• DocumentCode
    1600172
  • Title

    Power System Fault Identification Method Based on Multi-wavelet Packet and Artificial Neural Network

  • Author

    Wang Ke ; Chen Weirong ; Li Qi

  • Author_Institution
    South west Jiao Tong Univ., Chengdu, China
  • fYear
    2012
  • Firstpage
    1457
  • Lastpage
    1462
  • Abstract
    The fault identification of power system is of great significance in the event of failure This paper introduce a fault identification method based on multi-wavelet packet and artificial neural network. Firstly, through the simulation of a two-500Kv power source transmission line on PSCAD/EMTDC, the variety of fault signals is generated in different conditions. Then, these fault signals are decomposed appropriately by multi-wavelet packets. Therefore, the energy features of the fault signals in each frequency band are obtained. BP neural network is trained by suitable training sample. Finally, each fault type can be automatically identified through combining multi-wavelet packet and BP neural network. From the results, the method is effective to identify the fault of high-voltage AC transmission line in power systems.
  • Keywords
    backpropagation; fault diagnosis; neural nets; power engineering computing; power transmission faults; power transmission lines; wavelet transforms; BP neural network; PSCAD-EMTDC; artificial neural network; fault signals; high-voltage AC transmission line; multiwavelet packet; power source transmission line; power system fault identification method; voltage 500 kV; Biological neural networks; Circuit faults; Fault diagnosis; Grounding; Power transmission lines; Resistance; Training; BP neural network; Fault classification; Multi-wavelet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-1-4577-2120-5
  • Type

    conf

  • DOI
    10.1109/ISdea.2012.421
  • Filename
    6173483