Chapter 4 - Model predictive control of modular multilevel converters

Abstract

Model predictive control (MPC) is a promising tool to achieve multiple control objectives of a modular multilevel converter (MMC). This book chapter presents direct model predictive control (DMPC) and indirect model predictive control (IMPC) schemes for an MMC. The DMPC and IMPC schemes use sampled data models of an MMC to predict the control variables such as output current, circulating current, and submodule (SM) capacitor voltage. In the DMPC scheme, these predictions are performed for all possible switching states of an MMC. The minimization of error between the reference and predicted control variables is fulfilled through a single cost function. The optimal switching state corresponding to minimum cost value is selected and applied to the MMC. On the other hand, the IMPC combines a predictive algorithm with a classical voltage balancing strategy to control an MMC. The predictive algorithm is designed to control the output current and circulating current of an MMC. These control requirements are fulfilled through a cost function, which is evaluated for all possible voltage levels of an MMC. The optimal voltage level corresponding to the minimum cost value is selected and applied to the voltage balance strategy. The voltage balance strategy ensures voltage balance among the SMs in an arm based on the information of optimal voltage level, the instantaneous value of SM capacitor voltage, and arm current direction. Finally, an optimal switching state is generated and applied to an MMC. The simulation studies are conducted to verify the performance of DMPC and IMPC schemes for an MMC.