Hybrid genetic algorithms for forecasting power systems state variables

A problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The input signal is decomposed into orthogonal basis functions using the Hilbert-Huang transform. The hybrid-genetic algorithm is applied to optimal training of the support vector machine and artificial neural network. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm empowered with the Hilbert-Huang transform.