Greedy pursuit – an efficient approach from compressed sensing to robust state estimation.

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. [ABSTRACT FROM AUTHOR]