PSSE Redux: Convex Relaxation, Decentralized, Robust, and Dynamic Approaches
It provides a comprehensive survey of modern PSSE techniques for power system engineers and researchers, but is incremental as it synthesizes existing work.
This chapter reviews recent advances in power system state estimation, covering convex relaxations, decentralized methods, robust bad data processing, and dynamic tracking approaches.
This chapter aspires to glean some of the recent advances in power system state estimation (PSSE), though our collection is not exhaustive by any means. The Cram{é}r-Rao bound, a lower bound on the (co)variance of any unbiased estimator, is first derived for the PSSE setup. After reviewing the classical Gauss-Newton iterations, contemporary PSSE solvers leveraging relaxations to convex programs and successive convex approximations are explored. A disciplined paradigm for distributed and decentralized schemes is subsequently exemplified under linear(ized) and exact grid models. Novel bad data processing models and fresh perspectives linking critical measurements to cyber-attacks on the state estimator are presented. Finally, spurred by advances in online convex optimization, model-free and model-based state trackers are reviewed.