Sparse error correction with multiple measurement vectors: Observability-aware approach

Abstract

We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error correction algorithm using multiple measurement vectors is proposed based on local linear approximation of the nonlinear measurement model. Unlike existing approaches in the literature, the proposed method ensures that the estimated gross error locations are such that system is observable, i.e., the system state is uniquely identifiable. The proposed method was applied for power system AC state estimation of the IEEE 14-bus network and outperformed benchmark techniques.