An adaptive Neuro-Fuzzy controller for maximum power point tracking of photovoltaic systems

Mahdavi, M; Li, L; Zhu, J; Mekhilef, S

An adaptive Neuro-Fuzzy controller for maximum power point tracking of photovoltaic systems

Mahdavi, M Li, L

Zhu, J

Mekhilef, S

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Publication Type:: Conference Proceeding
Citation:: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2016, 2016-January
Issue Date:: 2016-01-05

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Field	Value	Language
dc.contributor.author	Mahdavi, M	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.contributor.author	Mekhilef, S	en_US
dc.date.available	2015-07-16	en_US
dc.date.issued	2016-01-05	en_US
dc.identifier.citation	IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2016, 2016-January	en_US
dc.identifier.isbn	9781479986415	en_US
dc.identifier.issn	2159-3442	en_US
dc.identifier.uri	http://hdl.handle.net/10453/42226
dc.description.abstract	© 2015 IEEE. This paper presents a high performance tracking method for maximum power generated by photovoltaic (PV) systems. Based on adaptive Neuro-Fuzzy inference systems (ANFIS), this method combines the learning abilities of artificial neural networks and the ability of fuzzy logic to handle imprecise data. It is able to handle non-linear and time varying problems hence making it suitable for accurate maximum power point tracking (MPPT) to ensure PV systems work effectively. The performance of the proposed method is compared to that of a fuzzy logic based MPPT algorithm to demonstrate its effectiveness.	en_US
dc.relation.ispartof	IEEE Region 10 Annual International Conference, Proceedings/TENCON	en_US
dc.relation.isbasedon	10.1109/TENCON.2015.7373030	en_US
dc.title	An adaptive Neuro-Fuzzy controller for maximum power point tracking of photovoltaic systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-January	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
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
pubs.volume	2016-January	en_US

Abstract:

© 2015 IEEE. This paper presents a high performance tracking method for maximum power generated by photovoltaic (PV) systems. Based on adaptive Neuro-Fuzzy inference systems (ANFIS), this method combines the learning abilities of artificial neural networks and the ability of fuzzy logic to handle imprecise data. It is able to handle non-linear and time varying problems hence making it suitable for accurate maximum power point tracking (MPPT) to ensure PV systems work effectively. The performance of the proposed method is compared to that of a fuzzy logic based MPPT algorithm to demonstrate its effectiveness.

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http://hdl.handle.net/10453/42226