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Leena G.
Manav Rachna International Institute of Research & Studies
Computer Science & IT Electrical & Electronics Engineering

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Fault detection in power transformers using random neural networks Amrinder Kaur; Yadwinder Singh Brar; Leena G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1071.245 KB) | DOI: 10.11591/ijece.v9i1.pp78-84

Abstract

This paper discuss the application of artificial neural network-based algorithms to identify different types of faults in a power transformer, particularly using DGA (Dissolved Gas Analysis) test. The analysis of Random Neural Network (RNN) using Levenberg-Marquardt (LM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms has been done using the data of dissolved gases of power transformers collected from Punjab State Transmission Corporation Ltd.(PSTCL), Ludhiana, India. Sorting of the preprocessed data have been done using dimensionality reduction technique, i.e., principal component analysis. The sorted data is used as inputs to the Random Neural Networks (RNN) classifier. It has been seen from the results obtained  that BFGS has better performance for the diagnosis of fault in transformer as compared to LM.
Co-Authors Amrinder Kaur Yadwinder Singh Brar