• DocumentCode
    2736844
  • Title

    Application of adaptive neuro fuzzy inference system to the partial discharge pattern recognition

  • Author

    Guo, Canxin ; Zhang, Li ; Qian, Yong ; Huang, Chengjun ; Wang, Hui ; Yao, Linpeng ; Jiang, Xiuchen

  • Author_Institution
    Dept. of Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
  • Volume
    2
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    729
  • Lastpage
    732
  • Abstract
    The application of adaptive neuro fuzzy inference system (ANFIS) to the partial discharge (PD) pattern recognition is presented in this paper. Four types of defect models are made according to the main reason of insulation failures in real power system. Experiments are carried out to acquire the sample data, from which eight statistical features are extracted to construct the ANFIS. Different characteristics of the proposed defect models are compared based on the extracted features. Then the ANFIS is trained by characteristic features. Testing samples are utilized to validate the performance of the recognition system. The result shows that ANFIS reaches a successful recognition rate in the application of PD pattern classification.
  • Keywords
    adaptive systems; discharges (electric); fuzzy reasoning; pattern recognition; power system faults; statistical analysis; adaptive neuro fuzzy inference system; defect models; insulation failures; partial discharge pattern recognition; power system; statistical features; Circuit testing; Data mining; Feature extraction; Fuzzy logic; Fuzzy systems; Partial discharges; Pattern recognition; Power system modeling; System testing; Voltage; ANFIS; PD; features extraciton; pattern recognition; power apparatus;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5358281
  • Filename
    5358281