Distributed Point-Based Gaussian Approximation Filtering for Forecasting-Aided State Estimation in Power Systems

Abstract

This paper proposes a new distributed framework for forecasting-aided state estimation in electric power systems, modeled by a nonlinear dynamic system. We first introduce the point-based Gaussian approximation filters that use Gaussian approximation and various quadrature rules for computing the related posteriors. These filters feature higher tracking accuracy for nonlinear systems compared to the conventional method, e.g., the extended Kalman filter (EKF). Then, motivated by the increasing demands for wide-area monitoring, we develop the distributed version of the point-based Gaussian approximation filter, based on statistical linearization. In distributed state estimation, each sub-station can only access their local measurements. In the absence of a control center, the average consensus algorithm is applied such that the sub-stations can maintain the global state through information exchange with neighbors.