Details

Autor(en) / Beteiligte

• Ling, Yin
• Bai, Demeng
• Wang, Menglin
• Gong, Xiaojin
• Gu, Chao

Titel

SVM-based Partial Discharge Pattern Classification for GIS

Ist Teil von

• Journal of physics. Conference series, 2018-01, Vol.960 (1), p.12051

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2018

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.