Limitations and Accuracy of a Continuous Reduced-Order Model for Modular Multilevel Converters

Lopez, AM; Quevedo, DE; Aguilera, RP; Geyer, T; Oikonomou, N

Limitations and Accuracy of a Continuous Reduced-Order Model for Modular Multilevel Converters

Lopez, AM Quevedo, DE Aguilera, RP

Geyer, T Oikonomou, N

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Power Electronics, 2018, 33 (7), pp. 6292 - 6302
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Lopez, AM	en_US
dc.contributor.author	Quevedo, DE	en_US
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Geyer, T	en_US
dc.contributor.author	Oikonomou, N	en_US
dc.date.issued	2018-07-01	en_US
dc.identifier.citation	IEEE Transactions on Power Electronics, 2018, 33 (7), pp. 6292 - 6302	en_US
dc.identifier.issn	0885-8993	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125031
dc.description.abstract	© 1986-2012 IEEE. This paper analyzes the limitations of a reduced-order model for modular multilevel converters (MMCs) by elucidating the relation between its accuracy, operating frequency, and converter parameters. A reduced-order simplifies the analysis of the MMC and thereby may provide additional information about the converter behavior. However, the accuracy of such model depends on several factors. In this paper, the effect of approximating the converter as a continuous system by neglecting quantization issues associated with having a finite number of modules is studied in detail. The analysis is done based on Fourier-series approximations with which it is possible to elucidate the relationship between the resonant frequencies of the MMC and the error of the reduced-order model. With the Fourier approximation, it is also possible to characterize resonant frequencies of the converter, both numerically and analytically, in terms of the converter parameters. The results can serve as a tool to identify situations when the reduced-order model produces good and also less accurate approximations especially when a low number of modules is available.	en_US
dc.relation.ispartof	IEEE Transactions on Power Electronics	en_US
dc.relation.isbasedon	10.1109/TPEL.2017.2743006	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Limitations and Accuracy of a Continuous Reduced-Order Model for Modular Multilevel Converters	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	33	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 1986-2012 IEEE. This paper analyzes the limitations of a reduced-order model for modular multilevel converters (MMCs) by elucidating the relation between its accuracy, operating frequency, and converter parameters. A reduced-order simplifies the analysis of the MMC and thereby may provide additional information about the converter behavior. However, the accuracy of such model depends on several factors. In this paper, the effect of approximating the converter as a continuous system by neglecting quantization issues associated with having a finite number of modules is studied in detail. The analysis is done based on Fourier-series approximations with which it is possible to elucidate the relationship between the resonant frequencies of the MMC and the error of the reduced-order model. With the Fourier approximation, it is also possible to characterize resonant frequencies of the converter, both numerically and analytically, in terms of the converter parameters. The results can serve as a tool to identify situations when the reduced-order model produces good and also less accurate approximations especially when a low number of modules is available.

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http://hdl.handle.net/10453/125031