An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model

Ibrahim, IA; Hossain, MJ; Duck, BC; Fell, CJ

An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model

Ibrahim, IA Hossain, MJ Duck, BC Fell, CJ

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Sustainable Energy, 2020, 11, (2), pp. 1054-1066
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Ibrahim, IA
dc.contributor.author	Hossain, MJ
dc.contributor.author	Duck, BC
dc.contributor.author	Fell, CJ
dc.date.accessioned	2020-05-14T09:20:13Z
dc.date.available	2020-05-14T09:20:13Z
dc.date.issued	2020-04-01
dc.identifier.citation	IEEE Transactions on Sustainable Energy, 2020, 11, (2), pp. 1054-1066
dc.identifier.issn	1949-3029
dc.identifier.issn	1949-3037
dc.identifier.uri	http://hdl.handle.net/10453/140729
dc.description.abstract	© 2010-2012 IEEE. This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I-V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I-V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Sustainable Energy
dc.relation.isbasedon	10.1109/TSTE.2019.2917513
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering
dc.title	An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-14T09:20:09Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	11
utslib.start-page	1054
utslib.citation.issue	2

Abstract:

© 2010-2012 IEEE. This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I-V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I-V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.

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http://hdl.handle.net/10453/140729