Electric power transient disturbance classification using wavelet-based hidden Markov models

Ln this paper, we utilize wavelet-based hidden il Markov models (HMM) to classify electric power transient disturbances associated with degradation of power quality. Since the wavelet transform extracts power transient disturbance characteristics very well, this wavelet-based HMM classifier illustrates high classification correctness rates. The power transient disturbance is decomposed into multi-resolution wavelet, domains, and the wavelet coefficients are modeled by a HMM. Based on this modeling, the maximum likelihood classification is applied to classify actual power quality transient disturbance data recorded on a 7200 V distribution line, and the result is tuned by post-processing. Of 507 power quality events experimentally observed by an electrical utility, 95.5% are correctly classified.