Discrimination between inrush and fault condition in transformer: a probabilistic neural network approach. Issue 1 (1st January 2012)

• Record Type: Journal Article
• Authors: Paraskar, S.R.; Beg, M.A.; Dhole, G.M.
• Is Part Of: International journal of computational systems engineering. Volume 1:Issue 1(2012)
• Page Start: 50
• Page End: 57
• Publication Date: 2012-01-01
• Subjects: differential protection -- discrete wavelet transform -- DWT -- inrush current -- internal fault -- probabilistic neural network -- PNN; Computer science -- Periodicals; Electronic data processing -- Periodicals; System analysis -- Periodicals; 003.3
• Languages: English
• ISSNs: 2046-3391

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.