Title :
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
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;
Conference_Titel :
Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on
Print_ISBN :
0-7803-3583-X
DOI :
10.1109/ISCAS.1997.612877
