Power system transient modelling and classification

Author

Chen, J. ; Kinsner, W. ; Huang, B.

Author_Institution

Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada

Volume

1

fYear

2002

fDate

2002

Firstpage

184

Abstract

This paper presents a method of modelling of power transients and their classification. A discrete wavelet transform and multifractal analysis based on a variance fractal dimension trajectory technique are used as tools to analyze the transients for feature extraction. A probabilistic neural network is used as a classifier for classification of transients associated with power system faults and switching. Experiments show that the classification system can achieve classification rate of 99% for power transients, and is robust in noisy environments.

Keywords

feature extraction; fractals; neural nets; pattern classification; power system analysis computing; power system faults; power system transients; probability; signal processing; wavelet transforms; discrete wavelet transform; feature extraction; multifractal analysis; power system faults; power system switching; power system transient classification; power system transient modelling; probabilistic neural network; signal processing; switching transients; variance fractal dimension trajectory technique; Analysis of variance; Discrete wavelet transforms; Feature extraction; Fractals; Neural networks; Power system faults; Power system modeling; Power system transients; Transient analysis; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on

ISSN

0840-7789

Print_ISBN

0-7803-7514-9

Type

conf

DOI

10.1109/CCECE.2002.1015196

Filename

1015196