SVM-based Partial Discharge Pattern Classification for GIS

Partial discharges (PD) occur when there are localized dielectric breakdowns in small regions of gas insulated substations (GIS). It is of high importance to recognize the PD patterns, through which we can diagnose the defects caused by different sources so that predictive maintenance can be conduct...

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Bibliographic Details
Published in:Journal of physics. Conference series 2018-01, Vol.960 (1), p.12051
Main Authors: Ling, Yin, Bai, Demeng, Wang, Menglin, Gong, Xiaojin, Gu, Chao
Format: Article
Language:English
Subjects:
Dielectric breakdown
Discharge
Feature extraction
Image classification
Pattern classification
Physics
Predictive maintenance
Substations
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Summary:Partial discharges (PD) occur when there are localized dielectric breakdowns in small regions of gas insulated substations (GIS). It is of high importance to recognize the PD patterns, through which we can diagnose the defects caused by different sources so that predictive maintenance can be conducted to prevent from unplanned power outage. In this paper, we propose an approach to perform partial discharge pattern classification. It first recovers the PRPD matrices from the PRPD2D images; then statistical features are extracted from the recovered PRPD matrix and fed into SVM for classification. Experiments conducted on a dataset containing thousands of images demonstrates the high effectiveness of the method.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/960/1/012051