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

Year 2025, Volume: 5 Issue: 2, 96 - 104, 16.06.2025

Yu-Jen Lin , Hong-Yi Lin

https://doi.org/10.5152/tepes.2025.25011

https://izlik.org/JA56LX43KA

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.

Keywords

Extended Kalman filter , forecasting-aided state estimation , power system state estimation , unscented Kalman filter

Supporting Institution

This study received financial support from ISU-112-02-01A, and MOST 108-2221-E-214-012-, Taiwan, R.O.C.

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