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Artificial neural network-based maximum power point tracking control for variable speed wind energy conversion systems

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4 Author(s)
Thongam, J.S. ; Dept. of Renewable Energy Syst., STAS Inc., Chicoutimi, QC, Canada ; Bouchard, P. ; Ezzaidi, H. ; Ouhrouche, M.

A new maximum power point tracking (MPPT) controller using artificial neural networks (ANN) for variable speed wind energy conversion system (WECS) is proposed. The algorithm uses Jordan recurrent ANN and is trained online using back propagation. The inputs to the networks are the instantaneous output power, maximum output power, rotor speed and wind speed, and the output is the rotor speed command signal for the WECS. The network output after a time step delay is used as the feed-back signal completing the Jordan recurrent ANN. Simulation is carried out in order to verify the performance of the proposed algorithm.

Published in:

Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE

Date of Conference:

8-10 July 2009