This paper proposes a constrained modulated model-predictive control (M$^{2}$PC) scheme for an LC-filtered voltage-source converter (VSC). To tackle the coupling effects of the state variables in a second-order LC filter,… Click to show full abstract
This paper proposes a constrained modulated model-predictive control (M$^{2}$PC) scheme for an LC-filtered voltage-source converter (VSC). To tackle the coupling effects of the state variables in a second-order LC filter, a dual-objective cost function (CF) is used to explicitly track both capacitor voltage and inductor current references, which can achieve an improved voltage quality. To handle the state and control input constraints of VSCs, a constrained M$^{2}$PC scheme is proposed with an “online post-correction” constraint-handling technique. First, the unconstrained optimal voltage vector (OVV) is derived. It is generated by seeking the minimum analytical solution of the CF offline, simplifying the online implementation. Then, an “online post-correction” strategy is employed by reconsidering the constraints to correct the precalculated OVV online, which guarantees the future states within the allowed range. Finally, the corrected OVV is synthesized by the space vector modulation, resulting in a fixed switching frequency and low harmonics. Compared with the typical constrained model-predictive control, the presented scheme has the advantages of improved steady-state performance, flexible constraint-handling ability, and lower computational cost. Additionally, design procedures for weighting factor selection in the CF are given. Comparative experiments are investigated to verify the presented control strategy.
