Diagnosis of partial discharge signals using neural networks and minimum distance classification

Two different methods for classifying partial discharge (PD) phenomena by a personal-computer-aided system are described. The first is concerned with common minimum distance classification, using statistical data on pulse quantities such as apparent charge, energy and phase. Applying the correct algorithms and features, such a system is able to discriminate between unknown defects using conventional discharge patterns. Classification with neural networks, which offers the possibility of classifying the shape of the PD pulses without using statistical tools for data reduction, is also discussed. Examples of diagnostic decisions are shown for a gas-insulated-switchgear system with several artificially introduced defects. The reliability of the diagnosis is estimated for both time-resolved detection evaluated by neural networks and classic phase-resolved PD evaluation. A two-step strategy of time-resolved preclassification and automated phase-resolved evaluation is introduced