Abstract:
This thesis investigates the intelligent flux-oriented control of active and reactive powers
in a Doubly Fed Induction Generator (DFIG) to optimize its performance. The study
begins with a comprehensive review of the state-of-the-art in Doubly-fed induction machines,
focusing specifically on generators. It then gets into Artificial Neural Networks,
first as a concept and then as an advanced control strategy for our system. After that,
it also explores the Adaptive Neuro-Fuzzy Inference System (ANFIS), theoretically, and
then its application to our control system in a similar manner.
Various tests were conducted to evaluate the efficiency and robustness of both control
strategies. At the end, a comparative analysis was performed to highlight the strengths
and weaknesses of each approach. Simulation results in the MATLAB/SIMULINK environment
demonstrate that both controllers deliver excellent performance in term of
robustness. However, ANFIS exhibits a slight edge regarding response time and robustness.
The findings underscore the potential of intelligent control techniques in optimizing
DFIG-based wind turbines; This improvement is attributed to the adaptive and flexible
nature of intelligent controllers, which better handle the complexities and uncertainties
inherent in wind energy systems.
