S-transform-based intelligent system for classification of power quality disturbance signals
Lee, I.W.C.
Dash, P.K.
Fac. of Eng., Multimedia Univ., Cyberjaya, Malaysia;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Aug. 2003
Volume: 50,
Issue: 4
On page(s): 800- 805
ISSN: 0278-0046
INSPEC Accession Number: 7708561
Digital Object Identifier: 10.1109/TIE.2003.814991
Current Version Published: 2003-07-28
Abstract
In this paper, a new approach is presented for the detection and classification of nonstationary signals in power networks by combining the S-transform and neural networks. The S-transform provides frequency-dependent resolution that simultaneously localizes the real and imaginary spectra. The S-transform is similar to the wavelet transform but with a phase correction. This property is used to obtain useful features of the nonstationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
