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Received: 14 December 2018 Accepted: 27 March 2019 Although the numerous advantages of wind turbine, the nonlinear characteristic (P–Ω) providing a unique maximum power point is the most drawbacks. Therefore a maximum power point tracker is usually adopted, which the Tip-Speed ratio, the perturbation and observation and the optimum torque control methods are widely used. In this article, a novel neural network (ANN) MPPT controller based on perturbation and observation has been projected and studied. The ANN MPPT controller of wind turbine system is established in two independents steps: the offline operation mode is mandatory for training of different neural networks parameters and the Online operation mode where the most advantageous neural network MPPT controller is implanted in wind turbine system. The developed MPPT controller is tested on wind turbine based–DFIG generator, which it is controlled by sliding mode control (SMC). Simulation results are presented and discussed.

A Novel Hybrid MPPT Controller Using (P&O)-neural Networks for Variable Speed Wind Turbine Based on DFIG