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

Abstract

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.

Keywords

• insulators
• ceramic insulators
• insulator contamination
• condition monitoring
• neural nets
• insulator testing
• flashover
• power overhead lines
• common electrical discharges
• controlled conditions
• outdoor ceramic insulators
• laboratory conditions
• surface pollution discharge
• insulator surface
• insulator string
• common discharges
• acoustic signals
• artificial neural network
• condition monitoring
• outdoor insulation systems
• transmission overhead lines
• substations
• commercial acoustic sensor
• ann