Neuro fuzzy based indirect vector control doubly fed induction generator

Doubly Fed Induction Generator is especially used as wind power generators. An Indirect vector control is used better dynamic performance of DFIG. But the performance of closed loop control is largely influenced by the type of speed and current controllers used. Usually, proportional-integral (PI) based current controllers are used due its simplicity. But these PI controllers suffer from tuning problem. To overcome the problem, this paper presents Adaptive Neuro-fuzzy based speed controller for indirect vector controlled VSI fed doubly fed induction generator to minimize the torque ripple. The proposed ANFIS controller significantly reduces the torque ripple compared to that of conventional PI controllers without using any filter. The performance of the indirect vector controlled induction motor drive has been simulated at different operating conditions using the ANFIS controller and the obtained results are compared with PI controller. Additionally in the implemented SVM algorithm, the switching times proportional to the instantaneous values of the reference phase voltages. It eliminates the need the calculation of sector and angle information unlike conventional SVM method.