Title: Optimal feature retrieval for classification of non-stationary Power Quality disturbances

Authors: V. Ravikumar Pandi, S.K. Sinha, Ankita Mohapatra, B.K. Panigrahi, Swagatam Das

Addresses: Department of Electrical Engineering, IIT, Delhi 110016, India. ' National Power Training Institute, Badarpur, New Delhi, India. ' Department of Electrical Engineering, College of Engineering and Technology, BPUT, Bhubaneswar, Orissa 751003, India. ' Department of Electrical Engineering, IIT, Delhi 110016, India. ' Department of Electronics and Telecommunication Engineering, Jadavpur University, West Bengal 700032, India

Abstract: Since last few decades, Power Quality (PQ) issues has drawn the attention of both the utilities and the customers. This paper presents one of the most advanced signal-processing techniques i.e., Wavelet Transform (WT) to extract some of the important useful features of the non-stationary PQ signal. The features are then used to classify the nature of the PQ disturbance. The feature dimension is further reduced by selecting the optimal set of features using Genetic Algorithm (GA) to achieve a higher classification accuracy. The optimal features obtained using GA are used to train a Support Vector Machine (SVM) classifier for automatic classification of the Power Quality (PQ) disturbances. Nine types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of WT and SVM can effectively classify different PQ disturbances.

Keywords: GAs; genetic algorithms; power quality disturbances; wavelet transform; SVM; support vector machines; feature retrieval; classification; simulation.

DOI: 10.1504/IJAISC.2010.038640

International Journal of Artificial Intelligence and Soft Computing, 2010 Vol.2 No.3, pp.211 - 222

Published online: 17 Feb 2011 *

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