Discrete-Time Neural Input Output Feedback Linearization Control for a DFIG based Wind Turbine

Abstract

Wind energy has many advantages, because it does not pollute and is an inexhaustible source of energy. The Double Fed Induction Generator (DFIG) is one of the most important electric generator used for Horizontal Axis Wind Turbine (HAWT). In this paper, a Discrete-time Neural Input-Output Feedback Linearization Control (N-IOFLC) for DFIG is proposed to force the rotor currents to track specified reference defined form the desired stator active and reactive powers, using the fourth order d − q model of the DFIG with rotor and stator currents as state variables. The DFIG rotor is coupled to the grid via a power converters, while the stator is linked directly to the electric network. The proposed controller is designed using a Recurrent High Order Neural Network (RHONN), trained with an Extended Kalman Filter (EKF). The RHONN works as an identifier to obtain an adequate model of DFIG which is robust to disturbances and parameter variations. The effectiveness of the proposed control scheme of DFIG is confirmed by simulation results obtained using Matlab/Similink.