Power System Switching Transient Detection using Wavelet Transformed Based Signal Decomposition

Authors

  • M Shafiul Alam Lecturer, Dept. of Electrical & Electronic Engineering, IIUC, Dhaka Campus
  • Md Shamimul Haque Chowdhury Lecturer, Dept. of Electrical & Electronic Engineering, IIUC, Dhaka Campus.
  • Muhammad Athar Uddin Assistant Professor, Dept. of Electrical & Electronic Engineering, IIUC, Dhaka Campus

DOI:

https://doi.org/10.3329/iiucs.v7i0.12270

Keywords:

Power system, switching transient, wavelet transform, multiresolution analysis (MRA), signal decomposition

Abstract

Switching transient phenomena in Electric Power System develop several disturbances, sometimes very hazardous for the electrical equipment life, for the environment and for the human life. Switching transient phenomena produce over voltage, over current and electrical fields, which haven't to neglect. Several types of wavelet network algorithms have been considered for detection of power system switching transients. But both time and frequency information are accessible by multiresolution analysis (MRA). This paper presents a wavelet transform based multiresolution analysis of power system signal to detect, localize and extract switching transients. Power system switching transients have been simulated using MATLAB-7.01. The key idea underlying the approach is to decompose a distorted signal into other signals which represents a smoothed version and detailed version of the original signal. The decomposition is performed using multiresolution analysis. The proposed method appears to be robust for detection and localization of power quality disturbances produced due to load switching and capacitor switching.

DOI: http://dx.doi.org/10.3329/iiucs.v7i0.12270

IIUC Studies Vol.7 2011: 241-248

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Published

2012-10-19

How to Cite

Alam, M. S., Chowdhury, M. S. H., & Uddin, M. A. (2012). Power System Switching Transient Detection using Wavelet Transformed Based Signal Decomposition. IIUC Studies, 7, 241–248. https://doi.org/10.3329/iiucs.v7i0.12270

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Section

Articles - English Section