• DocumentCode
    3373957
  • Title

    Neural network approach of harmonics detection

  • Author

    Zin, A. A Mohd ; Rukonuzzaman, Md. ; Shaibon, H. ; Lo, K.L.

  • Author_Institution
    Fac. of Electr. Eng., Univ. Teknologi Malaysia, Malaysia
  • Volume
    2
  • fYear
    1998
  • fDate
    3-5 Mar 1998
  • Firstpage
    467
  • Abstract
    This paper describes a novel approach of harmonics detection in a power system which can be used as an alternative to the conventional approaches. The proposed approach uses the multilayer feed forward neural network to determine the harmonic components in a six-pulse bridge converter. In this paper the detection of 5th, 7th, and 11th harmonic components from the distorted waves has been verified by means of the computer simulation. It is found that once trained by the learning algorithm, the neural network can determine each harmonic component very effectively and efficiently
  • Keywords
    bridge circuits; digital simulation; feedforward neural nets; harmonic distortion; learning (artificial intelligence); multilayer perceptrons; power convertors; power system analysis computing; power system harmonics; 11th harmonic; 5th harmonic; 7th harmonic; computer simulation; distorted waves; harmonics detection; learning algorithm; multilayer feed forward neural network; power system; six-pulse bridge converter; Adaptive filters; Bridge circuits; Harmonic distortion; IIR filters; Multi-layer neural network; Neural networks; Power harmonic filters; Power system harmonics; Pulse power systems; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on
  • Print_ISBN
    0-7803-4495-2
  • Type

    conf

  • DOI
    10.1109/EMPD.1998.702706
  • Filename
    702706