Partial discharge pulse pattern recognition using hidden Markov models

An approach for the classification of cavity sizes based on their maximum charge transfer charachteristics, applied voltage partial discharge pattern using Hidden Markow Models, is described. In the models, the partial discharge patterns for different cavity sizes are represented by secuencing of events rather than by the actual curves. In the training phase, each cavity size represents a unique class, which emits its own eigen sequence. A Hidden Markow Model is trained for each class. using a set of training patterns consisting of the labels produced by Vector Quantization. During testing, the sequence of events to be recognised is quantized and then matches against all the developed models. The best-matched model pin-points the cavity size class. Experimental results demonstrate the remarkable capability of the proposed algorithm.