Model Predictive Control of Doubly Fed Induction Generator (DFIG) Based on Transfer Function

The worldwide need for environmentally friendly and sustainable energy sources is continuously growing and has intensified research efforts toward wind power generation. Among the various generator technologies used in wind energy systems, asynchronous (induction) machines are highly favored due to their mechanical robustness, economic feasibility, and reliable operational characteristics. In particular, the Doubly Fed Induction Generator (DFIG) is extensively employed in variable-speed wind turbines owing to enables flexible power regulation and efficient energy conversion.Accordingly, this research focuses on developing a Model Predictive Control (MPC) scheme derived from a transfer function framework to upgrade operational behavior of DFIG system. The controller design is supported by comprehensive mathematical modeling and simulation analysis to achieve improved dynamic performance and system stability. To achieve this objective, a comprehensive theoretical analysis of DFIG has carried out. MPC based on Transfer Function has been designed, to examine the effectiveness of the proposed technique, a model has been developed using MATLAB/Simulink environment. As indicated by the simulation results, It is evident that transient state has been reduced (about 19.78%), the proposed strategy has reduced fluctuation of speed (165.1-164.9 = 0.2 rad/sec) that means maximum error of speed equal to 0.12%, Vbus has been stabilized at the desired level (1150 v), therefore indicating that proposed strategy has improvements in dynamic response of system.the proposed strategy has reduced the ripple of current for d-q axis ,in addition to achieve Q regulation by managing DC-link voltage level.