Feature vector extraction for the automatic classification of power quality disturbances

Author

Lee, C.H. ; Lee, J.S. ; Kim, J.O. ; Nam, S.W.

Author_Institution

Dept. of Electr. Eng., Hanyang Univ., Seoul, South Korea

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2681

Abstract

The objective of this paper is to present a systematic approach to feature vector extraction for the automatic classification of power quality (PQ) disturbances, where discrete wavelet transform (DWT), signal power estimation and data compression methods are utilized to improve the classification performance and reduce computational complexity. To demonstrate the performance and applicability of the proposed method, some test results obtained by analyzing 7-class power quality disturbances, generated by the EMTP, with white Gaussian noise are also provided

Keywords

Gaussian noise; computational complexity; data compression; fast Fourier transforms; feature extraction; pattern classification; power supply quality; transforms; transient analysis; wavelet transforms; white noise; EM transients program; EMTP; automatic classification; computational complexity; data compression methods; discrete wavelet transform; feature vector extraction; power quality disturbances; signal power estimation; white Gaussian noise; Computational complexity; Data compression; Data mining; Discrete wavelet transforms; EMTP; Feature extraction; Performance analysis; Power generation; Power quality; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on

Print_ISBN

0-7803-3583-X

Type

conf

DOI

10.1109/ISCAS.1997.612877

Filename

612877