资源描述：
《基于自适应遗传蚁群算法的永磁自启动同步电机的优化》由会员上传分享，免费在线阅读，更多相关内容在学术论文-天天文库。
1、-------哈尔滨理工大学工学硕士学位论文OptimizationofLine-startPermanentMagnet
SynchronousMotorBasedonAdaptive
GeneticAntColonyAlgorithmAbstractLine-startpermanentmagnetsynchronousmotoriswidelyusedinoilandtextileindustrybyitshighefficiencyandpowerfactor,whichmustbetakenintoconsiderationwhenwedesi
2、gnthistypeofmotor.RotorSquirrel-cagebarsareinstalledinthemotor,comparedwiththeordinarypermanentmagnetmotor,itsprocessofstartandpullintosynchronizationismorecomplex,whichmakethedesignmoredifficult.Sothenecessarymethodofmotoroptimizationdesignshouldbeadaptedtooptimizethemotorinorde
3、rtomeettherequirementsofoptimizedresult.Firstofall,theorigin,thedevelopingcourse,relatedproblemsandbasicprincipleofthetraditionalantcolonyalgorithmareintroducedinthispaper,whichproposesanimprovedadaptivegeneticantcolonyalgorithmandidentifiestheconditionofcombininggeneticalgorithm
4、withantcolonyalgorithm,makingalgorithmoptimizationprocesshavetheadvantagesofbothalgorithms,andgivesanimprovedpheromoneupdatingstrategy,andsetsuptheefficientalgorithmparameters.Traditionalantcolonyalgorithmandimprovedadaptivegeneticantcolonyalgorithmarerespectivelyusedtocalculatem
5、inimumoptimizationoftheknownfunction，theresultofcomparingtheperformancesofevolutioncurvesandthemeanchangeovertimecurvesdemonstratethattheimprovedadaptivegeneticantcolonyalgorithmhasbetterperformances.Additionally,a4-pole7.5kWline-startpermanentmagnetsynchronousmotorare
usedastheo
6、bjectofthestudy,inordertoimprovetheefficiencyofthemotor,the
optimizationdesignmathematicalmodelofthemotorareestablished,whichsetup
theobjectivefunctionandchoosethestructuralparameterswhichhavesignificant
effectontheobjectivefunctionastheoptimizationvariables.Inaddition,constraint
7、s
andthecorrespondingpenaltyfunctionaresetuptomeettherequirementsofthe
motordesign.Finally,theimprovedadaptivegeneticantcolonyalgorithmisappliedtooptimize-II------------哈尔滨理工大学工学硕士学位论文themotorandgetanewsetofelectromagneticdesign.Comparedwiththeoriginaldesign,confirmsthattheimprov
8、edalgorithmissuccessfultooptimizethemoto
