Transformer fault diagnosis based on autoassociative neural networks

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Date
2011
Authors
Shigeaki Leite Lima
Adriana Garcez Castro
Vladimiro Miranda
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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.
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