Multi-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modules

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dc.contributor.authorEkinci, Serdar
dc.contributor.authorRizk-Allah, R. M.
dc.contributor.authorİzci, Davut
dc.contributor.authorÇelik, Emre
dc.date.accessioned2025-10-11T20:45:20Z
dc.date.available2025-10-11T20:45:20Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractHarnessing the potential of solar photovoltaic (PV) technology relies heavily on accurately estimating the model parameters of PV cells/modules using real current-voltage (I-V) data. Achieving optimal parameter values is essential for the performance and efficiency of PV systems, necessitating the use of advanced optimization techniques. In our endeavor, we introduce a multi-strategy improvement approach for the Runge Kutta (RUN) optimizer, a cutting-edge tool used for tackling this critical task in both single-diode and double-diode PV unit models. By aligning experimental and model-based estimated data, our approach seeks to reduce errors and improve the accuracy of PV system performance. We conduct meticulous analyses of two compelling case studies and the CEC 2020 test suite to showcase the versatility and effectiveness of our improved RUN (IRUN) algorithm. The first case study involves a standard dataset derived from the well-known R.T.C. France silicon solar cell, where IRUN performs favorably compared to competing methods, demonstrating its effectiveness. IRUN effectively manages the complex task of defining model parameters for an industrial PV module situated at the Engineering Faculty of Düzce University in Turkey. The real-world I-V data, obtained under optimal conditions with a temperature of 30°C and solar radiance of 1000W/m2, provide strong evidence of the practical applicability and real-world benefits of our innovative method. Additional analyses through three-diode and PV module models further confirm the efficacy of the IRUN. A mean absolute error of down to 6.5E-04 and root mean square error of down to 7.3668E-04 are achieved. Our approach provides a practical and efficient tool for improving the accuracy of PV systems, enhancing their performance when compared to existing methods. © 2024 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1016/j.heliyon.2024.e39301
dc.identifier.issn2405-8440
dc.identifier.issue20en_US
dc.identifier.scopus2-s2.0-85206270049en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.heliyon.2024.e39301
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21298
dc.identifier.volume10en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofHeliyonen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectI-v Characteristicen_US
dc.subjectImproved Runge Kutta (run) Optimizeren_US
dc.subjectParameter Estimationen_US
dc.subjectSingle And Double Diode Modelsen_US
dc.subjectSolar Photovoltaic (pv) Uniten_US
dc.titleMulti-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modulesen_US
dc.typeArticleen_US

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