Partial discharge pattern classification using multilayer neural networks

Abstract

Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to automate this process is reported by using the partial discharge patterns as input to a multilayer neural network with two hidden layers. The patterns are complex and can be further complicated by interference. Therefore the recognition process appropriately qualifies as a challenging neural network task. The simulation results, and those obtained when tested with actual patterns, indicate the suitability of neural nets for real world applications in this emerging domain. Some limitations of this method are also mentioned.