Hierarchical single-objective variable double voltage vector model predictive control with low computational burden for cascaded H-bridge multilevel converter

In this paper, a single objective variable double voltage vector model predictive control (SOV-DVVMPC) to lower the computational burden for single-phase cascaded H-bridge (CHB) converters is proposed. The proposed algorithm is based on a hierarchical structure to control the grid-connected current and capacitor voltage without a weighting factor. Firstly, layer I controls the grid-connected current to select the optimal region, the cost function related to the voltage region is designed and the optimal state is calculated directly by analyzing the linear relationship between the divided region and its adjacent voltage levels, which can reduce the computational burden. Correspondingly, the optimal vectors are symmetrically distributed over the entire control cycle by a modulation principle, fulfilling the fixed switching frequency (FSF) of the system. Then, layer II uses redundant switching states to maintain capacitor voltage balancing. Finally, a single-phase CHB converter experiment setup is constructed, the results show that compared with DVV-MPC, the execution time of SOV-DVVMPC is reduced by 26.4%, and compared with traditional MPC, the switching frequency is fixed and the output current quality is improved. The method aims to reduce the computational burden of model predictive control of cascaded H-bridge multilevel converters. It uses a double-vector voltage control approach to achieve a fixed switching frequency, and a hierarchical structure to control the grid current and dc-side output capacitor voltages. image