• DocumentCode
    498993
  • Title

    Safety assessment in power supply enterprise based on kernel principal component analysis and fast multi-class support vector machine

  • Author

    Sun, Wei ; Ma, Guo-zhen

  • Author_Institution
    Dept. of Econ. Manage., North China Electr. Power Univ., Baoding, China
  • Volume
    2
  • fYear
    2009
  • fDate
    12-15 July 2009
  • Firstpage
    1214
  • Lastpage
    1218
  • Abstract
    According to the requirement for safety of power supply enterprises, a new method based on kernel principal component analysis and support vector machine is introduced in this paper: kernel principal component analysis which extracts the most important factors influencing safety can optimize the parameters of support vector machine. Fast multi-class support vector machine, as the evaluation tool, classifies enterprises´ safety condition into four groups. The result of experiment shows that the method can reduce the complex of assessment and is more comprehensive. It also improves rapidity and accuracy of traditional SVM model. Furthermore, the safety of power supply enterprises can be improved by the new method.
  • Keywords
    power engineering computing; power markets; power system management; principal component analysis; support vector machines; SVM model; kernel principal component analysis; multiclass support vector machine; power supply enterprise; safety assessment; Conference management; Cybernetics; Electrical safety; Energy management; Kernel; Machine learning; Power supplies; Principal component analysis; Support vector machine classification; Support vector machines; Fast multi-class support vector machine; Kernel principal component analysis; Safety assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Conference_Location
    Baoding
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212424
  • Filename
    5212424