Title :
Pattern recognition applications for power system disturbance classification
Author :
Gaouda, A.M. ; Kanoun, S.H. ; Salama, M.M.A. ; Chikhani, A.Y.
Author_Institution :
Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada
fDate :
7/1/2002 12:00:00 AM
Abstract :
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.
Keywords :
feature extraction; pattern classification; power system analysis computing; power system faults; wavelet transforms; automated online disturbance classification technique; feature extraction; k-nearest neighbor; minimum Euclidean distance; neural network classifiers; pattern recognition applications; power system disturbance classification; wavelet multiresolution analysis; Data mining; Monitoring; Multiresolution analysis; Pattern recognition; Power quality; Power system analysis computing; Power systems; Signal resolution; Wavelet analysis; Wavelet transforms;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2002.1022786