Low-complexity Repetitive Double-Vector Model Predictive Control Optimization Strategy for T-type Three-level Converter - Fang, Jian; Li, Ruihua; Wang, Hanqing; Sun, Chenwei; Hu, Bo (IEEE transactions on power electronics, 2025)
Conventional multi-vector model predictive control (MV-MPC) for three-level converters suffers from high computational complexity and low optimal voltage-vector (VV) tracking accuracy, resulting in poor overall performance of converter. To address these issues, this paper proposes a low-complexity repetitive double-vector model predictive control (LC-RDVMPC) optimization strategy for T-type three-level converters. To overcome the limited control bandwidth of conventional MV-MPC, this paper presents a SVPWM-sector identification method. Through constructing assistant lines to rapidly locating the sector of reference VV, this method reduces the times of cost function calculation and virtual VVs synthesis, decreasing the computational complexity of MV-MPC and improving the bandwidth of control system. Moreover, a repetitive double-vector synthesis method is proposed for synthesizing virtual VV in two steps. An auxiliary VV is generated firstly, and then virtual VV with capability of covering the whole output voltage range is synthesized, which improves the optimal VV tracking accuracy of MV-MPC. Finally, the effectiveness of the proposed method is verified through experiments.