Title: Discrimination between inrush and fault condition in transformer: a probabilistic neural network approach

Authors: S.R. Paraskar; M.A. Beg; G.M. Dhole

Addresses: Department of Electrical Engineering, S.S.G.M.C.E., Shegaon, Buldhana, Maharashtra, 444203, India. ' Department of Electrical Engineering, S.S.G.M.C.E., Shegaon, Buldhana, Maharashtra, 444203, India. ' Department of Electrical Engineering, S.S.G.M.C.E., Shegaon, Buldhana, Maharashtra, 444203, India

Abstract: In this paper, an algorithm has been developed around the theme of the conventional differential protection of the transformer. The proposed algorithm is based on probabilistic neural network (PNN) and use of the spectral energies of detail level wavelet coefficients of differential current signal for discriminating magnetising inrush and fault condition in the transformer. Performance of the proposed PNN is investigated with the conventional backpropagation feed forward (BPFF) multilayer perceptron neural network. To evaluate the developed algorithm, relaying signals for various operating condition (i.e., inrush and fault) of the transformer, are obtained from a custom-built single-phase transformer in the laboratory.

Keywords: differential protection; discrete wavelet transform; DWT; inrush current; internal faults; probabilistic neural networks; PNNs; single-phase transformers.

DOI: 10.1504/IJCSYSE.2012.044743

International Journal of Computational Systems Engineering, 2012 Vol.1 No.1, pp.50 - 57

Published online: 23 Aug 2014 *

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