• DocumentCode
    3272648
  • Title

    Fault Diagnosis against Oil-Immersed Transformer Based on PNN and GM (1,1)

  • Author

    Jia, Honghong ; Dai, Wenzhan

  • Author_Institution
    Zhejiang Sci-Tech Univ., Hangzhou
  • fYear
    2007
  • fDate
    20-24 March 2007
  • Firstpage
    394
  • Lastpage
    397
  • Abstract
    This paper introduces GM(1,1) and PNN technology into online fault diagnosis for oil-immersed transformer. Firstly, the seven fault types of oil-immersed transformer are characterized by the thickness of H2CH4,C2H6,C2H2 and C2H4. Secondly characteristic vector of fault status is forecasted by GM(1,1) model. Thirdly the forecasting results are chosen as the input of PNN and trained. At last, the method is applied to fault diagnose of oil-immersed transformer. The results show the approach effectively.
  • Keywords
    fault diagnosis; power engineering computing; power system faults; transformers; GM(1,1) model; GM(1,1) technology; PNN technology; oil-immersed transformer; online fault diagnosis; probabilistic neural network; Fault diagnosis; Gases; Hydrogen; Inspection; Oil insulation; Petroleum; Power transformer insulation; Predictive models; Sensor arrays; Temperature sensors; GM(1,1); Online Fault Diagnosis; Probabilistic Neural Network(PNN); Transformer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Integration Technology, 2007. ICIT '07. IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    1-4244-1092-4
  • Electronic_ISBN
    1-4244-1092-4
  • Type

    conf

  • DOI
    10.1109/ICITECHNOLOGY.2007.4290503
  • Filename
    4290503
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3272648