Partial discharge pattern classification of angled point-oil-pressboard degradation

This paper compares single network (SNN) and ensemble neural network (ENN) capabilities to recognize and distinguish surface discharges between two point-interface-pressboard arrangements with point angles of 100 and 450. The training fingerprints for both the SNN and ENN comprise statistical parameters from the measurement of the surface discharge patterns captured over a period of 15 hours. The results shows that there is minimal statistical variability for surface discharges from a 450 point-interface-pressboard angles in comparison to that of 100, which shows different behavior over a similar degradation period. In comparison to the widely applied SNN, the ENN also consistently provides improved recognition of PD patterns while the SNN actually shows improved discrimination potential between the two point-oil-pressboard degradation angle geometries.