Regulation of Active and Reactive Powers in Doubly-Fed Induction Generators Utilizing Proportional-Integral and Artificial Neural Network Controllers - Bouzidi, Mohammed; Nasri, Abdelfatah; Hafsi, Oussama; Faradji, Boubakar (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 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.