Sensorless maximum wind energy capture based on input output linearization and sliding mode control

This paper proposes a new sensorless direct power control (DPC) for wind turbine system driven by doubly fed induction generator (DFIG) in order to track maximum absorbable power in different wind speeds. A generalized regression neural network is used to estimate wind speed and tip speed ratio. Then the desired optimum power is determined online as a function of tip speed ratio for per wind speed. Finally new direct power control (DPC) employs input output linearization and sliding mode nonlinear controller for robust control of active and reactive power and obtaining maximum power from wind turbine. Also constant switching frequency is achieved by using space vector modulation. Simulation results on 660-kw wind turbine are provided and compared with those of classic stator-flux oriented vector control. Results show that the proposed controller using the new algorithm has low error for tracking maximum power in compression with the existing controller in presence machine parameters variation.