Space Vector Modulated Model Predictive Control for Grid-Tied Converters

This article proposes a space vector modulated model predictive control (SVM2 PC) for voltage source converters. It overcomes limitations of standard finite control set model predictive controller, ensuring fast dynamic response, fixed switching frequency, and low computational burden. It combines control and modulation in a convex optimization problem with affine inequality constraints to minimize the tracking errors of the output variables. First, the sector of the space vector diagram where the unconstrained solution lies is identified. Then, using the Karush-Kuhn-Tucker conditions, feasible duty cycles are computed for both linear and overmodulation regions. The proposed SVM2 PC is extended to multilevel converters. Hardware in the loop results are presented for three different case studies: 1) Grid-forming inverter with LC filter; 2) two-level grid-following inverter with LCL filter; and 3) grid-following neutral point clamped inverter with LCL filter. Experimental results demonstrate that the proposed SVM2 PC provides good performance, fast response, and low computational burden when compared with previously reported alternatives.