Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77028
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dc.contributorDepartment of Electrical Engineering-
dc.creatorZhang, Y-
dc.creatorHo, SL-
dc.creatorFu, W-
dc.date.accessioned2018-07-19T04:46:17Z-
dc.date.available2018-07-19T04:46:17Z-
dc.identifier.issn2158-3226-
dc.identifier.urihttp://hdl.handle.net/10397/77028-
dc.language.isoenen_US
dc.publisherAmerican Institute of Physicsen_US
dc.rights© 2017 Author(s).en_US
dc.rightsThis is an open access article under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Zhang, Y., Ho, S. L., & Fu, W. (2018). A dynamic multi-level optimal design method with embedded finite-element modeling for power transformers. AIP advances, 8(5), 056610 is available at https://doi.org/10.1063/1.5006736en_US
dc.titleA dynamic multi-level optimal design method with embedded finite-element modeling for power transformersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume8-
dc.identifier.issue5-
dc.identifier.doi10.1063/1.5006736-
dcterms.abstractThis paper proposes a dynamic multi-level optimal design method for power transformer design optimization (TDO) problems. A response surface generated by second-order polynomial regression analysis is updated dynamically by adding more design points, which are selected by Shifted Hammersley Method (SHM) and calculated by finite-element method (FEM). The updating stops when the accuracy requirement is satisfied, and optimized solutions of the preliminary design are derived simultaneously. The optimal design level is modulated through changing the level of error tolerance. Based on the response surface of the preliminary design, a refined optimal design is added using multi-objective genetic algorithm (MOGA). The effectiveness of the proposed optimal design method is validated through a classic three-phase power TDO problem.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAIP advances, 2018, v. 8, no. 5, 56610-
dcterms.isPartOfAIP advances-
dcterms.issued2018-
dc.identifier.scopus2-s2.0-85038445944-
dc.identifier.eissn2158-3226-
dc.identifier.artn56610-
dc.identifier.rosgroupid2017002982-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201807 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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