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A novel method based on neural networks to distinguish between load harmonics and source harmonics in a power system

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4 Author(s)
Mazumdar, J. ; Georgia Inst. of Technol., Atlanta, GA ; Harley, R.G. ; Lambert, F. ; Venayagamoorthy, G.K.

Utilities in recent years are experiencing increasing harmonic distortion problems. The harmonic voltages and currents deteriorate the power quality. This has lot of detrimental effect on equipments. A bigger issue is accurate determination of the source of harmonic distortion. Disputes arise between utility and customers regarding who is responsible for the harmonic distortions due to the lack of a reliable single index which can precisely point out the source of the harmonic pollution. The method proposed in this paper aims to tackle this problem with the aid of online trained neural networks. The main advantage of this method is that only waveforms of voltages and currents have to be measured. A neural network structure with memory is used to identify the non-linear load admittance of a load. Once training is achieved, the neural network predicts the true harmonic current of the load when supplied with a clean sine wave. This method is applicable for both single and three phase loads

Published in:

Power Engineering Society Inaugural Conference and Exposition in Africa, 2005 IEEE

Date of Conference:

11-15 July 2005