{"title":"A Review of The Research on Kalman Filtering in Power System Dynamic State Estimation","authors":"Zili Yang, Ran Gao, Weihua He","doi":"10.1109/IMCEC51613.2021.9482112","DOIUrl":null,"url":null,"abstract":"Firstly, the problems and common methods of power system dynamic state estimation are briefly introduced, and then the basic principles of Kalman filtering are introduced. Based on this principle, the common methods of power system dynamic state estimation problems are derived: extended Kalman filter, unscented Kalman filter and cubature Kalman filter, which are introduced respectively. The advantages and disadvantages of these methods and their applications in power system dynamic state estimation are compared and analyzed. Finally, the development trend of Kalman filter in dynamic state estimation is prospected.","PeriodicalId":240400,"journal":{"name":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMCEC51613.2021.9482112","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
Firstly, the problems and common methods of power system dynamic state estimation are briefly introduced, and then the basic principles of Kalman filtering are introduced. Based on this principle, the common methods of power system dynamic state estimation problems are derived: extended Kalman filter, unscented Kalman filter and cubature Kalman filter, which are introduced respectively. The advantages and disadvantages of these methods and their applications in power system dynamic state estimation are compared and analyzed. Finally, the development trend of Kalman filter in dynamic state estimation is prospected.
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