Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules

Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed a...

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Autores principales:	Waleed Abd El Maguid Ahmed, Hala M. Abdel Mageed, Samah AbdEltwab Mohamed, Amr A. Saleh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Photovoltaic Particle swarm optimization Fractional order Darwinian Modeling Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/14d1c8695f6148039ed2cb756f1cafaa
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spelling	oai:doaj.org-article:14d1c8695f6148039ed2cb756f1cafaa2021-11-18T04:45:09ZFractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules1110-016810.1016/j.aej.2021.06.019https://doaj.org/article/14d1c8695f6148039ed2cb756f1cafaa2022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S111001682100377Xhttps://doaj.org/toc/1110-0168Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed as well. The proposed FODPSO together with the PSO has been applied in both the single and double diode models to describe the specified PV units for purpose of comparison. To examine the advantages of the proposed FODPSO over conventional PSO, two approaches have been performed; the first approach is implementing the FODPSO and PSO algorithms using input data obtained from previous international publications for different types of PV cells/modules, then comparing the FODPSO algorithm's results to the results of PSO and the other previous optimization techniques that were using the same input data and identical PV cells/modules. The second validation approach is achieved by performing real measurements in outdoor conditions for another two types of PV modules (mono and poly crystalline Silicon) and inserting these measurements' readings as input data to the PSO and FODPSO algorithms. A comparative study has also been done for both algorithms' results. Enhanced results have been achieved using the FODPSO technique.It is worth mentioning that during the development of this work, other efforts have been made with different techniques that may result in similar or even better results, yet this paper is still introducing for the first time the FODPSO technique (as a modified technique of the PSO) as applied on the PV modeling problem, in addition the paper presents the benefits of the proposed FODPSO over the conventional PSO and presents a comparison that shows the superiority of the FODPSO over the conventional PSO.Waleed Abd El Maguid AhmedHala M. Abdel MageedSamah AbdEltwab MohamedAmr A. SalehElsevierarticlePhotovoltaicParticle swarm optimizationFractional order DarwinianModelingEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1249-1263 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photovoltaic Particle swarm optimization Fractional order Darwinian Modeling Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Photovoltaic Particle swarm optimization Fractional order Darwinian Modeling Engineering (General). Civil engineering (General) TA1-2040 Waleed Abd El Maguid Ahmed Hala M. Abdel Mageed Samah AbdEltwab Mohamed Amr A. Saleh Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
description	Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed as well. The proposed FODPSO together with the PSO has been applied in both the single and double diode models to describe the specified PV units for purpose of comparison. To examine the advantages of the proposed FODPSO over conventional PSO, two approaches have been performed; the first approach is implementing the FODPSO and PSO algorithms using input data obtained from previous international publications for different types of PV cells/modules, then comparing the FODPSO algorithm's results to the results of PSO and the other previous optimization techniques that were using the same input data and identical PV cells/modules. The second validation approach is achieved by performing real measurements in outdoor conditions for another two types of PV modules (mono and poly crystalline Silicon) and inserting these measurements' readings as input data to the PSO and FODPSO algorithms. A comparative study has also been done for both algorithms' results. Enhanced results have been achieved using the FODPSO technique.It is worth mentioning that during the development of this work, other efforts have been made with different techniques that may result in similar or even better results, yet this paper is still introducing for the first time the FODPSO technique (as a modified technique of the PSO) as applied on the PV modeling problem, in addition the paper presents the benefits of the proposed FODPSO over the conventional PSO and presents a comparison that shows the superiority of the FODPSO over the conventional PSO.
format	article
author	Waleed Abd El Maguid Ahmed Hala M. Abdel Mageed Samah AbdEltwab Mohamed Amr A. Saleh
author_facet	Waleed Abd El Maguid Ahmed Hala M. Abdel Mageed Samah AbdEltwab Mohamed Amr A. Saleh
author_sort	Waleed Abd El Maguid Ahmed
title	Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
title_short	Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
title_full	Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
title_fullStr	Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
title_full_unstemmed	Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules
title_sort	fractional order darwinian particle swarm optimization for parameters identification of solar pv cells and modules
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/14d1c8695f6148039ed2cb756f1cafaa
work_keys_str_mv	AT waleedabdelmaguidahmed fractionalorderdarwinianparticleswarmoptimizationforparametersidentificationofsolarpvcellsandmodules AT halamabdelmageed fractionalorderdarwinianparticleswarmoptimizationforparametersidentificationofsolarpvcellsandmodules AT samahabdeltwabmohamed fractionalorderdarwinianparticleswarmoptimizationforparametersidentificationofsolarpvcellsandmodules AT amrasaleh fractionalorderdarwinianparticleswarmoptimizationforparametersidentificationofsolarpvcellsandmodules
_version_	1718425022479990784

Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules

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