Classification of common discharges in outdoor insulation using acoustic signals and artificial neural network

• Published in: High voltage 2019-12, Vol.4 (4), p.333-338
• Main Authors: Polisetty, Satish, El-Hag, Ayman, Jayram, Shesha
• Format: Article
• Language: English
• Subjects: acoustic signals; ANN; artificial neural network; ceramic insulators; commercial acoustic sensor; common discharges; common electrical discharges; condition monitoring; controlled conditions; flashover; insulator contamination; insulator string; insulator surface; insulator testing; insulators; laboratory conditions; neural nets; outdoor ceramic insulators; outdoor insulation systems; power overhead lines; Research Article; substations; surface pollution discharge; transmission overhead lines
• ISSN: 2397-7264; 2397-7264
• DOI: 10.1049/hve.2019.0113

Condition monitoring of outdoor insulation systems is crucial to the integrity of distribution and transmission overhead lines and substations. The objective of this study is to use a commercial acoustic sensor along with artificial neural network (ANN), to classify different typical types of discharges in outdoor insulation systems. First, ANN was used to distinguish between five common electrical discharges that were generated under controlled conditions. Next, this approach was extended to include outdoor ceramic insulators. Three types of defects were tested under laboratory conditions, i.e. a crack in the ceramic disc, surface pollution discharge, and corona near the insulator surface. Both a single disc, and three discs connected in an insulator string were tested with respect to these defects. For both controlled samples and full insulators, a recognition rate of more than 85% was achieved.