• DocumentCode
    2718433
  • Title

    Intelligent automatic generation control: Multi-agent Bayesian networks approach

  • Author

    Bevrani, H. ; Daneshfar, F. ; Daneshmand, P.R.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Kurdistan, Sanandaj, Iran
  • fYear
    2010
  • fDate
    8-10 Sept. 2010
  • Firstpage
    773
  • Lastpage
    778
  • Abstract
    A new intelligent agent based control scheme, using Bayesian networks (BNs), to design automatic generation control (AGC) system in a multi-area power system is addressed. Model independency and flexibility in specifying the control objectives, make the proposed approach as an attractive solution for AGC design in a real-world power system. The proposed control scheme is tested in simulation on a three areas power system and shows desirable performance. The results are also compared with the multi-agent reinforcement learning based AGC design technique.
  • Keywords
    Bayes methods; intelligent control; multi-agent systems; power engineering computing; power generation control; automatic generation control system design; intelligent agent based control scheme; intelligent automatic generation control; multiagent Bayesian networks approach; multiarea power system; Bayesian methods; Computational modeling; Data models; Graphical models; Power system dynamics; Probability distribution; AGC; Bayesian networks; Frequency deviation; Multi-agent system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control (ISIC), 2010 IEEE International Symposium on
  • Conference_Location
    Yokohama
  • ISSN
    2158-9860
  • Print_ISBN
    978-1-4244-5360-3
  • Electronic_ISBN
    2158-9860
  • Type

    conf

  • DOI
    10.1109/ISIC.2010.5612931
  • Filename
    5612931