{"title":"风电系统LPV建模及基于LPV观测器的故障检测","authors":"Hui Shao, Zhiwei Gao, K. Busawon","doi":"10.1109/INDIN.2016.7819206","DOIUrl":null,"url":null,"abstract":"In this paper, linear parameter varying (LPV) modelling technique is addressed for modelling a wind turbine system, with real-time changing scheduling parameters. Based on the LPV wind turbine model, a LPV observer-based fault detection method is utilized to detect faults under four scenarios. The effectiveness of the proposed modelling and fault detection techniques is demonstrated by using the widely-recognized wind turbine benchmark system.","PeriodicalId":421680,"journal":{"name":"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"LPV modelling and LPV observer-based fault detection for wind turbine systems\",\"authors\":\"Hui Shao, Zhiwei Gao, K. Busawon\",\"doi\":\"10.1109/INDIN.2016.7819206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, linear parameter varying (LPV) modelling technique is addressed for modelling a wind turbine system, with real-time changing scheduling parameters. Based on the LPV wind turbine model, a LPV observer-based fault detection method is utilized to detect faults under four scenarios. The effectiveness of the proposed modelling and fault detection techniques is demonstrated by using the widely-recognized wind turbine benchmark system.\",\"PeriodicalId\":421680,\"journal\":{\"name\":\"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN.2016.7819206\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2016.7819206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LPV modelling and LPV observer-based fault detection for wind turbine systems
In this paper, linear parameter varying (LPV) modelling technique is addressed for modelling a wind turbine system, with real-time changing scheduling parameters. Based on the LPV wind turbine model, a LPV observer-based fault detection method is utilized to detect faults under four scenarios. The effectiveness of the proposed modelling and fault detection techniques is demonstrated by using the widely-recognized wind turbine benchmark system.