• DocumentCode
    508217
  • Title

    Deformation Prediction of Transmission Pole Foundation by Using Improved BP Neural Network

  • Author

    Yong, Zhang ; Yunyun, Zhao

  • Author_Institution
    Hebei Univ. of Eng., Handan, China
  • Volume
    3
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    607
  • Lastpage
    611
  • Abstract
    Based on the field survey data of the goaf along the UHV path and by including the main geological and mining factors, the stability of UHV transmission pole foundation via Shanxi goaf have been analyzed in details. Using BP artificial neural network method, the paper set up the prediction model of subsidence deformation of pole foundation above the goaf through experiment and study of the data samples. Levenberg-Marquardt algorithm was applied in order to achieve better results. It is concluded that by using BP neural network model, predicting pole foundation stability of the goaf is convenient, reliable, and more applicable.
  • Keywords
    backpropagation; neural nets; poles and towers; power engineering computing; Levenberg-Marquardt algorithm; Shanxi goaf; UHV transmission pole foundation; deformation prediction; improved BP neural network; Artificial neural networks; Computer networks; Deformable models; Geology; Neural networks; Nonlinear distortion; Predictive models; Stability; Surface cracks; Transmission lines; 1000kV UHV; BP neural network; goaf; pole foundation stability prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.474
  • Filename
    5366018