Disturbance classification using Hidden Markov Models and vector quantization

This paper presents a novel approach to the classification of power quality disturbances by the employment of Hidden Markov Models. In these models, power quality disturbances are represented by a sequence of consecutive frames. Both the Fourier and Wavelet Transforms are utilized to produce sequenc...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2005-07, Vol.20 (3), p.2129-2135
Main Authors: Abdel-Galil, T.K., El-Saadany, E.F., Youssef, A.M., Salama, M.M.A.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural networks
Classification
Discrete wavelet transforms
Disturbances
Electrical engineering. Electrical power engineering
Electrical power engineering
Employment
Exact sciences and technology
Fourier transforms
Hidden Markov models
Labels
Mathematical analysis
Mathematical models
monitoring techniques
Operation. Load control. Reliability
Power electronics, power supplies
Power networks and lines
Power quality
Power system modeling
Spectra
Testing
Vector quantization
Vectors (mathematics)
Wavelet packets
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Summary:This paper presents a novel approach to the classification of power quality disturbances by the employment of Hidden Markov Models. In these models, power quality disturbances are represented by a sequence of consecutive frames. Both the Fourier and Wavelet Transforms are utilized to produce sequence of spectral vectors that can accurately capture the salient characteristics of each disturbance. Vector Quantization is used to assign chain of labels for power quality disturbances utilizing their spectral vectors. From these labels, a separate Hidden Markov Model is developed for each class of the power quality disturbances in the training phase. During the testing stage, the unrecognized disturbance sequence is matched against all the developed Hidden Markov Models. The best-matched model pinpoints the class of the unknown disturbance. Simulation results prove the competence of the proposed algorithm.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2004.843399