Table of Contents
Advances in Computer Engineering
Volume 2014 (2014), Article ID 524740, 8 pages
Review Article

Application of Compressive Sampling in Computer Based Monitoring of Power Systems

Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON, Canada L1H 7K4

Received 5 March 2014; Revised 14 September 2014; Accepted 16 September 2014; Published 26 November 2014

Academic Editor: Kazuhiko Terashima

Copyright © 2014 Sarasij Das and Tarlochan Sidhu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Shannon’s Nyquist theorem has always dictated the conventional signal acquisition policies. Power system is not an exception to this. As per this theory, the sampling rate must be at least twice the maximum frequency present in the signal. Recently, compressive sampling (CS) theory has shown that the signals can be reconstructed from samples obtained at sub-Nyquist rate. Signal reconstruction in this theory is exact for “sparse signals” and is near exact for compressible signals provided certain conditions are satisfied. CS theory has already been applied in communication, medical imaging, MRI, radar imaging, remote sensing, computational biology, machine learning, geophysical data analysis, and so forth. CS is comparatively new in the area of computer based power system monitoring. In this paper, subareas of computer based power system monitoring where compressive sampling theory has been applied are reviewed. At first, an overview of CS is presented and then the relevant literature specific to power systems is discussed.