• DocumentCode
    473587
  • Title

    Application of EMD and SVD in fault identification

  • Author

    Zhu, Zhihui ; Sun, Yunlian

  • Author_Institution
    Sch. of Electr. Eng., Wuhan Univ., Wuhan
  • fYear
    2007
  • fDate
    3-6 Dec. 2007
  • Firstpage
    1247
  • Lastpage
    1250
  • Abstract
    The method based on empirical mode decomposition (EMD) and singular value decomposition (SVD) for power fault identification is presented in this paper. First, fault signal was adaptively decomposed into a series of smooth intrinsic mode functions (IMFs) with different time scales via EMD; second, the matrix is formed by different level IMFs and SVD method decompose the matrix to obtain singular value as eigenvector; finally, support vector machines (SVMs) is used as classifier to identify the fault type. The simulation results show that EMD and SVD can well extract the fault feature and SVMs network can attain high accuracy of fault identification.
  • Keywords
    fault location; power system faults; singular value decomposition; support vector machines; EMD; SVD; SVM; empirical mode decomposition; fault feature extraction; matrix decomposition; power fault identification; singular value decomposition; smooth intrinsic mode functions; support vector machines; Fault diagnosis; Power engineering; empirical mode decomposition; fault identification; singular value decomposition; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Conference, 2007. IPEC 2007. International
  • Conference_Location
    Singapore
  • Print_ISBN
    978-981-05-9423-7
  • Type

    conf

  • Filename
    4510216