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

TL;DR

This paper reviews recent advances in power system state estimation, including convex relaxations, decentralized methods, robustness to bad data, and online tracking, highlighting new algorithms and theoretical bounds.

Contribution

It provides a comprehensive overview of modern PSSE techniques, including convex relaxations, decentralized schemes, and cyber-attack resilience, with new perspectives and models.

Findings

Cramér-Rao bound derived for PSSE

Convex relaxation and successive approximation methods explored

Decentralized and distributed schemes exemplified

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{\'e}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.