• DocumentCode
    3457845
  • Title

    An artificial neural network based method for harmonic detection in power system

  • Author

    Na, He ; Lina, Huang ; Jian, Wu ; Dianguo, Xu

  • Author_Institution
    Dept. of Electr. Eng., Harbin Inst. of Technol., Harbin
  • fYear
    2008
  • fDate
    24-28 Feb. 2008
  • Firstpage
    456
  • Lastpage
    461
  • Abstract
    A novel advanced harmonic detection method based on neural network (NN) is proposed in this paper. It is an adaptive harmonic detection method with variable step-size based on adaptive linear NN and self-adaptive noise countervailing principle. And this proposed method adopts a sliding integrator to extract the real tracing error and then uses a fuzzy adjuster with self-adjustable factor to modify the step-size. So the novel harmonic detection method can obtain fast convergence speed and high steady-state precision at the same time. Comparisons are made between conventional harmonic detection methods based on NN and the advanced method based on NN proposed in this paper. Finally detailed simulation and experimental results verify the validity and superiority of the advanced methods.
  • Keywords
    fuzzy set theory; neural nets; power engineering computing; power system harmonics; adaptive harmonic detection method; adaptive linear NN; artificial neural network; fuzzy adjuster; high steady-state precision; power system; self-adaptive noise countervailing principle; sliding integrator; variable step-size; Active filters; Artificial neural networks; Convergence; Fuzzy systems; Neural networks; Power harmonic filters; Power system harmonics; Power system simulation; Robustness; Steady-state; Harmonic detection; fuzzy adjustor; neural network (NN); selfadaptive noise countervailing principle;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applied Power Electronics Conference and Exposition, 2008. APEC 2008. Twenty-Third Annual IEEE
  • Conference_Location
    Austin, TX
  • ISSN
    1048-2334
  • Print_ISBN
    978-1-4244-1873-2
  • Electronic_ISBN
    1048-2334
  • Type

    conf

  • DOI
    10.1109/APEC.2008.4522761
  • Filename
    4522761