{"title":"Using an on-line BSE technique for wide-area oscillations monitoring","authors":"J. J. Ayon, S. Narasimhan","doi":"10.1109/PESGM.2015.7286149","DOIUrl":null,"url":null,"abstract":"The dynamic behavior of large interconnected power systems can be provided by wide-area measurement systems. Motivated by this, the use of on-line techniques has been developed, as well as multivariate methods have been proposed to extract useful patterns from a large data set. In this paper, a multivariable technique to identify and extract dynamic patterns from simultaneously measured data is proposed. One dynamic pattern or a limited set of dynamic patterns can be sequentially extracted. The technique combines blind source extraction with a recursive least squares adaptive filter, which exploits the temporal structure of the measured signals. In order to show the applicability of the proposed methodology, a numerical simulation of a four-machine, two-area test system is carried out. Results indicate that the method has the ability to estimate modal responses and mode shapes.","PeriodicalId":423639,"journal":{"name":"2015 IEEE Power & Energy Society General Meeting","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Power & Energy Society General Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PESGM.2015.7286149","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
The dynamic behavior of large interconnected power systems can be provided by wide-area measurement systems. Motivated by this, the use of on-line techniques has been developed, as well as multivariate methods have been proposed to extract useful patterns from a large data set. In this paper, a multivariable technique to identify and extract dynamic patterns from simultaneously measured data is proposed. One dynamic pattern or a limited set of dynamic patterns can be sequentially extracted. The technique combines blind source extraction with a recursive least squares adaptive filter, which exploits the temporal structure of the measured signals. In order to show the applicability of the proposed methodology, a numerical simulation of a four-machine, two-area test system is carried out. Results indicate that the method has the ability to estimate modal responses and mode shapes.