• DocumentCode
    656708
  • Title

    Blind topology identification for power systems

  • Author

    Xiao Li ; Poor, H. Vincent ; Scaglione, Anna

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of California, Davis, Davis, CA, USA
  • fYear
    2013
  • fDate
    21-24 Oct. 2013
  • Firstpage
    91
  • Lastpage
    96
  • Abstract
    In this paper, the blind topology identification problem for power systems only using power injection data at each bus is considered. As metering becomes widespread in the smart grid, a natural question arising is how much information about the underlying infrastructure can be inferred from such data. The identifiability of the grid topology is studied, and an efficient learning algorithm to estimate the grid Laplacian matrix (i.e., the graph equivalent of the grid admittance matrix) is proposed. Finally, the performance of our algorithm for the IEEE-14 bus system is demonstrated, and the consistency of the recovered graph with the true graph associated with the underlying power grid is shown in simulations.
  • Keywords
    IEEE standards; graph theory; learning (artificial intelligence); matrix algebra; power system identification; power system measurement; power system simulation; smart power grids; IEEE-14 bus system; blind topology identification problem; grid Laplacian matrix estimation; grid admittance matrix; grid topology identification; learning algorithm; power grid simulation; power injection data; power system; smart grid metering; true graph recovery; Eigenvalues and eigenfunctions; Laplace equations; Load modeling; Monitoring; Null space; Power grids; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Type

    conf

  • DOI
    10.1109/SmartGridComm.2013.6687939
  • Filename
    6687939