Al-khamis, Omran Al-abedGumus, Bilal2024-04-242024-04-2420231975-01022093-7423https://doi.org/10.1007/s42835-022-01179-zhttps://hdl.handle.net/11468/15015Model predictive control (MPC) is an important control technique for Brushless doubly-fed induction generators (BDFIGs) which are commonly used for wind turbines, and its control performance can be affected by the MPC design. In this study, the performances of the transfer function based model and the state space based model are compared in MPC design for BDFIG. For this purpose, transfer function based model predictive control (TFMPC) and state space based model predictive control (SSMPC) were developed for BDFIG. The vector control of the BDFIG was simulated using the designed MPCs. The simulation results have shown that TFMPC produces better results than SSMPC. Additionally,The simulation results clearly show the effectiveness and good response of TFMPC in both dynamic operation and steady-state operation. TFMPC reduces power ripple and decreases harmonics, resulting in an improvement in the quality of the electrical power generated by the BDFIG. The reference value (set point) was brought closer to the set point with TFMPC, and the duration of the transient condition was also reduced in this system. The study demonstrated that using the transfer function to calculate the parameters of the MPC can eliminate the drawbacks of other design models.eninfo:eu-repo/semantics/closedAccessBrushless Doubly Fed Induction GeneratorModel Predictive ControlState Space Model Based Model Predictive Control (Ssmpc)Transfer Function Model Based Model Predictive Control (Tfmpc)Article181111121WOS:0008355972000042-s2.0-8513533460210.1007/s42835-022-01179-zQ2Q3