Open AccessFault detection in power transformers using random neural networks
Author(s) -
Amrinder Kaur,
Yadwinder Singh Brar,
G Leena
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i1.pp78-84
Subject(s) - broyden–fletcher–goldfarb–shanno algorithm , computer science , dissolved gas analysis , artificial neural network , principal component analysis , dimensionality reduction , transformer , artificial intelligence , pattern recognition (psychology) , machine learning , data mining , transformer oil , engineering , voltage , computer network , asynchronous communication , electrical engineering
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.