基于2D-LPEWT的特征提取方法在电缆局部放电分析中的应用

Application of Feature Extraction Method Based on 2D-LPEWT in Cable Partial Discharge Analysis

局部放电检测对识别电力电缆绝缘缺陷具有重要意义,其中提取有效的特征参量为其研究重点。该文提出一种基于二维Littlewood-Paley经验小波变换(2D-LPEWT)的特征提取方法,可实现电缆局部放电不同缺陷类型的准确识别。通过搭建电缆绝缘局部放电检测平台,利用2DLPEWT对四种典型缺陷模型下局部放电产生的?-Q-n图谱进行分解,对得到的经验小波系数子图提取了Tamura特征、矩特征和熵特征,并讨论了不同的特征提取方法对KNN、决策树、支持向量机(SVM)三种分类算法性能的影响。结果表明所提出的特征提取方法在不同的分类器下均可达到很高的识别精度,具有很好的实用性。

Partial discharge detection is important for identifying insulation defects of power cables.And the extraction of effective characteristic parameters is the emphasis of the study.In this paper,a feature extraction method based on 2 dimensions Littlewood-Pale empirical wavelet transform(2D-LPEWT)is proposed to realize the accurate identification of different types of defects in cable discharge.By constructing a cable-insulated partial discharge detection platform,three-dimensional spectrum of four typical defect models was decomposed by 2D-LPEWT.And then Tamura,moments and entropy characteristics were extracted from the obtained wavelet coefficients sub-graphs.The effects of different feature extraction methods on performance of K-nearest neighbour(KNN),decision tree and support vector machine(SVM)were discussed.Results show that the proposed feature extraction method can achieve high recognition accuracy under different classifiers,and has good practicability.