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The wind power generation is a pollution free technique for electricity production, and also it does not requires additional driving source like coal hence after installing once the generation cost can be minimize but the power generation by this method requires a continuous wind flow in all weather. Unfortunately the wind velocity is weather dependent and changes widely with time to time. This create difficulties in the system scheduling, dispatching & power distribution hence it requires a predictive system which can estimate the wind velocity in advance & so will help in power management. In this paper we analyzed RBF (radial basis function), FFBP (feed forward back propagation) & CFBP (cascade forward back propagation) neural networks for this purpose. At last the simulation results and discussion are presented.

OPTIMIZATION OF THE PERFORMANCE OF THE WIND POWER GENERATION UNIT BY USING DIFFERENT NEURAL NETWORKS