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Application of back-propagation neural network for transformer differential protection schemes part 1 discrimination between external short circuit and internal winding fault
Author(s)
Klomjit, Jittiphong
Seewirote, Buncha
Suttisinthong, Nuchtita
Date Issued
December 1, 2012
Type
Conference Paper
Abstract
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for discriminating between external fault and internal winding fault of three-phase two-winding transformer. The DWT is employed for extracting the high frequency component contained in the post-fault differential current waveforms, and the coefficients of the first scale from the DWT that can detect fault are investigated as an input for the training pattern. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique is highly satisfactory. © 2012 IEEE.
Citation
6th International Conference on Soft Computing and Intelligent Systems and 13th International Symposium on Advanced Intelligence Systems Scis Isis 2012, 1493-1498, 2012