Artificial neural network based protection of power transformers

Zaman, Marzia Rabbi (1996) Artificial neural network based protection of power transformers. Doctoral (PhD) thesis, Memorial University of Newfoundland.

[English] PDF (Migrated (PDF/A Conversion) from original format: (application/pdf)) - Accepted Version Available under License - The 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. Download (21MB) [English] PDF - Accepted Version Available under License - The 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. (Original Version)

Abstract

In 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.

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