A Robust Forecasting-aided State Estimation Algorithm for Power System

Real-time monitoring and control of system operation states play a significant role in the secure and economical operation of power systems. A robust forecasting-aided state estimation algorithm for power systems, called generalized maximum likelihood extended kalman filter (GM-EKF), is proposed. On the basis of forecasting-aided state estimation, GM-EKF consists of such processes as construction of a linear regression model, identification of abnormal value, robust pre-whitening noise and robust filtering. GM-EKF can effectively decrease the impact of the identification of abnormal value on estimation results if noise follows normal distribution. Simulation experiments are carried out on various systems in such cases as normal condition, multiple bad data and sudden load change, which verify the feasibility and robustness of GM-EKF.