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.

中文翻译：

---

应用模糊熵改进高压电缆接头支持向量机缺陷识别的特征选择


本研究提出了一种基于局部放电（PD）的高压电缆接头缺陷识别方法。这种认可涉及三个主要的系统程序。在第一个过程中，局部放电模式由两个不同的实验室模型生成，代表高压电缆中的两种类型的缺陷。局部放电数据是通过对六个高压电缆接头进行局部放电测试的一组实验收集的，其中包括预制的人工缺陷。第二部分涉及通过采用模糊熵算法来计算每个缺陷的熵值的特征选择。使用这种模糊熵算法，可以找到对于区分电缆接头缺陷最有用的特征。第三部分，使用选定的特征来测试和训练支持向量机（SVM）模型，并计算准确率测试率以获得最优结果。本研究中的SVM模型通过提出的基于特征选择的模糊熵算法实现了高达96%的分类准确率。