• DocumentCode
    1615802
  • Title

    A multi-agent technology based predictive control strategy in cascading failures of large power grids

  • Author

    Yang Jun ; Wang Xinyi ; Sun Qiuye ; Zhao Qingqi

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
  • fYear
    2013
  • Firstpage
    900
  • Lastpage
    905
  • Abstract
    A real-time predictive control strategy based on multi-agent technology is presented to prevent cascading trips which can trigger a blackout accident in bulk power systems. The optimum principle of load shedding and generator tripping is also given. Every node in power grid is regarded as an agent differing from traditional distributed computation that used the subarea or substratum method. Each agent calculates independently and communicates with others simultaneously though its action module, data acquisition module, computation module and communication module. Then the optimal load shedding and generator tripping can be obtained by the proposed rolling optimization of predictive control method. The measurement data for the optimization algorithm can be acquired from WAMS.
  • Keywords
    data acquisition; electric generators; electrical accidents; load shedding; multi-agent systems; optimisation; power grids; power system reliability; predictive control; WAMS; blackout accident; bulk power systems; cascading failure; communication module; generator tripping; large power grid; multi-agent technology; optimal load shedding; predictive control method; rolling optimization; Generators; Optimization; Power grids; Power system faults; Power system protection; Predictive control; cascading failure; load shedding and generator tripping; multi-agent system; optimization algorithm; predictive control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Chinese Automation Congress (CAC), 2013
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-0332-0
  • Type

    conf

  • DOI
    10.1109/CAC.2013.6775860
  • Filename
    6775860