Model predictive control with reduced computational burden of modular multilevel converter

Advances in voltage source converter-based high-power applications are driven by the Modular Multilevel Converter (MMC) and its variants, while the practical implementation of Model Predictive Control (MPC) faces challenges in MMCs with numerous submodules per arm. This paper proposes a Folding MPC...

Full description

Saved in:

Bibliographic Details
Published in:	12th International Conference on Power Electronics, Machines and Drives (PEMD 2023) Vol. 2023; pp. 357 - 364
Main Authors:	Kadhum, H. T., Alan, W., Marco, R., Pericle, Z., Patrick, W.
Format:	Conference Proceeding
Language:	English
Published:	The Institution of Engineering and Technology 2023
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Advances in voltage source converter-based high-power applications are driven by the Modular Multilevel Converter (MMC) and its variants, while the practical implementation of Model Predictive Control (MPC) faces challenges in MMCs with numerous submodules per arm. This paper proposes a Folding MPC strategy combined with a pre-processing sorting algorithm method to solve this problem. Five control objectives, including AC current, DC current, circulating current, arm energy, and leg energy, are optimized directly in a single cost function. The results show that the MMC operates satisfactorily under various operating conditions when using the proposed technique. The practical application of the MMC, even with a large number of submodules (SMs) per arm, is made possible by significantly reduction in the switching states and computational burden.
DOI:	10.1049/icp.2023.2023