Sensitivity analysis for estimation of power losses in magnetic cores using neural network

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dc.contributor.buuauthorKüçük, İlker
dc.contributor.buuauthorDerebaşı, Naim
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.tr_TR
dc.contributor.orcid0000-0003-2546-0022tr_TR
dc.contributor.researcheridAAI-2254-2021tr_TR
dc.contributor.scopusid6602910810tr_TR
dc.contributor.scopusid11540936300tr_TR
dc.date.accessioned2021-12-27T06:21:53Z
dc.date.available2021-12-27T06:21:53Z
dc.date.issued2006-12
dc.description.abstractExperimental data from a sample of 42 cores made from grain oriented 0.27 mm thick 3 % SiFe electrical steel with dimensions ranging from 35 to 160 mm outer diameter. 25-100 mm inner diameter and 10-70 mm strip width and a flux density range 0.2-1.7T have been obtained at 500 Hz and used as training data to a feed forward neural network. An analytical equation for prediction of power loss as depends on input parameters from the results of sensitivity analysis has been obtained. The calculated power losses with the analytical expression have also been compared with power loss obtained from the Preisach model after it has been applied to toroidal cores. The results show the proposed model can be used for estimation of power losses in the toroidal cores.en_US
dc.identifier.citationKüçük, İ. ve Derebaşı, N. (2006). ''Sensitivity analysis for estimation of power losses in magnetic cores using neural network''. Journal of Physics and Chemistry of Solids, 67(12), 2473-2477.en_US
dc.identifier.endpage2477tr_TR
dc.identifier.issn0022-3697
dc.identifier.issn1879-2553
dc.identifier.issue12tr_TR
dc.identifier.scopus2-s2.0-33750444635tr_TR
dc.identifier.startpage2473tr_TR
dc.identifier.urihttps://doi.org/10.1016/j.jpcs.2006.07.001
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0022369706003623
dc.identifier.urihttp://hdl.handle.net/11452/23635
dc.identifier.volume67tr_TR
dc.identifier.wos000242958500007tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevieren_US
dc.relation.journalJournal of Physics and Chemistry of Solidsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChemistryen_US
dc.subjectPhysicsen_US
dc.subjectMagnetic materialsen_US
dc.subjectMagnetic propertiesen_US
dc.subjectSensitivity analysisen_US
dc.subjectNeural networksen_US
dc.subjectMagnetic propertiesen_US
dc.subjectMagnetic materialsen_US
dc.subjectMagnetic fluxen_US
dc.subjectEnergy dissipationtr_TR
dc.subjectDensity (specific gravity)en_US
dc.subjectData processingen_US
dc.subjectToroidal coresen_US
dc.subjectPreisach modelen_US
dc.subjectPower lossesen_US
dc.subjectElectrical steelen_US
dc.subjectMagnetic coresen_US
dc.subjectToolen_US
dc.subjectPerformanceen_US
dc.subject.scopusSilicon Steel; Soft Magnetic Materials; Induction Motorsen_US
dc.subject.wosChemistry, multidisciplinaryen_US
dc.subject.wosPhysics, condensed matteren_US
dc.titleSensitivity analysis for estimation of power losses in magnetic cores using neural networken_US
dc.typeArticle
dc.wos.quartileQ2 (Chemistry, multidisciplinary)en_US
dc.wos.quartileQ3 (Physics, condensed matter)en_US

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