Distributed Particle Filter-Based State Estimation Using Nodal Redundancy in EPS

This paper presents a comparative study of Bayesian state estimators applied to power system state estimation under centralized and distributed architectures. Building on a previously validated nodal redundancy-based partitioning strategy, a distributed implementation of the Particle Filter (PF) is developed and assessed alongside the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The methodology is evaluated exclusively on the IEEE 14-bus system with synchronized voltage, injection, and line flow measurements. Simulation results indicate that while UKF achieves the fastest convergence and lowest RMSE, the PF offers robustness in nonlinear and non-Gaussian environments. The distributed PF exhibits higher computational cost but maintains estimation accuracy, validating the scalability potential of the nodal redundancy approach. These findings highlight the promise of PF-based distributed estimation in complex and uncertain power systems.