• DocumentCode
    295872
  • Title

    Fast valving control using radial-basis function neural network

  • Author

    Chen, Qi ; Tan, Shaohua ; Han, Yingduo ; Wang, Zonghong

  • Author_Institution
    Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
  • Volume
    5
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    2247
  • Abstract
    Fast valving has long been seen as an effective and economic method to perform transient control in a power generation plant. Due to the inherent nonlinearities that exist in this operation, the fast valving controller designed in the conventional way cannot deliver a satisfactory control. This paper introduces a new approach to control fast valving by using a RBF(radial-basis function) neural network. A controller construction scheme is proposed, in which a stable learning algorithm is embedded. Then the implementation issue is discussed. From the outcome of on-line tests, it is seen that the controller constructed is effective and robust in many different fault situations
  • Keywords
    control system synthesis; feedforward neural nets; neurocontrollers; power system control; power system transients; robust control; controller construction scheme; fast valving control; fault situations; implementation; nonlinearities; power generation plant; radial-basis function neural network; stable learning algorithm; transient control; Control systems; Neural networks; Power engineering and energy; Power generation; Power generation economics; Power system economics; Power system stability; Power system transients; Testing; Valves;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.487711
  • Filename
    487711