Regulation of Active and Reactive Powers in Doubly-Fed Induction Generators Utilizing Proportional-Integral and Artificial Neural Network Controllers

Bouzidi, Mohammed (Department of Sciences and Technology, Faulty of Sciences and Technology, University of Tamanghasset, Algeria. Energy and Materials Laboratory, University of Tamanghasset, Algeria)
Nasri, Abdelfatah (Laboratory Smart Grid and Renewable Energy SGRE University Tahri Mohamed Bechar Algeria)
Hafsi, Oussama (Department of Electrical Engineering, LDDI Laboratory, Ahmed Draia University, Adrar, Algeria)
Faradji, Boubakar (Energy and Materials Laboratory, University of Tamanghasset, Algeria)

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol 12, No 3: September 2024

In this paper, vector orientation and neural networks are used to simulate and regulate a Doubly Fed Induction Generator (DFIG) wind turbine. The aerodynamic turbine and DFIG dq models are developed. PI current regulation is used in vector control to separate active and reactive power control. To reproduce the PI response, training networks create a different neural vector control scheme. Comparative simulations confirm the effectiveness of both control methods in following set points and counteracting disturbances. The neural vector control scheme outperforms the PI scheme in managing short-term changes. In contrast to the PI control, it has quicker response times for both rising and settling. Neural vector control enables precise and rapid tracking of electromagnetic torque. Neural vector control could improve the performance of DFIG wind turbines because it has an adaptive architecture that lets it respond well to changes in parameters and maintain its accuracy over time. Additional investigation is needed to improve neural network training techniques and incorporate them with conventional control systems.