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- Title
Fault detection in power transformers using random neural networks.
- Authors
Kaur, Amrinder; Brar, Yadwinder Singh; G., Leena
- 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.
- Subjects
PUNJAB; LUDHIANA (India); POWER transformers; MULTIPLE correspondence analysis (Statistics); FAULT diagnosis; GAS analysis
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2019, Vol 9, Issue 1, p78
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v9i1.pp78-84