Performance analysis and control of brushless doubly fed induction generators

Nowadays, wind energy is gaining more and more importance with the increasing energy demand. The amount of energy produced from wind energy depends on the type of generator used with the turbine. Brushless doubly fed induction generators are one of the widely used generator type among the electric generator types used with wind turbines due to their advantages. In order to get the most out of brushless doubly fed induction generators, an ideal control method should be used among alot of control methods that can be used. In this study, first of all, the control methods of BDFIG have been analyzed, then the most suitable design method has been searched by comparing the design methods of the model predictive control method, which is a suitable control method for this machine. Model 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's rotor side control. 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 Matlab / Simulink environment based on 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. Keywords: Brushless doubly-fed induction generators, Model predictive control, Transfer function, State space