An Extended Dynamic Mode Decomposition Rolling Prediction Method for VSC-HVDC System

Power systems with high penetration of renewable energy sources often experience significant frequency deviations and severe oscillations due to reduced inertia and weak damping. This paper proposes a rolling prediction-based EDMD (RP-EDMD) method to construct the state-space model of power systems and predict the trajectories of system states. The rolling prediction updating mechanism can dynamically update the equivalent state-space model with the variation of disturbances to improve the prediction accuracy under uncertainty scenarios. Moreover, the Radial Basis Function (RBF) is employed to improve the accuracy of EDMD state estimation. The proposed estimator leverages a limited set of real-time measurements to reconstruct an equivalent state-space model, enabling rapid online estimation of key state trajectories of power systems. Time-domain simulations validated that the trajectories of the dominant modes of the power systems can be effectively and accurately estimated by the proposed method, which has the potential to enhance real-time monitoring and control of power systems with the integration of renewable energy.