Analysis of non‐stationary electric signals using the S‐transform-Reference-Cited by-同舟云学术

Analysis of non‐stationary electric signals using the S‐transform

Published:2009-01-02 Issue:1 Volume:28 Page:204-210
ISSN:0332-1649
Container-title:COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
language:en
Short-container-title:

Author:

Leonowicz Zbigniew,Lobos Tadeusz,Wozniak Krzysztof

Abstract

PurposeThe purpose of this paper is to compare the accuracy of tracking the amplitude and frequency changes of non‐stationary electric signals.Design/methodology/approachShort‐time fourier transform (STFT) and S‐transform algorithms were applied to analyze non‐stationary signals originating from switching of capacitor banks in a power system.FindingsThe S‐transform showed possibilities of sharp localization of the basic component, and allowed improvement of tracking dynamism the transient components in comparison to STFT.Practical implicationsS‐transform is a better tool for the analysis of non‐stationary waveforms in power systems and its properties can be used for diagnostic and power quality applications.Originality/valueThe dynamic tracking of the changes in time and frequency of real‐like signals originating from a power system are investigated in this paper.

Publisher

Emerald

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computational Theory and Mathematics,Computer Science Applications

Reference8 articles.

1. Allen, R.L. and Mills, D. (2004), Signal Analysis: Time, Frequency, Scale, and Structure, Wiley, New York, NY.

2. Lee, I.W.C. and Dash, P.K. (2003), “S‐transform‐based intelligent system for classification of power quality disturbance signals”, IEEE Transactions on Industrial Electronics, Vol. 50 No. 4, pp. 800‐5.

3. Mertins, A. (1999), Signal Analysis: Wavelets, Filter Banks, Time‐Frequency Transforms and Applications, Wiley, New York, NY.

4. Pinnegar, C.R. and Mansinha, L. (2003), “Time‐local spectral analysis for non‐stationary time series: the S‐transform for noisy signals”, Fluctuation and Noise Letters, Vol. 3 No. 3, pp. L357‐64.

5. Quian, S. and Chen, D. (1996), Joint Time‐frequency Analysis. Methods and Applications, Prentice‐Hall, Upper Saddle River, NJ.

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