A Pendekatan Extended Kalman Filter untuk Estimasi Keadaan Dinamis pada Generator Sinkron dengan Model Kompleks

Abstract

State information of synchronous generators plays a crucial role in monitoring, control, and fault detection within power systems, yet direct measurement often remains limited. This study proposes a dynamic state estimation method based on the sub-transient model of a synchronous generator combined with the Extended Kalman Filter algorithm. The approach enables accurate state estimation using only terminal measurements, making it suitable for real-time monitoring applications. The algorithm operates through prediction and correction stages that iteratively update the estimated states. Simulation results demonstrate that the proposed method achieves low mean square error values across various operating conditions and disturbance scenarios. The unstable fault case yields the smallest error of 4.41×10⁻⁹, while the combined process and measurement noise scenario results in the largest error of 1.19×10⁻¹. These findings confirm that the proposed approach provides accurate and reliable state estimation for synchronous generator sub-transient models and has strong potential to enhance power system stability and reliability.