Operating Conditions Forecasting for Monitoring and Control of Electric Power Systems

Two approaches are proposed for short-term forecast of the parameters of expected operating conditions. The Kalman filter based algorithms and the modern technologies of an artificial intelligence and nonlinear optimization algorithms are employed for dynamical state estimation. The new approach combining the artificial neural networks and the Hilbert-Huang transform is designed in order to increase the accuracy of operating conditions forecasting. Numerical experiments on real time series have demonstrated the improvement of the prediction.