NDLI: Identifying Harmonic Contributions from Non-Linear Loads using Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Mazumdar, J. Harley, R.G. Lambert, F.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Georgia Inst. of Technol., Atlanta, GA (Mazumdar, J.; Harley, R.G.)
Abstract	This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument
Starting Page	117
Ending Page	122
File Size	284182
Page Count	6
File Format	PDF
ISBN	1599751747
DOI	10.1109/ISAP.2005.1599250
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ISAP
Subject Keyword	Power measurement Recurrent neural networks Substations Current measurement Instruments Neural networks Voltage Power system harmonics Harmonic distortion Distortion measurement
Content Type	Text
Resource Type	Article

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