• DocumentCode
    2282609
  • Title

    Dynamic neural network for AGC in restructure power system

  • Author

    Sabahi, Kamel ; Narimani, Easa ; Faramarzi, Ahmad

  • Author_Institution
    Mamaghan Branch, Islamic Azad Univ., Mamaghan, Iran
  • fYear
    2010
  • fDate
    Nov. 29 2010-Dec. 1 2010
  • Firstpage
    594
  • Lastpage
    599
  • Abstract
    In This paper, a new adaptive controller based on unsupervised learning approach, named feedback error learning (FEL), is proposed for automatic generation control (AGC) of power system. In the FEL strategy, both feedforward and feedback controller are used for control of process, simultaneously. Generally, the feedback controller contains the classic controller, i.e. PID controller, and the feedforward controller is a neural network based controller. In this paper dynamic neural network (DNN) is used for feedforward controller. The DNN have some memory in his structure and improved the overall performance. The proposed FEL controller has been compared with the conventional FEL (CFEL) controller and the PID controllers for two areas restructure power system.
  • Keywords
    adaptive control; neurocontrollers; power generation control; unsupervised learning; AGC; FEL strategy; PID controller; adaptive controller; automatic generation control; conventional FEL controller; dynamic neural network; feedback controller; feedback error learning; restructure power system; unsupervised learning approach; Automatic generation control; dynamic neural network; feedback error learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy (PECon), 2010 IEEE International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4244-8947-3
  • Type

    conf

  • DOI
    10.1109/PECON.2010.5697651
  • Filename
    5697651