A Computationally Efficient Model Predictive Control for Star-Connected Cascaded Static Synchronous Compensator Under Unbalanced Conditions

The conventional model predictive control (MPC) experiences a tremendous number of switching state evaluations per control cycle when applied to multilevel converters, which makes it computationally impractical. To address this issue, this article proposes a computationally efficient MPC (EMPC) for the cascaded H-bridge (CHB) static synchronous compensator (STATCOM), which is enabled by the sorting of the H-bridge submodules upon their dc capacitor voltages, such that the candidate switching states are restricted to the scope in which the lower-voltage submodules are charged and the higher-voltage submodules are discharged. And therefore, the exponentially increasing switching states in the CHB-STATCOM can be dramatically reduced while the computational efficiency is greatly improved. In addition, prior to control implementation, a generic discrete-time prediction model with the incorporation of a zero-sequence component is established to merge the balanced and unbalanced scenarios into one framework, so as to address the issues related to either grid and/or load unbalances in the CHB-STATCOM for distribution grids. Both simulation and hardware-in-loop experimental studies are provided to verify the effectiveness of the EMPC strategy.