Background and Objectives: Wind turbines as one of the means of producing electrical energy from renewable and clean energies have been the focus of many researchers. The discussion of turbine control in order to produce more power and its economical use against fossil fuels has challenged different control methods.

Methods: In the current research, the purpose of using intelligent fuzzy controllers is to improve the output power and stabilize it when necessary due to its robustness. For this purpose, the induction generator with two-way feeding and variable wind was modeled first, then phase controllers will be designed to separately control active and reactive powers, reduce interference and uncertainty effects. that we used the particle swarm algorithm and the best rules and fuzzy parameters of the intelligent fuzzy system based on deep learning to create the rule and interference system to better performance.

Findings: The comparison of the simulation results of intelligent fuzzy and PI controllers shows the better performance and efficiency of the fuzzy controller in terms of more stability, steady state error and less settling time than the PI controller used in the system. The performance accuracy of the fuzzy controller based on deep learning due to rule extraction and optimal PSO design using random forest algorithm for this system is suitable according to the obtained outputs and the system is controlled in less than 0.4 seconds.

Conclusion: Our integrated and hybrid algorithm shows the good performance due to accuracy and precision parameters, applying the deep learning in order to select the effective parameters on system design for rule extraction in fuzzy and create the decision making in PSO leads the novel way to approach the results.