{"title":"多目标粒子群算法在无线电力传输系统设计中的应用","authors":"N. Hasan, Tuba Yilmaz, R. Zane, Zeljko Pantic","doi":"10.1109/WPT.2015.7139138","DOIUrl":null,"url":null,"abstract":"This paper proposes a stochastic method - particle swarm optimization (PSO) and Pareto front technique, to conduct a multivariable optimization and design an inductively coupled power transfer system. Previously, design methods have been proposed which require designer experience; they are not only computationally challenging, but also frequently result in suboptimum solutions. The algorithm proposed in this paper models all losses and inductance matrix analytically. We study four common compensation structures, and select a series-parallel topology to design a 200 W experimental prototype optimized with respect to transfer efficiency and VA rating. Experiments and numerical simulations are employed to verify the optimization algorithm.","PeriodicalId":194427,"journal":{"name":"2015 IEEE Wireless Power Transfer Conference (WPTC)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Multi-objective particle swarm optimization applied to the design of Wireless Power Transfer systems\",\"authors\":\"N. Hasan, Tuba Yilmaz, R. Zane, Zeljko Pantic\",\"doi\":\"10.1109/WPT.2015.7139138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a stochastic method - particle swarm optimization (PSO) and Pareto front technique, to conduct a multivariable optimization and design an inductively coupled power transfer system. Previously, design methods have been proposed which require designer experience; they are not only computationally challenging, but also frequently result in suboptimum solutions. The algorithm proposed in this paper models all losses and inductance matrix analytically. We study four common compensation structures, and select a series-parallel topology to design a 200 W experimental prototype optimized with respect to transfer efficiency and VA rating. Experiments and numerical simulations are employed to verify the optimization algorithm.\",\"PeriodicalId\":194427,\"journal\":{\"name\":\"2015 IEEE Wireless Power Transfer Conference (WPTC)\",\"volume\":\"170 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Wireless Power Transfer Conference (WPTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WPT.2015.7139138\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Wireless Power Transfer Conference (WPTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WPT.2015.7139138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-objective particle swarm optimization applied to the design of Wireless Power Transfer systems
This paper proposes a stochastic method - particle swarm optimization (PSO) and Pareto front technique, to conduct a multivariable optimization and design an inductively coupled power transfer system. Previously, design methods have been proposed which require designer experience; they are not only computationally challenging, but also frequently result in suboptimum solutions. The algorithm proposed in this paper models all losses and inductance matrix analytically. We study four common compensation structures, and select a series-parallel topology to design a 200 W experimental prototype optimized with respect to transfer efficiency and VA rating. Experiments and numerical simulations are employed to verify the optimization algorithm.