Yulan Li, Zhenyu Huang, N. Zhou, Barry Lee, R. Diao, P. Du
{"title":"集成卡尔曼滤波在电力系统状态跟踪和灵敏度分析中的应用","authors":"Yulan Li, Zhenyu Huang, N. Zhou, Barry Lee, R. Diao, P. Du","doi":"10.1109/TDC.2012.6281499","DOIUrl":null,"url":null,"abstract":"An ensemble Kalman filter (EnKF) method is proposed to track dynamic states of generators. The algorithm of the EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF method can still effectively track generator dynamic states.","PeriodicalId":19873,"journal":{"name":"PES T&D 2012","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Application of ensemble Kalman filter in power system state tracking and sensitivity analysis\",\"authors\":\"Yulan Li, Zhenyu Huang, N. Zhou, Barry Lee, R. Diao, P. Du\",\"doi\":\"10.1109/TDC.2012.6281499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An ensemble Kalman filter (EnKF) method is proposed to track dynamic states of generators. The algorithm of the EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF method can still effectively track generator dynamic states.\",\"PeriodicalId\":19873,\"journal\":{\"name\":\"PES T&D 2012\",\"volume\":\"8 1\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PES T&D 2012\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TDC.2012.6281499\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PES T&D 2012","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TDC.2012.6281499","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of ensemble Kalman filter in power system state tracking and sensitivity analysis
An ensemble Kalman filter (EnKF) method is proposed to track dynamic states of generators. The algorithm of the EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF method can still effectively track generator dynamic states.