Improved Indirect Model Predictive Control for Modular Multilevel Converter

Abstract

Due to the ability to control several parameters by the cost function and the high dynamic response as well as direct modeling, the model predictive control (MPC) is among the best methods for controlling a modular multilevel converter (MMC). In conventional MPC, the optimal value of the cost function is obtained after calculating all switching states. If the number of sub-modules (SMs) increases, the computational volume also increases exponentially, which makes this method impossible to implement. In this paper, an improved indirect model predictive control (IIMPC) is presented to control the parameters of MMC, such as circulating and load currents and capacitor voltage. Moreover, this method can make the computational volume independent from the number of SMs. On the other hand, to control the voltages of the capacitors, a sorting algorithm has been used. The superiority and privilege of the proposed IIMPC are verified by simulation results of a five-level single-phase MMC with four SMs in each arm.