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Energies 2017, 10(10), 1516; doi:10.3390/en10101516

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

1
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
2
Shandong Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China
*
Author to whom correspondence should be addressed.
Received: 5 September 2017 / Revised: 22 September 2017 / Accepted: 26 September 2017 / Published: 1 October 2017
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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. View Full-Text
Keywords: gas-insulated switchgear (GIS); partial discharge (PD); feature selection; minimum-redundancy maximum-relevance (mRMR); SVM; severity assessment gas-insulated switchgear (GIS); partial discharge (PD); feature selection; minimum-redundancy maximum-relevance (mRMR); SVM; severity assessment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tang, J.; Jin, M.; Zeng, F.; Zhou, S.; Zhang, X.; Yang, Y.; Ma, Y. Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance. Energies 2017, 10, 1516.

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