Application of back-propagation neural network for transformer differential protection schemes part 1 discrimination between external short circuit and internal winding fault

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 co...

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Bibliographic Details
Main Authors: Ngaopitakkul, A., Jettanasen, C., Klomjit, J., Pothisarn, C., Seewirote, B., Suttisinthong, N.
Format: Conference Proceeding
Language:English
Subjects:
Back-propagation neural network
Discrete Wavelet Transforms
External faults
Internal faults
Transformer windings
Online Access:Request full text
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Summary: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.
DOI:10.1109/SCIS-ISIS.2012.6505164