• DocumentCode
    527834
  • Title

    Fault diagnosis of wind turbine power electronic equipment based on SOM neural network

  • Author

    Guo, Hongche ; Wu, Bo

  • Author_Institution
    Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang, China
  • Volume
    4
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1679
  • Lastpage
    1681
  • Abstract
    It has great significance for reducing the operation failure rate and operation maintenance cost of wind turbine through monitoring possible fault power electronic devices and identifying the faults of power electronic devices. The paper applies self-organizing feature map neural network (SOM) to the fault diagnosis of wind turbine power electronic equipment and verifies its engineering application effect. The experimental results show that the method has great effect in fault diagnosis of wind turbine power electronic equipment, and it has some engineering value.
  • Keywords
    fault diagnosis; power engineering computing; power generation faults; self-organising feature maps; wind turbines; fault diagnosis; operation failure rate; operation maintenance cost; power electronic devices; self-organizing feature map neural network; wind turbine power electronic equipment; Artificial neural networks; Circuit faults; Fault diagnosis; Neurons; Power electronics; Training; Wind turbines; SOM neural network; power electronic device; wind turbine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5584567
  • Filename
    5584567