Publication:
Adaptive electromagnetic field optimization algorithm for the solar cell parameter identification problem

dc.contributor.authorKüçükoğlu, İlker
dc.contributor.buuauthorKÜÇÜKOĞLU, İLKER
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentEndüstri Mühendisliği Bölümü
dc.contributor.orcid0000-0002-5075-0876
dc.contributor.researcheridD-8543-2015
dc.date.accessioned2024-07-12T06:09:57Z
dc.date.available2024-07-12T06:09:57Z
dc.date.issued2019-04-28
dc.description.abstractSolar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I-V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.
dc.identifier.doi10.1155/2019/4692108
dc.identifier.issn1110-662X
dc.identifier.urihttps://doi.org/10.1155/2019/4692108
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1155/2019/4692108
dc.identifier.urihttps://hdl.handle.net/11452/43217
dc.identifier.volume2019
dc.identifier.wos000471923400001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherHindawi
dc.relation.journalInternational Journal of Photoenergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectChemistry
dc.subjectEnergy & fuels
dc.subjectOptics
dc.subjectPhysics
dc.titleAdaptive electromagnetic field optimization algorithm for the solar cell parameter identification problem
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Endüstri Mühendisliği Bölümü
relation.isAuthorOfPublication3715d274-af41-48cd-a5d7-8b2b7cd50a1a
relation.isAuthorOfPublication.latestForDiscovery3715d274-af41-48cd-a5d7-8b2b7cd50a1a

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