Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system

To achieve maximum power point tracking (MPPT) for wind power generation systems, the rotational speed of wind turbines should be adjusted in real time according to wind speed. In this paper, a Wilcoxon radial basis function network (WRBFN) with hill-climb searching (HCS) MPPT strategy is proposed for a permanent magnet synchronous generator (PMSG) with a variable-speed wind turbine. A high-performance online training WRBFN using a back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller is designed for a PMSG. The MPSO is adopted in this study to adapt to the learning rates in the back-propagation process of the WRBFN to improve the learning capability. The MPPT strategy locates the system operation points along the maximum power curves based on the dc-link voltage of the inverter, thus avoiding the generator speed detection.