• DocumentCode
    2069120
  • Title

    On the nonlinearity effects on malicious data attack on power system

  • Author

    Liyan Jia ; Thomas, R.J. ; Lang Tong

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
  • fYear
    2012
  • fDate
    22-26 July 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    There has been a growing literature on the malicious data attack (or data injection attack) on power systems. Most existing work focuses on the DC (linear) model with linear state estimators. This paper examines the effects of nonlinearity in the power systems on the effectiveness of malicious data attack on state estimation and real-time market. It is demonstrated that attack algorithms designed for the DC model may not be effective when they are applied to nonlinear system with nonlinear state estimators. Discussion and experiments results about nonlinearity are provided.
  • Keywords
    power markets; power system security; power system simulation; DC model; data injection attack; linear model; malicious data attack; nonlinear state estimators; nonlinear system; nonlinearity effects; power systems nonlinearity; real-time market; state estimation; Detectors; Electricity supply industry; Nonlinear systems; Power systems; Real-time systems; State estimation; Vectors; Bad Data Detection; Electricity Market; Malicious Data Attack; Nonlinear System; State Estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2012 IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4673-2727-5
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESGM.2012.6345685
  • Filename
    6345685