Power System Steady-State Estimation Revisited

TL;DR

This paper revisits power system steady-state estimation by addressing robustness, nonconvex constraints, and input variability using advanced optimization techniques, leading to improved accuracy and trajectory tracking.

Contribution

It introduces a comprehensive approach combining robust statistics, global optimization, and trajectory analysis for power system estimation.

Findings

Huber model enhances robustness against outliers

Global methods outperform first order methods significantly

SDP relaxations can effectively track optimal trajectories

Abstract

In power system steady-state estimation (PSSE), one needs to consider (1) the need for robust statistics, (2) the nonconvex transmission constraints, (3) the fast-varying nature of the inputs, and the corresponding need to track optimal trajectories as closely as possible. In combination, these challenges have not been considered, yet. In this paper, we address all three challenges. The need for robustness (1) is addressed by using an approach based on the so-called Huber model. The non-convexity (2) of the problem, which results in first order methods failing to find global minima, is dealt with by applying global methods. One of these methods is based on a mixed integer quadratic formulation, which provides results of several orders of magnitude better than conventional gradient descent. Lastly, the trajectory tracking (3) is discussed by showing under which conditions the trajectory tracking of the SDP relaxations has meaning.