Artificial Neural Networks (ANNs) - Based Robust Tracking Control for DFIG integrated in WECS

The present paper deals with the introduction of Artificial Intelligent Systems “Neural Networks (ANNs)” in a new power control scheme of wind turbine associated with Doubly Fed Induction Machine. These Neural Networks will be replacing the four fuzzy logic controllers that exist in the process and trained by using the database of this latter. So, the conceived neural network controllers are used to regulate the power flowing between the stator of the DFIG and the power network. The performances of the proposed ANNs controllers added to our system were tested and compared with two other previously proposed techniques based on Proportional-Integral (PI) and Fuzzy Logic (FL) controllers. The simulation test was carried out by means of computational simulations in Matlab/Simulink environment. The obtained results show that the proposed controller exhibits better behavior in terms of settling time, overshoot, robustness with respect to machine parameters variation, and good tracking references.