Comparison of feature extraction methods in partial discharge waveform recognition

Automated recognition of various types of partial discharge pulses based on the pulse waveform was investigated through application of an artificial neural network. Various feature extraction methods were applied, and the recognition efficiency was determined. The results indicate that the method based on the physical characteristics of the partial discharge, which employs expert prior knowledge, is most effective and computationally least intensive.