Fault detection in power transformers using random neural networks

Amrinder Kaur (Inder Kumar Guzral Punjab Technical University)
Yadwinder Singh Brar (Inder Kumar Guzral Punjab Technical University)
Leena G. (Manav Rachna International Institute of Research & Studies)

International Journal of Electrical and Computer Engineering
Vol 9, No 1: February 2019

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.