PSSE Redux: Convex Relaxation, Decentralized, Robust, and Dynamic Approaches

Abstract

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.

43 Pages, 8 figures

References in corpus (3)

• The Complex Gradient Operator and the CR-Calculus
• Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix
• Phase retrieval combined with digital holography

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• ADMM-based Distributed State Estimation for Power Systems: Evaluation of Performance
• Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation