{"title":"Multi-objective design optimization of Surface Mount Permanent Magnet machine with particle swarm intelligence","authors":"Y. Duan, R. Harley, T. Habetler","doi":"10.1109/SIS.2008.4668319","DOIUrl":null,"url":null,"abstract":"An efficient multi-objective design method with particle swarm optimization (PSO) is developed for surface mount permanent magnet machines to reduce the complexity in the PMSM machine design. First an analytical model of the PMSM machinepsilas geometry is developed and results are verified by finite element analysis. With proper design specification and assumption, the design input variables in this model can be reduced to as low as two, which significantly simplifies the optimization process. PSO is then applied to this analytical model. Compared to the traditional machine design methods, this proposed algorithm finds the optimized solution with fast computation and high convergence.","PeriodicalId":178251,"journal":{"name":"2008 IEEE Swarm Intelligence Symposium","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Swarm Intelligence Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIS.2008.4668319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
An efficient multi-objective design method with particle swarm optimization (PSO) is developed for surface mount permanent magnet machines to reduce the complexity in the PMSM machine design. First an analytical model of the PMSM machinepsilas geometry is developed and results are verified by finite element analysis. With proper design specification and assumption, the design input variables in this model can be reduced to as low as two, which significantly simplifies the optimization process. PSO is then applied to this analytical model. Compared to the traditional machine design methods, this proposed algorithm finds the optimized solution with fast computation and high convergence.