• DocumentCode
    2793973
  • Title

    Identifying of Hydraulic Turbine Generating Unit Model Based on Neural Network

  • Author

    Xiao, Zhihuai ; Wang, Shuqing ; Zeng, Hongtao ; Yuan, Xiaohui

  • Author_Institution
    Coll. of Power & Mech. Eng., Wuhan Univ.
  • Volume
    1
  • fYear
    2006
  • fDate
    16-18 Oct. 2006
  • Firstpage
    113
  • Lastpage
    117
  • Abstract
    It is difficult to describe hydraulic turbine generating unit system via accurate mathematics model because it is a complicated non-liner system. In the paper, RBF neural networks models were established to identify hydraulic turbine generating unit. In RBF networks training, a practical learning algorithm was proposed for adjusting effectively the node number, centers and width of Gaussian function of hidden layer nodes. Off-line training and on-line identifying were combined together to train networks and identify hydraulic turbine generating unit system. Simulation results show that the designed model can well identify the characteristic of hydraulic turbine generating unit. Thus, the identifying model can lay the good foundation for study on intelligent control strategies of hydraulic turbine generating system
  • Keywords
    Gaussian processes; hydraulic turbines; hydroelectric generators; radial basis function networks; Gaussian function; RBF neural network; hydraulic turbine generating unit model; radial basis function network; Artificial neural networks; Character generation; Educational institutions; Hydraulic turbines; Hydroelectric power generation; Mathematical model; Neural networks; Power generation; Power system modeling; Radial basis function networks; RBF neural network; generating unit; hydraulic turbine; model identifying;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    0-7695-2528-8
  • Type

    conf

  • DOI
    10.1109/ISDA.2006.172
  • Filename
    4021419