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.
J. Mazumdar et al., "Predicting Load Harmonics in Three Phase Systems Using Neural Networks," Proceedings of the 21st Annual IEEE Applied Power Electronics Conference and Exposition, 2006. APEC '06, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at http://dx.doi.org/10.1109/APEC.2006.1620775
21st Annual IEEE Applied Power Electronics Conference and Exposition, 2006. APEC '06
Electrical and Computer Engineering
Keywords and Phrases
Artificial Neural Network; Harmonic Distortion; Load Harmonics; Neural Nets; Nonlinear Admittance; Nonlinear Impedance; Power System Analysis Computing; Power System Harmonics; Power System Network; Three Phase Nonlinear Loads
Article - Conference proceedings
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