Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application

dc.authoridYavuzdeger, Abdurrahman/0000-0001-8058-4672
dc.authoridekinci, firat/0000-0002-4888-7881
dc.authoridESENBOGA, BURAK/0000-0002-7777-259X
dc.authoridTumay, Mehmet/0000-0002-6055-3761
dc.contributor.authorDemirdelen, Tugce
dc.contributor.authorEsenboga, Burak
dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorOzdogan, Alican
dc.contributor.authorYavuzdeger, Abdurrahman
dc.contributor.authorEkinci, Firat
dc.contributor.authorTumay, Mehmet
dc.date.accessioned2025-01-06T17:36:27Z
dc.date.available2025-01-06T17:36:27Z
dc.date.issued2022
dc.description.abstractIn recent years, the optimum and efficient design of the transformer core and conductive materials is the most significant issues to overcome the high-temperature problems. The temperature increases on the transformer materials are directly related to the energy efficiency of it. The overheating of the core and coils of the transformer reduces the amount of energy to be obtained from the transformer. However, copper, core, eddy current and other losses can be minimized by obtaining an optimum design of the transformer for maximum efficiency. Thus, the transformer life and the energy efficiency to be obtained from the transformer are maximized. The temperature rise and temperature distribution of the windings can be monitored by computer technology and the transformer can be safely overloaded and the production cost can be minimized. Also, the operating life of the transformers can be further increased by specifying hot spot temperatures on the transformer coils and core. In this study, 3 kVA and 5 kVA Dyn 11 connected 380/220-V dry-type transformers are optimized by multiobjective swarm intelligence-based optimization methods. The main contribution of this study is to prevent the overheating of the transformers by reducing the losses in the transformer core and coils and to reduce the costs of the transformer. The thermal and electromagnetic analyses of the transformers are realized by ANSYS/Maxwell software program which utilizes the industry-leading ANSYS/Fluent computational fluid dynamics and finite element method solvers. Finally, the experimental analyses are realized under the loaded conditions for the transformers. The experimental results are verified with the simulation results. The optimization, modeling, thermal/electromagnetic analysis and experimental processes are carried out step by step in this study. The transformer manufacturers will realize the optimum cost, efficiency and thermal analysis before transformers are manufactured.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [118E697]
dc.description.sponsorshipThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests. This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 118E697.
dc.identifier.doi10.1007/s00521-021-06447-z
dc.identifier.endpage1098
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85113885912
dc.identifier.scopusqualityQ1
dc.identifier.startpage1079
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06447-z
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1884
dc.identifier.volume34
dc.identifier.wosWOS:000691216400005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial intelligence
dc.subjectDry-type transformer
dc.subjectEnergy efficiency
dc.subjectMetaheuristic optimizations
dc.subjectSwarm intelligence
dc.titleModeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application
dc.typeArticle

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