Model-Based Predictive Control with Graph Theory Approach Applied to Multilevel Back-to-Back Cascaded H-Bridge Converters

Abstract

The multilevel back-to-back cascaded H-bridge converter (CHB-B2B) presents a significantly reduced components per level in comparison to other classical back-to-back multilevel topologies. However, this advantage cannot be fulfilled because of the several internal short circuits presented in the CHB-B2B when a conventional PWM modulation is applied. To solve this issue, a powerful math tool known as graph theory emerges as a solution for defining the converter switching matrix to be used with an appropriate control strategy, such as the model-based predictive control (MPC). Therefore, this research paper proposes a MPC with the graph theory approach applied to CHB-B2B which capable of not only eliminating the short circuit stages, but also exploring all the switching states remaining without losing the converter controllability and power quality. To demonstrate the proposed strategy applicability, the MPC with graph theory approach is tested in four different types of SST configurations, input-parallel output-parallel (IPOP), input-parallel output series (IPOS), input-series output-parallel (ISOP), and input-series output series (ISOS), attending distribution grids with different voltage and power levels. Real-time experimental results obtained in a hardware-in-the-loop (HIL) platform demonstrate the proposed strategy’s effectiveness, such as DC-link voltages regulation, multilevel voltage synthesis, and currents with reduced harmonic content.