Decoupled Koopman-Based Tracking for Enhanced Convergence in Dynamic State Estimation of Power Systems

Abstract

Dynamic state estimation (DSE) plays a critical role in the monitoring and control of modern electrical power systems, particularly under time-varying operating conditions. Conventional estimators such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) rely on nonlinear dynamic models in the prediction stage, which may introduce computational complexity and numerical sensitivity. This paper proposes a decoupled Koopman-based tracking framework in which a data-driven Koopman operator is employed exclusively as a linear prediction mechanism prior to the estimation stage, while preserving the classical correction structure of conventional estimators. The approach is validated on the IEEE 14-bus system under dynamic load variations over 300 operating points. Performance is evaluated in terms of global and nodal root mean square error (RMSE), measurement residual metrics, execution time statistics, and operational robustness under latency constraints. The results show that the Koopman-enhanced estimators maintain competitive estimation accuracy while improving measurement fitting consistency and computational stability. Furthermore, the proposed tracking mechanism demonstrates bounded execution time variability and enhanced robustness in real-time scenarios. The findings confirm that Koopmanbased tracking provides a viable and computationally efficient enhancement for dynamic state estimation in power systems.