Model Predictive Control with Autotuning Weighting Factors for Single-Phase Six-Level Hybrid-Clamped Converters

Yong Yang, Jianyu Pan, Huiqing Wen, Xinan Zhang, Yiwang Wang, Will Perdikakis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Compared with the conventional multilevel converters, the hybrid-clamped converter (HCC) reduces the number of power devices, which brings down the converter cost while enhancing its reliability for medium-voltage high-power applications. As one of typical medium-voltage HCCs, the single-phase, six-level HCC (6L-HCC) faces the main technical challenge of the voltage balance among five inner capacitors. Most of the existing pulsewidth modulation techniques cannot solve this problem. Advanced control methods that directly apply a selected voltage vector, such as the classical model predictive control (MPC), may fail to balance the inner capacitor voltages during low output frequencies. In view of this situation, this article proposes an improved MPC approach with autotuning weighting factors. It contributes to ensure excellent performance of 6L-HCC under all operating conditions with well-balanced inner capacitor voltages. Simulation and experimental results are proposed to verify the effectiveness of the proposed approach.

Original languageEnglish
Article number9145801
Pages (from-to)7946-7956
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number9
DOIs
Publication statusPublished - Sep 2021

Fingerprint

Dive into the research topics of 'Model Predictive Control with Autotuning Weighting Factors for Single-Phase Six-Level Hybrid-Clamped Converters'. Together they form a unique fingerprint.

Cite this