{"title":"基于粒子群算法的感应电机故障识别","authors":"S.A. Etny, P.P. Acarlney, B. Zahawi, D. Giaouris","doi":"10.1109/PEDES.2006.344310","DOIUrl":null,"url":null,"abstract":"The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.","PeriodicalId":262597,"journal":{"name":"2006 International Conference on Power Electronic, Drives and Energy Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Induction Machine Fault Identification using Particle Swarm Algorithms\",\"authors\":\"S.A. Etny, P.P. Acarlney, B. Zahawi, D. Giaouris\",\"doi\":\"10.1109/PEDES.2006.344310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.\",\"PeriodicalId\":262597,\"journal\":{\"name\":\"2006 International Conference on Power Electronic, Drives and Energy Systems\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Conference on Power Electronic, Drives and Energy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PEDES.2006.344310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Conference on Power Electronic, Drives and Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PEDES.2006.344310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Induction Machine Fault Identification using Particle Swarm Algorithms
The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.