A neuro-fuzzy control based torque tracking approach for doubly fed induction generator

In this paper a performances of neuo-fuzzy control based torque tracking approach for Doubly Fed Induction Generator (DFIG) is proposed. First, a mathematical model of DFIG written in an appropriate d-q reference frame is established to investigate simulations. In order to control the rotor currents of DFIG, a torque tracking control law is synthesized using PI controllers, under conditions of the stator side power factor is controlled at unity level. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performances of neuro-fuzzy controller (NFC) which is based on the torque tracking control algorithm are investigated and compared to those obtained from the PI controller. Results obtained in Matlab/Simulink environment show that the NFC is more robust, superior dynamic performance and hence found to be a suitable replacement of the conventional PI controller for the high performance drive applications.