Locating and classifying partial discharge due to sharp-edges, polluted
insulators and loose-contacts in power systems significantly reduce the
outage time, impending failure, equipment damage and supply interruption. In
this paper, based on wavelet packets features of their modulated ultrasound
emissions, an efficient novel scheme for neural network recognition of partial discharges is proposed. The employed preprocessing, wavelet features and near-optimally sized network led to successful classification up to 100%, particularly when longer duration signals are processed.