Identifiability-Aware Joint Sparse Error Correction for Robust State Estimation

We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. Most existing sparse error correction methods in the literature aim to find the sparsest estimate of gross errors without concerning about whether the ground-truth gross errors are fundamentally identifiable in the sparse error correction regime. In this paper, we present the identifiability-aware approach to joint sparse error correction. First, we will discuss identifiability of gross errors in the joint sparse error correction regime, in particular, how identifiability of gross errors can be guaranteed by a proper sensor protection strategy, which ensures that measurements from certain subset of sensors are free of gross errors.  We also derive a property that the locations of any identifiable gross error should satisfy. Second, we present an identifiability-aware algorithm for joint sparse error correction wherein we exploit the aforementioned property of identifiable gross errors to improve the accuracy of gross error localization. Furthermore, we provide an iterative solver for the associated nonlinear sparse optimization problem with a convergence proof. We demonstrate the efficacy of the proposed approach by applying it to power system nonlinear state estimation of IEEE 14-bus and 118-bus networks.

Major Advisor:  Jinsub Kim
Committee: Raviv Raich
Committee: Thinh Nguyen
Committee: Eduardo Cotilla-Sanchez
GCR: Leonard Coop