Transformer fault diagnosis based on autoassociative neural networks
Transformer fault diagnosis based on autoassociative neural networks
No Thumbnail Available
Date
2011
Authors
Shigeaki Leite Lima
Adriana Garcez Castro
Vladimiro Miranda
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders are trained, so that each becomes tuned with a particular fault mode. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy is achieved with this architecture, in a large data set used for result validation.