Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

Ju Tang, Miao Jin, Fuping Zeng, Siyuan Zhou, Xiaoxing Zhang, Yi Yang and Yan Ma
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Ju Tang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Miao Jin: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Fuping Zeng: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Siyuan Zhou: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Xiaoxing Zhang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Yi Yang: Shandong Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China
Yan Ma: Shandong Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China

Energies, 2017, vol. 10, issue 10, 1-14

Abstract: Scientific evaluation of partial discharge (PD) severity in gas-insulation switchgear (GIS) can assist in mastering the insulation condition of in-service GIS. Limited theoretical research on the laws of PD deterioration leads to a finite number of evaluation features extracted and subjective features selected for PD severity assessment. Therefore, this study proposes a minimum-redundancy maximum-relevance (mRMR) algorithm-based feature optimization selection method to realize the scientific and reasonable choice of PD severity features. PD ultra-high frequency data of varying severities are produced by simulating four typical insulation defects in GIS, which are then collected in the lab. A 16-dimension feature set describing PD original characteristics is abstracted in phase-resolved partial discharge (PRPD) mode, and the more informative evaluation feature set characterizing PD severity is further excavated by the mRMR method. Finally, a support vector machine (SVM) algorithm is employed as the classifier for intelligent evaluation to compare the evaluation effects of PD severity between the feature set selected by mRMR and the feature set is composed of discharge times, amplitude value, and time intervals obtained traditionally based on discharge change theory. The proposed comparison test showed the effectiveness of the mRMR method in informative feature selection and the accuracy of PD severity assessment for all defined defects.

Keywords: gas-insulated switchgear (GIS); partial discharge (PD); feature selection; minimum-redundancy maximum-relevance (mRMR); SVM; severity assessment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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