Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

Ibrahim, IA; Hossain, MJ; Duck, BC; Badar, AQH

Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

Ibrahim, IA Hossain, MJ Duck, BC Badar, AQH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019, 2020, 00, pp. 1-6
Issue Date:: 2020

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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	Badar, AQH
dc.date	2019-12-10
dc.date.accessioned	2020-11-03T02:27:03Z
dc.date.available	2020-11-03T02:27:03Z
dc.date.issued	2020
dc.identifier.citation	2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019, 2020, 00, pp. 1-6
dc.identifier.isbn	9781728131924
dc.identifier.uri	http://hdl.handle.net/10453/143724
dc.description.abstract	© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 20th International Conference on Intelligent System Application to Power Systems, ISAP 2019
dc.relation.ispartof	International Conference on Intelligent System Application to Power Systems
dc.relation.isbasedon	10.1109/ISAP48318.2019.9065989
dc.rights	info:eu-repo/semantics/closedAccess
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.title	Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	New Delhi, India
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	*
pubs.consider-herdc	false
dc.date.updated	2020-11-03T02:26:54Z
pubs.finish-date	2019-12-14
pubs.place-of-publication	Piscatway, USA
pubs.publication-status	Published
pubs.start-date	2019-12-10
pubs.volume	00
dc.location	Piscatway, USA

Abstract:

© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model.

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http://hdl.handle.net/10453/143724