Diagnosing faults in power transformers with autoassociative neural networks and mean shift.
Diagnosing faults in power transformers with autoassociative neural networks and mean shift.
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Date
2012
Authors
Vladimiro Miranda
Adriana Castro
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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 or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, so that training becomes efficient. 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 of 100% is achieved with this architecture, in a validation data set using all real information available.