Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters

Cuzmar, RH; Mora, A; Pereda, J; Poblete, P; Aguilera, RP

Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters

Cuzmar, RH Mora, A Pereda, J Poblete, P Aguilera, RP

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2024, 71, (5), pp. 5137-5147
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Cuzmar, RH
dc.contributor.author	Mora, A
dc.contributor.author	Pereda, J
dc.contributor.author	Poblete, P
dc.contributor.author	Aguilera, RP
dc.date.accessioned	2024-03-08T18:47:09Z
dc.date.available	2024-03-08T18:47:09Z
dc.date.issued	2024-05-01
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2024, 71, (5), pp. 5137-5147
dc.identifier.issn	0278-0046
dc.identifier.issn	1557-9948
dc.identifier.uri	http://hdl.handle.net/10453/176392
dc.description.abstract	The modular multilevel matrix converter (M3C) is a direct ac-ac power converter that presents several features such as scalable and flexible structure, high efficiency, fault-tolerant capability, and high power quality. However, this converter requires a complex control strategy to govern the cluster currents and balance the internal floating capacitor voltages among clusters and cells. To fulfill these requirements, this article proposes a long-horizon sequential finite-control-set model predictive control (sFCS-MPC). The resulting predictive strategy is formulated to perform both the local cell balancing control and to regulate cluster currents of the M3C in a unified control algorithm, considering the nonlinear and coupled dynamic model of each cluster. Additionally, the total available combinations and the average cell switching frequency are reduced. The proposed sFCS-MPC is implanted for a prediction horizon from one to four, and compared with some previous FCS-MPC approaches. Experimental results on a 3-kW prototype are provided to demonstrate the effectiveness and high-quality performance of the proposed strategy.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/DP210101382
dc.relation.ispartof	IEEE Transactions on Industrial Electronics
dc.relation.isbasedon	10.1109/TIE.2023.3286013
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters
dc.type	Journal Article
utslib.citation.volume	71
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-08T18:47:00Z
pubs.issue	5
pubs.publication-status	Accepted
pubs.volume	71
utslib.citation.issue	5

Abstract:

The modular multilevel matrix converter (M3C) is a direct ac-ac power converter that presents several features such as scalable and flexible structure, high efficiency, fault-tolerant capability, and high power quality. However, this converter requires a complex control strategy to govern the cluster currents and balance the internal floating capacitor voltages among clusters and cells. To fulfill these requirements, this article proposes a long-horizon sequential finite-control-set model predictive control (sFCS-MPC). The resulting predictive strategy is formulated to perform both the local cell balancing control and to regulate cluster currents of the M3C in a unified control algorithm, considering the nonlinear and coupled dynamic model of each cluster. Additionally, the total available combinations and the average cell switching frequency are reduced. The proposed sFCS-MPC is implanted for a prediction horizon from one to four, and compared with some previous FCS-MPC approaches. Experimental results on a 3-kW prototype are provided to demonstrate the effectiveness and high-quality performance of the proposed strategy.

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http://hdl.handle.net/10453/176392