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

Authors:
Jaehak Chung;E. J. Powers;W. M. Grady;S. C. Bhatt
Affiliations:
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA;-;-;-
Venue:
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Year:
2000

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Abstract

We utilize wavelet-based hidden 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.