• DocumentCode
    2825921
  • Title

    SVM and Classification Ensembles based High-voltage Transmission Line Fault Diagnosis

  • Author

    Bin, Shen ; Min, Yao ; Bo, Yuan

  • Author_Institution
    Coll. of Comput., Zhejiang Univ., Hangzhou
  • fYear
    2005
  • fDate
    21-23 Sept. 2005
  • Firstpage
    11
  • Lastpage
    17
  • Abstract
    This paper analyzes the inner mechanism of basic methods for high-voltage transmission line (HTL) fault diagnosis, and proposes the new SVM based HTL diagnosis models, which has the characteristic of good generalization. We also put forward the neural network ensembles model and multiple kinds of classifiers ensembles model based on the technology of classifier ensembles. These models can further promote the performance of single classifiers, such as traditional NN, rough set rules classifier, SVM etc. The simulation and experiments results completely show that our new models are more efficient than traditional ones
  • Keywords
    fault diagnosis; neural nets; pattern classification; power engineering computing; power transmission faults; power transmission lines; rough set theory; support vector machines; SVM based HTL diagnosis models; classifier ensembles; high-voltage transmission line fault diagnosis; neural network ensembles model; rough set rules classifier; Fault diagnosis; Neural networks; Power system faults; Power system protection; Power system restoration; Power transmission lines; Support vector machine classification; Support vector machines; Transmission line theory; Transmission lines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology, 2005. CIT 2005. The Fifth International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    0-7695-2432-X
  • Type

    conf

  • DOI
    10.1109/CIT.2005.180
  • Filename
    1562620