Applying the Extended Kalman Filter and Unscented Kalman Filter to Power System Forecasting-Aided State Estimation

Abstract: This paper represents the power system forecasting-aided state estimation (FASE) using the extended Kalman filter (EKF) and unscented Kalman filter (UKF). First, the concepts and mathematical formulations of power system state estimation (SE) are studied. Two types of power system state estimation, dynamic state estimation (DSE), and FASE are examined. Second, the principles and the essentials of the EKF and the UKF are described. Finally, the EKF and UKF are applied to the FASE of a five-bus power system. The research results and computer simulations lead to two key findings. First, we conclude that some pioneering works on DSE should be more appropriately re-classified as FASE. Second, we observe that the performance of the UKF does not always outperform that of the EKF. These two findings differ slightly from previous pioneering works and represent the key contributions of this paper.Cite this article as: Y.-J.Lin and H.-Y. Lin, "Applying the extended Kalman filter and unscented Kalman filter to power system forecasting-aided state estimation," Turk J Electr Power Energy Syst., 2025; 5(2), 96-104.