Model predictive control method with preselected control options for reduced computational complexity in modular multilevel converters (MMCs)

Even though Model Predictive Control (MPC) has a straightforward implementation, multivariable control capability and outstanding dynamic response, it suffers from an excessive increase in computational complexity when the number of submodules (SMs) per arm (N) is increased in Modular Multilevel Converters (MMCs). The high computational complexity of the MPC controller for MMCs can be improved by decoupling the submodule (SM) capacitor voltage control from the cost function and balance them in an external voltage sorting algorithm. In this paper, further reduction in the computational burden for the MPC is realized by the preselection of control options that are able to satisfy the control objectives in the next sampling time. The preselection algorithm generates a small number of control options to be computed by a single MPC loop at each sampling time, thus, it can generate 2N+1 output voltage levels and suppress the circulating currents. The performance of the MMC operating with the proposed method is verified by simulation and experimental results.