Abstract
The variable speed wind energy conversion system (WECS) produces variable output power that cannot be directly supplied to the load. It needs a suitable controller to match the amplitude and frequency of the output voltage with the load. This paper proposes a radial basis function neural network (RBFNN)-based controller as a maximum power point tracker (MPPT) to extract maximum power available from wind. MATLAB-/Simulink-based simulations have given better results in comparison with FLC and MLFFNN.
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Dinku, T.M., Manshahia, M.S. (2023). RBFNN for MPPT Controller in Wind Energy Harvesting System. In: Manshahia, M.S., Kharchenko, V., Weber, GW., Vasant, P. (eds) Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26496-2_2
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DOI: https://doi.org/10.1007/978-3-031-26496-2_2
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