal results. The SVM model in this study achieves a higher accuracy rate of 96% for classification with the proposed feature-selection-based fuzzy entropy algorithm.

机译：本研究提出了一种基于局部放电（PD）的高压电缆接头中缺陷识别方法。此识别涉及三个主要的系统程序。在第一过程中，PD图案由代表高压电缆中的两种类型的缺陷的两个不同的实验室模型产生。从PD测试中的一组实验中收集PD数据，其中六个高压电缆接头，包括预制的人工缺陷。第二部分涉及通过使用模糊熵算法来计算每个缺陷的模糊熵算法。使用这种模糊熵算法，找到了对电缆接头中缺陷区分的最有用特性的特征。在第三部分中，所选功能用于测试和训练支持向量机（SVM）模型，并计算精度测试率以获得最佳结果。本研究中的SVM模型实现了与所提出的基于特征选择的模糊熵算法分类的高精度率为96％。

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来源
《Dielectrics and Electrical Insulation, IEEE Transactions on》 |2020年第6期|2147-2155|共9页
作者
Chien-Kuo Chang; Bharath Kumar Boyanapalli; Ruay-Nan Wu;
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作者单位

National Taiwan University of Science and Technology Taipei Taiwan R.O.C;

National Taiwan University of Science and Technology Taipei Taiwan R.O.C;

National Taiwan University of Science and Technology Taipei Taiwan R.O.C;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Support vector machines; Partial discharges; Systematics; High-voltage techniques; Feature extraction; Entropy; Testing;

机译：支持向量机;部分放电;系统性;高压技术;特征提取;熵;测试;

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Application of Fuzzy Entropy to Improve Feature Selection for Defect Recognition Using Support Vector Machine in High Voltage Cable Joints

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