Abstract
This study proposes an adaptive sliding mode type-2 neuro-fuzzy control scheme for power-control and speed-control modes of doubly fed induction generator (DFIG). DFIG-based wind turbine system operates as variable-speed wind energy conversion system (WECS) with constant frequency. In the proposed controller design, a sliding mode control (SMC) strategy is used for online training of the parameters of type-2 fuzzy set (T2FS) membership functions. Due to the uncertainty of the wind speed and variation in parameters in the WECS, an interval T2FS is used in the proposed control scheme. Based on the controller inputs, the SMC adaptive strategy is used to tune the parameters of antecedent and consequent parts of T2FS. These inputs are active and reactive power and rotor speed errors and their time derivative. These inputs fed to the type-2 neuro-fuzzy system. The results of simulation for a 1.5 MW DFIG-based WECS are compared with the classical proportional–integral (PI) controller and type-1 neuro-fuzzy controller to validate the effectiveness of the proposed controller in power-control and speed-control regions. The results of simulation indicate that, in comparison to the PI and type-1 neuro-fuzzy controller, the proposed control scheme has better performance in both power-control and speed-control modes.
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