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Memorial University - Electronic Theses and Dissertations 2
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Document Description
TitleArtificial neural network based protection of power transformers
AuthorZaman, Marzia Rabbi, 1965-
DescriptionThesis (Ph.D.)--Memorial University of Newfoundland, 1996. Engineering and Applied Science
Paginationxvii, 175 leaves : ill.
SubjectElectric power systems--Protection; Neural networks (Computer science); Electric transformers--Protection
Degree GrantorMemorial University of Newfoundland. Faculty of Engineering and Applied Science
DisciplineEngineering and Applied Science
NotesBibliography: leaves 143-154.
AbstractIn this work, an artificial neural network based algorithm for a three phase power transformer protection scheme is developed and implemented in real time using the DS-1102 digital signal processor. Distinguishing between the magnetizing inrush and internal fault currents is always a consideration for power transformer protection. Existing methods, mostly based on harmonic restraint, are not very reliable for modern transformer protection. The reason for loss of reliability is that the use of low-loss amorphous material in modern transformer cores causes reduced second harmonic content of the magnetizing inrush current. Other methods based on the transformer equivalent circuit model are susceptible to parameter variations and hence are not suitable under all operating conditions. The work presented here shows the usefulness of the artificial neural network which is able to distinguish between the magnetizing inrush and the internal fault currents without harmonic decomposition or using the transformer equivalent circuit model. The inherent advantages of the generalization and the pattern recognition characteristics make the artificial neural network based method quite suitable for distinguishing between magnetizing inrush and the internal fault currents. In this work, a two-layer artificial neural network with sixteen inputs and one output is designed. The data to train and test the artificial neural network are experimentally obtained. The artificial neural network is trained with an input data set and subsequently tested with a different data set. The off-line test results show that the artificial neural network is quite capable of distinguishing between the magnetizing inrush and internal fault currents. Finally, the on-line implementation successfully establishes the efficacy of the ANN based algorithm for power transformer protection.
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
RightsThe author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(20.28 MB) --
CONTENTdm file name265227.cpd