Partial Discharge Pattern Recognition Method for GIS Based on GA-BPNN

In order to study different types of partial discharge inspired by defects in GIS and increase the rate of correct identification on defects, four kinds of typical insulation defects physical model are designed based on the insulation defects of 110 kV GIS and its partial discharge characteristics. Ten feature parameters including the signal peak and kurtosis are acquired from 222 groups of partial discharge signal data, and recognized by BP neural network which is optimized by input genetic algorithm. Recognition results show that this method works well, owning a higher recognition rate than adaptive momentum BP neural network