• DocumentCode
    1700196
  • Title

    Predicting load harmonics in three phase systems using neural networks

  • Author

    Mazumdar, Joy ; Harley, R.G. ; Lambert, F. ; Venayagamoorthy, Ganesh K.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
  • fYear
    2006
  • Abstract
    This paper proposes a artificial neural network (ANN) based method for the problem of measuring the actual harmonic current injected into a power system network by three phase nonlinear loads without disconnecting any loads from the network. The ANN directly estimates or identifies the nonlinear admittance (or impedance) of the load by using the measured values of voltage and current waveforms. The output of this ANN is a waveform of the current that the load would have injected into the network if the load had been supplied from a sinusoidal voltage source and is therefore a direct measure of load harmonics
  • Keywords
    harmonic distortion; neural nets; power system analysis computing; power system harmonics; artificial neural network; load harmonics; nonlinear admittance; nonlinear impedance; power system network; three phase nonlinear loads; Admittance; Artificial neural networks; Current measurement; Impedance; Neural networks; Phase measurement; Power measurement; Power system harmonics; Power system measurements; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applied Power Electronics Conference and Exposition, 2006. APEC '06. Twenty-First Annual IEEE
  • Conference_Location
    Dallas, TX
  • Print_ISBN
    0-7803-9547-6
  • Type

    conf

  • DOI
    10.1109/APEC.2006.1620775
  • Filename
    1620775