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Coyote optimization algorithm for the parameter extraction of photovoltaic cells

In this paper, a new and powerful metaheuristic optimization technique known as the Coyote Optimization Algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms ado...

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Main Authors:	Chin, Vun Jack, Salam, Zainal
Format:	Article
Published:	Elsevier Ltd 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/89488/ http://dx.doi.org/10.1016/j.solener.2019.10.093
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record_format	eprints
spelling	my.utm.894882021-02-22T01:47:45Z http://eprints.utm.my/id/eprint/89488/ Coyote optimization algorithm for the parameter extraction of photovoltaic cells Chin, Vun Jack Salam, Zainal TK Electrical engineering. Electronics Nuclear engineering In this paper, a new and powerful metaheuristic optimization technique known as the Coyote Optimization Algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 × 10−5 for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module. Elsevier Ltd 2019-12 Article PeerReviewed Chin, Vun Jack and Salam, Zainal (2019) Coyote optimization algorithm for the parameter extraction of photovoltaic cells. Solar Energy, 194 . pp. 656-670. ISSN 0038-092X http://dx.doi.org/10.1016/j.solener.2019.10.093
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Chin, Vun Jack Salam, Zainal Coyote optimization algorithm for the parameter extraction of photovoltaic cells
description	In this paper, a new and powerful metaheuristic optimization technique known as the Coyote Optimization Algorithm (COA) is proposed for the parameter extraction of the PV cell/module. It is utilized to identify the parameters of the single diode and two-diode models. Inspired by the social norms adopted by the coyotes to ensure the survivability of their species, the COA possesses several outstanding merits such as low number of control parameters, ease of implementation and diverse mechanisms for balancing exploration and exploitation. For physically meaningful solutions, a set of parametric constraints is introduced to prevent the coyotes from straying outside of the predefined boundaries of the search space. Extensive tests indicate that the proposed optimizer exhibits superior accuracy compared to other state-of-the-art EA-based parameter extraction methods. It achieved root-mean-square error (RSME) as low as 7.7301E-04 A and 7.3265E-04 A, for the single-diode and two-diode models, respectively. Moreover, the algorithm maintains outstanding performance when tested on an assortment of modules of different technologies (i.e. mono-crystalline, poly-crystalline, and thin film) at varying irradiance and temperature. The standard deviations (STDs) of the fitness values over 35 runs are measured to be less than 1 × 10−5 for both models. This suggests that the results produced by the algorithm are highly consistent. With these outstanding merits, the COA is envisaged to be a competitive option for the parameter extraction problem of PV cell/module.
format	Article
author	Chin, Vun Jack Salam, Zainal
author_facet	Chin, Vun Jack Salam, Zainal
author_sort	Chin, Vun Jack
title	Coyote optimization algorithm for the parameter extraction of photovoltaic cells
title_short	Coyote optimization algorithm for the parameter extraction of photovoltaic cells
title_full	Coyote optimization algorithm for the parameter extraction of photovoltaic cells
title_fullStr	Coyote optimization algorithm for the parameter extraction of photovoltaic cells
title_full_unstemmed	Coyote optimization algorithm for the parameter extraction of photovoltaic cells
title_sort	coyote optimization algorithm for the parameter extraction of photovoltaic cells
publisher	Elsevier Ltd
publishDate	2019
url	http://eprints.utm.my/id/eprint/89488/ http://dx.doi.org/10.1016/j.solener.2019.10.093
_version_	1692991789459308544
score	13.211869

Coyote optimization algorithm for the parameter extraction of photovoltaic cells

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