This paper presents an improved finite-control-set model predictive control (MPC) strategy for the doubly fed induction generator (DFIG). The MPC-based controller's main drawback is high computational burden, whic...
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This paper presents an improved finite-control-set model predictive control (MPC) strategy for the doubly fed induction generator (DFIG). The MPC-based controller's main drawback is high computational burden, which requires a long time to perform online optimization. In this regard, real-time implementation becomes impossible in some cases. Thereupon, this paper suggests a variable sampling time in the prediction process to achieve a balance between the prediction horizon and computational burden. In other words, compared to the conventional MPC-based techniques, longer horizon is predicted with fewer samples. In this method, there are two sampling times including a fixed sampling time for the system discretization structure, and a variable sampling time used in the prediction process. The proposed technique also utilizes a comparative incremental algorithm to prevent checking all possible control inputs over the objective function in order to reduce the computational time further. In addition, a penalty term, which includes the number of switch changes, is added to the cost function to reduce the switching frequency. Both the simulation and experimental results show that the proposed MPC can significantly improve the DFIG control system's performance by increasing the accuracy of tracking response and reducing the computational time. The experimental test is carried out by the hardware in loop system by using TMS320F28338 and PC-Lab card. The computational delay caused by the digital signal processor and other influential factors are compensated by using the prediction plus prediction approach. The results confirm that the proposed controller has a lower overshoot and power ripple compared to the vector controller and direct torque controller. It also reduces the computational time up to 40% and reduces the average switching frequency by 12% in comparison to the conventional MPC.
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