• DocumentCode
    3587865
  • Title

    Dynamic joint outage identification and state estimation in power systems

  • Author

    Yue Zhao ; Jianshu Chen ; Goldsmith, Andrea ; Poor, H. Vincent

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Stony Brook Univ., Stony Brook, NY, USA
  • fYear
    2014
  • Firstpage
    1138
  • Lastpage
    1142
  • Abstract
    Joint outage identification and state estimation is studied in power systems in which cascading outages dynamically develop and network states dynamically evolve. A recursive algorithm is developed that computes in closed form the joint posterior of cascades and network states at every time step. A beam search technique is employed that prevents the number of cascades to compute from growing exponentially. Because the joint posterior is a sufficient statistic for jointly identifying the cascades and estimating the states, the derived closed forms can be applied to develop the optimal dynamic joint detector and estimator under any performance criterion. We simulate cascading line outages with uncertain network states in the IEEE 14-bus and 57-bus systems, and the proposed algorithm is evaluated for dynamically identifying outages and estimating states at every time step. It is observed that retaining just a few cascades in the beam search can achieve a joint identification and estimation performance close to that with all cascades retained.
  • Keywords
    power system reliability; recursive estimation; IEEE 14-bus system; IEEE 57-bus system; beam search technique; cascading outage; dynamic joint outage identification; optimal dynamic joint detector; power system state estimation; recursive algorithm; uncertain network state; Error probability; History; Joints; Power system dynamics; Power system faults; State estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers, 2014 48th Asilomar Conference on
  • Print_ISBN
    978-1-4799-8295-0
  • Type

    conf

  • DOI
    10.1109/ACSSC.2014.7094635
  • Filename
    7094635