Multiple partial discharge sources separation using a method based on laplacian score and correlation coefficient techniques

局部放电 模式识别(心理学) 聚类分析 相关系数 特征选择 变压器 计算机科学 相关性 人工智能 数据库扫描 哈达玛变换 拉普拉斯算子 小波 算法 数据挖掘 数学 工程类 机器学习 电压 相关聚类 数学分析 几何学 树冠聚类算法 电气工程
作者
Vahid Javandel,Mehdi Vakilian,Keyvan Firuzi
出处
期刊:Electric Power Systems Research [Elsevier BV]
卷期号:210: 108070-108070
标识
DOI:10.1016/j.epsr.2022.108070
摘要

• Laplacian score is an effective method to select high performance features in terms of discrimination of multiple PD source types. • Similar features of PD signals do not add much more information in discrimination of multiple PD sources. • Correlation coefficient is a method to evaluation of similarity between different features. • Different sets of features in different case of multiple PD source types presence, are selected by feature selection algorithm. Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of them may be effective. Therefore, among the extracted features, those useful for discrimination of the multiple PD sources are studied. Then, a method, using laplacian score, and the correlation coefficient algorithms; is developed for feature selection. In order to discriminate among the multiple partial discharge sources, a density-based algorithm spatial clustering of applications with noise (DBSCAN) have been employed to cluster among available PD sources and the noise. The results of some case studies demonstrated the great ability of this method in proper discrimination of multiple PD sources.
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