Design and Optimization of Hybrid Excitation Synchronous Machine Based on Multi-objective Genetic Algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Z. | - |
dc.contributor.author | Zhao, W. | - |
dc.contributor.author | Liu, Y. | - |
dc.contributor.author | Wang, X. | - |
dc.contributor.author | Kwon, B.-I. | - |
dc.date.accessioned | 2021-07-28T08:13:07Z | - |
dc.date.available | 2021-07-28T08:13:07Z | - |
dc.date.created | 2021-07-14 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/105820 | - |
dc.description.abstract | To improve the electromagnetic performance of conventional hybrid excitation synchronous machine (HESM), a machine optimization method based on multi-objective genetic algorithm (MOGA) is proposed. The key idea is to improve the torque superposition by optimizing the shifting angle of permanent magnet (PM), thus to maximize the average output torque. The multi-objective global optimization method is used to comprehensively improve the torque and reduce the torque ripple and the amount of PMs. The effectiveness of the optimization scheme is verified by the finite element method (FEM), and the results show that the optimized model has higher average output torque and unit PM torque, as well as lower torque ripple when compared with the conventional model. © 2020 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Design and Optimization of Hybrid Excitation Synchronous Machine Based on Multi-objective Genetic Algorithm | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwon, B.-I. | - |
dc.identifier.doi | 10.1109/SCEMS48876.2020.9352267 | - |
dc.identifier.scopusid | 2-s2.0-85101981176 | - |
dc.identifier.wosid | 000680418500022 | - |
dc.identifier.bibliographicCitation | 2020 IEEE Student Conference on Electric Machines and Systems, SCEMS 2020, pp.124 - 129 | - |
dc.relation.isPartOf | 2020 IEEE Student Conference on Electric Machines and Systems, SCEMS 2020 | - |
dc.citation.title | 2020 IEEE Student Conference on Electric Machines and Systems, SCEMS 2020 | - |
dc.citation.startPage | 124 | - |
dc.citation.endPage | 129 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Electric excitation | - |
dc.subject.keywordPlus | Genetic algorithms | - |
dc.subject.keywordPlus | Global optimization | - |
dc.subject.keywordPlus | Multiobjective optimization | - |
dc.subject.keywordPlus | Permanent magnets | - |
dc.subject.keywordPlus | Torque | - |
dc.subject.keywordPlus | Turing machines | - |
dc.subject.keywordPlus | Conventional modeling | - |
dc.subject.keywordPlus | Design and optimization | - |
dc.subject.keywordPlus | Electromagnetic performance | - |
dc.subject.keywordPlus | Global optimization method | - |
dc.subject.keywordPlus | Hybrid excitation synchronous machine | - |
dc.subject.keywordPlus | Multi-objective genetic algorithm | - |
dc.subject.keywordPlus | Optimization method | - |
dc.subject.keywordPlus | Permanent magnets (pm) | - |
dc.subject.keywordPlus | Synchronous machinery | - |
dc.subject.keywordAuthor | finite element method | - |
dc.subject.keywordAuthor | hybrid excitation synchronous machine | - |
dc.subject.keywordAuthor | multi-objective genetic algorithm torque superposition | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9352267 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.