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

Volume

17

Issue

3

fYear

2002

fDate

7/1/2002 12:00:00 AM

Firstpage

677

Lastpage

683

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;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/TPWRD.2002.1022786

Filename

1022786