Forecasting-aided imperfect false data injection attacks against power system nonlinear state estimation

J. Zhao, G. Zhang, Z.Y. Dong, Kitpo Wong

    Research output: Contribution to journalArticle

    46 Citations (Scopus)

    Abstract

    © 2015 IEEE. This letter proposes an imperfect false data injection attack model and its corresponding forecasting-aided implementation method against the nonlinear power system state estimation by introducing an attack vector relaxing error. The upper bound of the relaxing error within the method is presented through theoretical analysis. Simulation experiments on the IEEE 30-bus system show that the proposed method works well both to the nonlinear model and to the dc model. In this letter, both single and multiple state variables attacks are considered.
    Original languageEnglish
    Pages (from-to)6-8
    JournalIEEE Transactions on Smart Grid
    Volume7
    Issue number1
    DOIs
    Publication statusPublished - 2016

    Fingerprint Dive into the research topics of 'Forecasting-aided imperfect false data injection attacks against power system nonlinear state estimation'. Together they form a unique fingerprint.

    Cite this