• DocumentCode
    3573746
  • Title

    The Fault Diagnosis of Power Transformer Based on Improved RBF Neural Network

  • Author

    Guo, Ying-Jun ; Li-Hua Sun ; Liang, Yong-Chun ; Ran, Hai-chao ; Hui-Qin Sun

  • Author_Institution
    Hebei Univ. of Sci. & Technol., Shijiazhuang
  • Volume
    2
  • fYear
    2007
  • Firstpage
    1111
  • Lastpage
    1114
  • Abstract
    The radial basis function (RBF) neural network is prior to BP neural network in the ability of approach, the ability of classification and the rate of train. A fault diagnosis method of power based on the RBF neural network is discussed in this paper. The example shows that two input vectors of different class may be more near than two input vectors of the same class. In order to overcome this defect, improve the ability of approach and the ability of classification, the input data is processed according to data reliability analysis and the center of RBF is trained according to the class of input data. The effect of improvement of RBF network has been approved in the fault diagnosis of power transformer.
  • Keywords
    backpropagation; fault diagnosis; power engineering computing; power transformers; radial basis function networks; BP neural network; RBF neural network; data reliability analysis; fault diagnosis; power transformer; radial basis function neural network; Cybernetics; Data analysis; Fault diagnosis; Frequency; Gaussian processes; Machine learning; Neural networks; Power system reliability; Power transformers; Vectors; Data reliability analysis; Fault diagnosis; Power transformer; RBF neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370310
  • Filename
    4370310