Double Vector Model Predictive Control to Reduce Common-Mode Voltage Without Weighting Factors for Three-Level Inverters
Tong Liu, Alian Chen, Changwei Qin, Jie Chen, Xiaoyan Li
Abstract
The conventional model predictive control (MPC) suffers from high common-mode voltage (CMV) magnitude and large current ripples. In this article, the reduced CMV MPC strategy with double vector (RCMV-MPCDV) is proposed for three-level inverters, which adopts double vector to reduce the CMV and current ripples simultaneously in per-sampling period. First, based on geometric relationship of median, a novel cost function that evaluates median line distance is proposed to reduce the computational complexity, rather than evaluating the closest distance. Second, in order to reduce the computational burden, the double vector preselection algorithm is presented with fewer evaluation times. Third, the candidate vectors are reclassified and regrouped from partial basic vectors, which restricts CMV magnitude within one-sixth of dc-link voltage. In addition, to select more satisfactory double vectors for RCMV-MPCDV scheme, two improved methods are proposed to compensate the losing candidate vectors. The simulation and experimental results with steady-state performance and dynamic response validate the effectiveness of the proposed method.