Model Predictive Control of a DFIG-Based Wind Turbine Using Three-Level NPC Converter

The three-level NPC converters applied in wind power is increasing, but the traditional control strategy of doubly-fed induction generator (DFIG) cannot balance the midpoint voltage well, and the modulation algorithm is more complicated. This paper establishes the mathematical models of DFIG and NPC converters, and uses the characteristics of model predictive control multi-objective optimization to control the three-level converter on the machine side to improve the performance of the system. At the same time, the preselected vector is optimized, which reduces the average switching frequency and computational complexity of the control algorithm. Finally, simulations verify the effectiveness of the proposed control strategy.