Fuzzy-based self-tuning model predictive direct power control of grid-connected multilevel converters

Aghdam, MM; Aguilera, RP; Li, L; Zhu, J

Fuzzy-based self-tuning model predictive direct power control of grid-connected multilevel converters

Aghdam, MM Aguilera, RP

Li, L

Zhu, J

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Publication Type:: Conference Proceeding
Citation:: 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017
Issue Date:: 2017-10-02

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Field	Value	Language
dc.contributor.author	Aghdam, MM	en_US
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.date.issued	2017-10-02	en_US
dc.identifier.citation	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017	en_US
dc.identifier.isbn	9781538632468	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119560
dc.description.abstract	© 2017 IEEE. This paper proposes a self-tuning model predictive direct power control (MPDPC) strategy for power flow control and power quality improvement in grid-connected power converters. At each sampling instant, a fuzzy logic controller is used to determine online the best weighting factor values for a given operating point. These values are then used to solve the multi-objective optimal control problem associated to the MPDPC. The optimal solution that minimizes the multi-objective cost function is chosen as the input (power switch state). The proposed method is examined through a case study and verified numerically via MAT LAB SIMULINK. A comparative study is conducted to demonstrate the effective performance of this approach. As a result of the proposed weighting factor online tuning, an improved performance in terms of total harmonic distortion and average switching frequency is attained when compared with fixed weighting factors.	en_US
dc.relation.ispartof	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017	en_US
dc.relation.isbasedon	10.1109/ICEMS.2017.8056425	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Fuzzy-based self-tuning model predictive direct power control of grid-connected multilevel converters	en_US
dc.type	Conference Proceeding
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
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. This paper proposes a self-tuning model predictive direct power control (MPDPC) strategy for power flow control and power quality improvement in grid-connected power converters. At each sampling instant, a fuzzy logic controller is used to determine online the best weighting factor values for a given operating point. These values are then used to solve the multi-objective optimal control problem associated to the MPDPC. The optimal solution that minimizes the multi-objective cost function is chosen as the input (power switch state). The proposed method is examined through a case study and verified numerically via MAT LAB SIMULINK. A comparative study is conducted to demonstrate the effective performance of this approach. As a result of the proposed weighting factor online tuning, an improved performance in terms of total harmonic distortion and average switching frequency is attained when compared with fixed weighting factors.

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http://hdl.handle.net/10453/119560