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Reference: ISH2015_536

Type:
ISH Collection
Title:

PATTERN RECOGNITION OF PARTIAL DISCHARGE IN OIL-PAPER INSULATION USING A COMPREHENSIVE CLASSIFIER

 

Abstracts

Since the insulation defects in electric equipment usually lead to partial discharge (PD), PD detection can be used to evaluate the insulation condition. PD pattern recognition is of considerable practical interest to power system utilities. In this paper, several kinds of artificial oil-paper defect models are made. PD tests on them under AC voltage are performed, and a comprehensive classifier is proposed to recognize different defects. The phase resolved partial discharge (PRPD) patterns are recorded. Several statistical operators representing PD patterns are extracted and used for PD recognition features. A comprehensive classifier using three kinds of recognition methods including minimum distance, neural network and support vector machine are adopted. The recognition results are indicated as defect type and confidence level, showing good classification accuracy.
 

File Size: 399,3 KB

Year: 2015

 
 
 
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