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PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm

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dc.contributor.authorElshara, R.
dc.contributor.authorHançerlioğullari, A.
dc.contributor.authorRahebi, J.
dc.contributor.authorLopez-Guede, J.M.
dc.date.accessioned2024-04-22T09:02:21Z
dc.date.available2024-04-22T09:02:21Z
dc.date.issued2024
dc.description.abstractIn recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field.
dc.identifier10.3390/en17071716
dc.identifier.doi10.3390/en17071716
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85190299743
dc.identifier.urihttps://hdl.handle.net/20.500.12597/33098
dc.identifier.volume17
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofEnergies
dc.relation.ispartofseriesEnergies
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectChaos theory, coati optimization algorithm (COA), opposition-based learning, optimization of PV parameters, solar systems
dc.titlePV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm
dc.typearticle
dspace.entity.typeScopus
oaire.citation.issue7
oaire.citation.volume17
person.affiliation.nameUniversity of Kastamonua
person.affiliation.nameKastamonu University
person.affiliation.nameIstanbul Topkapi University
person.affiliation.nameUniversidad del Pais Vasco
person.identifier.orcid0000-0003-4078-3735
person.identifier.orcid0000-0001-9875-4860
person.identifier.orcid0000-0002-5310-1601
person.identifier.scopus-author-id58983365200
person.identifier.scopus-author-id18133515900
person.identifier.scopus-author-id36451137000
person.identifier.scopus-author-id34880300000
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