Squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice

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dc.authorscopusid36976188000
dc.authorscopusid55354654200
dc.authorscopusid43361385400
dc.authorscopusid57216326306
dc.contributor.authorMaden, Dinçer
dc.contributor.authorÇelik, Emre
dc.contributor.authorHoussein, Essam H.
dc.contributor.authorSharma, Gulshan
dc.date.accessioned2023-07-26T11:54:40Z
dc.date.available2023-07-26T11:54:40Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractModel parameters estimation of solar photovoltaic (PV) cells/modules using real current–voltage (I–V) data is a critical task for the performance of PV systems. Therefore, there is a necessity to procure optimal parameters of PV models using proper optimization techniques. For this aim, squirrel search algorithm (SSA) as the recent and powerful tool is employed to accomplish the mentioned task in the single-diode model (SDM) and double-diode model (DDM) of a PV unit. Of course, better parameter values can be obtained by reducing the error between the experimental and model-based estimated data. Analyses are performed under two case studies. The former considers a standard dataset of R.T.C. France silicon solar cell, whereas the latter uses an experimental dataset of a polycrystalline CS6P-220P solar module. The I-V data of this PV module were acquired when it worked under 30 °C and solar radiance of 1000W/m2 at the Engineering Faculty Campus of Düzce University, Turkey. The results of the first case study are compared with those of other prevalent approaches, which demonstrate the superiority of SSA over its competing peers. Moreover, SSA is found to handle the model parameters definition of an industrial PV module located at the university campus. Thus, the new method offers a practical tool beneficial to boost the effectiveness of PV systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.identifier.doi10.1007/s00521-023-08451-x
dc.identifier.issn0941-0643
dc.identifier.scopus2-s2.0-85150192364en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08451-x
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12893
dc.identifier.wosWOS:000951853900011en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇelik, Emre
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectDouble-diode modelen_US
dc.subjectI–V characteristicen_US
dc.subjectParameter estimationen_US
dc.subjectSingle-diode modelen_US
dc.subjectSolar PV uniten_US
dc.subjectSquirrel search algorithmen_US
dc.subjectDiodesen_US
dc.subjectLearning algorithmsen_US
dc.subjectSilicon solar cellsen_US
dc.subjectSolar panelsen_US
dc.subjectSolar power generationen_US
dc.subjectSpace division multiple accessen_US
dc.subjectDiode modelingen_US
dc.subjectDouble-diode modelen_US
dc.subjectI–V characteristicen_US
dc.subjectParameters estimationen_US
dc.subjectPhotovoltaic modelen_US
dc.subjectSearch Algorithmsen_US
dc.subjectSingle-diode modelsen_US
dc.subjectSolar photovoltaic unitsen_US
dc.subjectSolar photovoltaicsen_US
dc.subjectSquirrel search algorithmen_US
dc.subjectParameter estimationen_US
dc.titleSquirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practiceen_US
dc.typeArticleen_US

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