Greedy pursuit – an efficient approach from compressed sensing to robust state estimation. Issue 9 (26th March 2018)

• Record Type: Journal Article
• Authors: Liu, Lufeng; Fu, Lijun; Ji, Feng; Xiao, Xiongbo; Wu, Bo
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 9(2018)
• Page Start: 2140
• Page End: 2147
• Publication Date: 2018-03-26
• Subjects: greedy algorithms -- power system state estimation -- energy management systems -- compressed sensing; efficient approach -- compressed sensing -- robust state estimation -- efficient state estimation -- power system operation -- energy management -- large‐scale power systems -- SE algorithms -- estimation reliability -- multiple gross errors -- greedy pursuit algorithm -- bad measurements -- composed normalised measurement errors; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.0339
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Robust and efficient state estimation (SE) is essential for power system operation and energy management. However, in modern large‐scale power systems, the main existing SE algorithms lack either estimation reliability or computation efficiency with the increasing number of multiple gross errors. This study addresses such deficiencies and proposes a variant of the greedy pursuit algorithm. Deriving from compressed sensing, the proposed algorithm operates on the measurement set and exploits the sparsity of bad measurements. By updating the composed normalised measurement errors, it detects the overall reliability, eliminates the bad measurements, and restores the wrongly rejected measurements per iteration. Numerical tests on various benchmark power systems illustrate the superior performance of the proposed algorithm, compared to several representative SE algorithms.