An efficient model predictive control based on Lyapunov function for doubly fed induction generator fed by a T-type inverter

This paper proposed an efficient model predictive control strategy for a three-level T-type inverter connected to a doubly fed induction generator. A dynamic model is utilized to track the power reference, preserve the DC-link voltage balancing and reduce the inverter switching frequency and common-mode voltage. Unlike the traditional finite control set model predictive control, the stability of the closed-loop system and computational burden reduction are concurrently accomplished as the main contribution to the proposed strategy. At each sampling period, the proposed algorithm only considers the candidate control inputs which satisfy the stability condition taken from a suitable control Lyapunov function for evaluating the optimization problem. Due to model uncertainties and discrete set control input voltages, an attractivity set is employed to determine the stability and converge of the system. Therefore, the lower computation time of the optimization problem is the potential benefit compared with the conventional method. Simulation investigation with different operating conditions of a 2 MW wind turbine is carried out by MATLAB/Simulink. The achieved results confirm the stability guarantees while reducing the computational burden and obtaining the same control performances in comparison with the conventional method.