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Transfer Learning-Based Design Method for Cogging Torque Reduction in PMSM with Step-Skew Considering 3-D Leakage Flux

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dc.contributor.authorWon, Yun-Jae-
dc.contributor.authorKim, Jae-Hyun-
dc.contributor.authorPark, Soo-Hwan-
dc.contributor.authorLee, Ji-Hyeon-
dc.contributor.authorAn, Soo-Min-
dc.contributor.authorKim, Doo-Young-
dc.contributor.authorLim, Myung Seop-
dc.date.accessioned2023-09-11T01:52:21Z-
dc.date.available2023-09-11T01:52:21Z-
dc.date.created2023-07-20-
dc.date.issued2023-05-
dc.identifier.issn0018-9464-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190379-
dc.description.abstractStep-skew is a common technique for eliminating the cogging torque of a target harmonic order in permanent magnet synchronous motors (PMSMs). However, when step-skew is applied to the rotor, the cogging torque of the target harmonic order is not completely eliminated due to 3-D leakage flux. Therefore, the 3-D leakage flux should be considered in designing a PMSM with step-skew for cogging torque reduction. The most accurate way to consider the 3-D leakage flux is to perform 3-D finite element analysis (FEA), but it has the disadvantage of high computation time. To resolve this challenge, this paper proposes a design method that utilizes transfer learning to reduce the time for 3-D FEA while maintaining accuracy. Through the proposed method, a large amount of 2-D FEA-based data and a small amount of 3-D FEA-based data are used instead of a large amount of 3-D FEA-based data, with similar accuracy as using a large amount of 3-D FEA-based data, and the computational time is highly reduced. Finally, a prototype is fabricated and tested to verify the validity of the proposed design method for cogging torque reduction. IEEE-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE-INSTITUTE OF ELECTRICAL and ELECTRONICS ENGINEERS-
dc.titleTransfer Learning-Based Design Method for Cogging Torque Reduction in PMSM with Step-Skew Considering 3-D Leakage Flux-
dc.typeArticle-
dc.contributor.affiliatedAuthorLim, Myung Seop-
dc.identifier.doi10.1109/TMAG.2023.3294601-
dc.identifier.scopusid2-s2.0-85165277209-
dc.identifier.bibliographicCitationIEEE International Conference on Magnetics (INTERMAG), pp.1 - 5-
dc.relation.isPartOfIEEE International Conference on Magnetics (INTERMAG)-
dc.citation.titleIEEE International Conference on Magnetics (INTERMAG)-
dc.citation.startPage1-
dc.citation.endPage5-
dc.type.rimsART-
dc.type.docTypeProceeding-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlus3-D leakage flux-
dc.subject.keywordPlusCogging torque-
dc.subject.keywordPlusDeep neural network-
dc.subject.keywordPlusDesign Methodology-
dc.subject.keywordPlusForging-
dc.subject.keywordPlusLeakage flux-
dc.subject.keywordPlusPermanent magnet synchronoi motor-
dc.subject.keywordPlusPermanent Magnet Synchronous Motor-
dc.subject.keywordPlusStep skews-
dc.subject.keywordPlusTransfer learning-
dc.subject.keywordAuthor3-D leakage flux-
dc.subject.keywordAuthorCogging torque-
dc.subject.keywordAuthordeep neural network (DNN)-
dc.subject.keywordAuthorDesign methodology-
dc.subject.keywordAuthorForging-
dc.subject.keywordAuthorGeometry-
dc.subject.keywordAuthorHarmonic analysis-
dc.subject.keywordAuthorpermanent magnet synchronous motors (PMSMs)-
dc.subject.keywordAuthorRotors-
dc.subject.keywordAuthorstep-skew-
dc.subject.keywordAuthorTorque-
dc.subject.keywordAuthortransfer learning-
dc.subject.keywordAuthorTransfer learning-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/10184449-
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