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  • 标题:Structure in total least squares parameter estimation for electrical networks
  • 本地全文:下载
  • 作者:Adair Lang ; Iman Shames ; Michael Cantoni
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:23
  • 页码:420-425
  • DOI:10.1016/j.ifacol.2018.12.072
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractA total least squares approach to estimating the line admittances of an electrical network for power distribution is explored. It is observed that the Newton iterates for solving the corresponding non-linear Karush-Kuhn-Tucker (KKT) optimality conditions are structured for path and tree networks. In particular, it is revealed that a condensed form of the linear system of equations to be solved at each Newton step has a sparse block arrow-head structure, which can be exploited to improve the scalability of the approach. A numerical example for a path network is presented.
  • 关键词:KeywordsParameter estimationerror in variableselectrical networkslarge-scale systems
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