Abstract：In this paper, the data sequences of apparent charge versus applied voltage (ΔQ-U) in the process of stepping-up/down voltage is used as characteristic features for pattern recognition of partial discharge (PD). Discrete hidden Markov models (DHMMs) classifier is introduced to realize the PD pattern recognition. Firstly, by utilizing vector quantization method, a codebook is formed based on LBG encoding data, and then the codebook index sequences are assigned to the train and test samples of various PD types respectively. In the training of the classifier, the DHMMs are obtained for each PD source. In the testing process, the output probabilities of the test samples in all DHMMs are calculated. The model number with the largest probability is chosen as the classification results. The recognition results from 5 PD sources and 150 samples demonstrate high classification rates and easy expansion of the classifier.
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2011, Vol. 26 
