Forecasting power system state variables on the basis of dynamic state estimation and artificial neural networks

This paper is devoted to the technique of forecasting all state variables for a short term. Kalman filter-based algorithms of dynamic state estimation and learned artificial neural networks are used to forecast the state vector components. The trend should be taken into consideration to forecast the state vector components for more than 5 min. The trend is forecasted based on the special table of trends that is filled beforehand for the studied state variable by using two artificial neural networks.