Missouri S&T Scholar's Mine Research RepositoryMissouri S&T Research
print 
Title: Predicting load harmonics in three phase systems using neural networks
Author (s): Mazumdar, J.
Harley, R.G.
Lambert, F.
Venayagamoorthy, Ganesh K.
Department/Lab Affiliations: Electrical and Computer Engineering
Real-Time Power and Intelligent Systems Laboratory
Keywords: 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
Issue Date: 2006
Publisher: Institute of Electrical and Electronics Engineers
Citation: Mazumdar, J.; Harley, R.G.; Lambert, F.; Venayagamoorthy, G.K., "Predicting load harmonics in three phase systems using neural networks" APEC '06. Twenty-First Annual IEEE Applied Power Electronics Conference and Exposition, 2006. pp. 7 pp.-, 19-23 March 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.
Type: Article - Conference proceedings
text
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
FULL COPYRIGHT INFORMATION:
http://www.ieee.org/web/publications/rights/policies.html
Publisher URL:
http://ieeexplore.ieee.org/iel5/10769/33947/01620775.pdf?arnumber=162077
Link to this page:
http://scholarsmine.mst.edu/post_prints/01620775_09007dcc8030d9e5.html
Full Text:
01620775_09007dcc8030d9ea.pdf



titlePredicting load harmonics in three phase systems using neural networks
contributor.authorMazumdar, J.
contributor.authorHarley, R.G.
contributor.authorLambert, F.
contributor.authorVenayagamoorthy, Ganesh K.
contributor.deptlabElectrical and Computer Engineering
contributor.deptlabReal-Time Power and Intelligent Systems Laboratory
subjectartificial neural network
subjectharmonic distortion
subjectload harmonics
subjectneural nets
subjectnonlinear admittance
subjectnonlinear impedance
subjectpower system analysis computing
subjectpower system harmonics
subjectpower system network
subjectthree phase nonlinear loads
date.issued2006
date.submitted2007
publisherInstitute of Electrical and Electronics Engineers
identifier.citationMazumdar, J.; Harley, R.G.; Lambert, F.; Venayagamoorthy, G.K., "Predicting load harmonics in three phase systems using neural networks" APEC '06. Twenty-First Annual IEEE Applied Power Electronics Conference and Exposition, 2006. pp. 7 pp.-, 19-23 March 2006
identifier.pub.URI
http://ieeexplore.ieee.org/iel5/10769/33947/01620775.pdf?arnumber=162077
description.abstractThis 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.
typeArticle - Conference proceedings
type.DCMITypetext
type.statusFinal version
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rights.URI
http://www.ieee.org/web/publications/rights/policies.html
date.accessioned2007-04-05T14:26:45Z
date.available2007-04-05T14:26:44Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/01620775_09007dcc8030d9e5.html
Full Text
01620775_09007dcc8030d9ea.pdf