D. Correa, S. I. Nabeta, F. Pereira, J. A. da Silva
{"title":"A new methodology for vibration reduction of a 2 phase SRM based on FEM coupled simulations and genetic algorithm model","authors":"D. Correa, S. I. Nabeta, F. Pereira, J. A. da Silva","doi":"10.1109/PEDS.2017.8289234","DOIUrl":null,"url":null,"abstract":"This work proposes a new methodology for vibration reduction of a 2-Phase SRM based on the power electronic drive and motor modelling, associated to a genetic algorithm optimization model which uses the vibration data to adjust some drive control parameters. To implement the power drive computational model, it was used coupled FEM and Multi-Physics simulations resources. The optimization procedure is based on a bootstrapping neural network interpolation approach and the genetic algorithm method and it was used to obtain reliable results with a small subset of the vibration data which allows us to reduce the number of experiments. Firstly, it was presented the power drive computational model development and its validation through comparison with the experimental results. Afterwards, it was presented the details of the optimization procedure.","PeriodicalId":411916,"journal":{"name":"2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PEDS.2017.8289234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This work proposes a new methodology for vibration reduction of a 2-Phase SRM based on the power electronic drive and motor modelling, associated to a genetic algorithm optimization model which uses the vibration data to adjust some drive control parameters. To implement the power drive computational model, it was used coupled FEM and Multi-Physics simulations resources. The optimization procedure is based on a bootstrapping neural network interpolation approach and the genetic algorithm method and it was used to obtain reliable results with a small subset of the vibration data which allows us to reduce the number of experiments. Firstly, it was presented the power drive computational model development and its validation through comparison with the experimental results. Afterwards, it was presented the details of the optimization procedure.