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Pattern recognition applications for power system disturbance classification

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4 Author(s)
A. M. Gaouda ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada ; S. H. Kanoun ; M. M. A. Salama ; A. Y. Chikhani

This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.

Published in:

IEEE Transactions on Power Delivery  (Volume:17 ,  Issue: 3 )