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
Link To Document :
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