IEEE Access (Jan 2022)
Hierarchical Single-Objective Model Predictive Control With Reduced Computational Burden in Cascaded H-Bridge Converter Based on 3-Level Flying Capacitor Unit Cell
Abstract
This paper presents a control algorithm to reduce the computational burden of model predictive control in a grid-connected single-phase $N$ -cell cascaded flying capacitor H-bridge (CFCHB) multilevel converter of a solid-state transformer. The proposed control algorithm is a finite-control-set model predictive control (FCS-MPC) that is based on a hierarchical structure controlling the grid current, DC-link voltage, and flying capacitor voltage, using each cost function through three layers. Unlike the conventional multi objective cost function that evaluates and compares all candidates to determine the optimal state, the proposed method uses a single-objective cost function by separating the control variables to determine the optimal state through calculation without comparison. Therefore, the amount of computation is significantly reduced compared with that of the conventional method, and the execution time is shortened. Thus, the sampling period can be shortened and the switching frequency can be increased. Further, it can lead to using a smaller inductor to produce a high-quality grid current, which can help reduce the system size and cost. The execution time is not large, even if the voltage level increases and the effect of time reduction is large compared to the existing method, because the computational burden of the proposed method is linearly proportional to the number of cells. The voltage level expansion is easy and suitable for digital system implementation, and the control performance is not affected. The effectiveness of the proposed method is verified through the simulation of a single-phase 250 kW 9-cell CFCHB multilevel converter using PSIM and an experiment with a single-phase 2 kW 3-cell CFCHB multilevel converter at the laboratory scale.
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