Robust Forecasting-Aided State Estimation for Power System Against Uncertainties

Abstract

Accurate forecasting-aided state estimation plays a vital role in reliable and secure operation of power systems. However, most of existing methods are unable to deal with the uncertainties that might be caused by uncertain model parameters or uncertain noise statistics. Therefore, the performance of these methods may be inevitably degraded significantly. To address these issues, based on the robust control theory, in this paper, by incorporating the modified innovation based Sage-Husa estimator of noise statistics and the proposed estimation error covariance matrix adaptive technique, a novel adaptive $H_{\infty }$ extended Kalman filter (AHEKF) is developed to realize robust forecasting-aided state estimation for power system with model uncertainties. Extensive simulations carried out on several different test systems demonstrate the efficiency and robustness of the proposed method.