In the absence of aerodynamic pitch control, it is required to drive the wind turbine at an optimal speed for a given wind speed to extract maximum power from a wind turbine generator system. Due to unpredictable wind speed fluctuations, operating at maximum power point is a difficult task to undertake. This paper presents a maximum power point tracking (MPPT) algorithm for variable speed wind turbines. The strategy uses neural networks and genetic algorithms to learn the wind turbine's nonlinear dynamic model and achieve accurate tracking. As such, robustness to unpredictable wind uncertainties is achieved. Simulation results for different situations highlight the performance of the proposed controller under various wind speed operating conditions.

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Conference 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014
Chaoui, H, Miah, S. (Suruz), Oukaour, A. (Amrane), & Gualous, H. (Hamid). (2014). Maximum power point tracking of wind turbines with neural networks and genetic algorithms. In Proceedings, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society (pp. 197–201). doi:10.1109/IECON.2014.7048499