Redes neurais profundas aplicadas ao diagnóstico de faltas incipientes em transformadores imersos em óleo isolante.

Abstract

Diagnosing incipient faults in transformers is a major challenge because it is very difficult to define the source and type of fault, due to the variability in the conditions under which faults occur. Conventional methods based on the analysis of dissolved gases in oil have been used by companies to diagnose faults, however, these methods still need to be applied together to reach a satisfactory result, as well as relying heavily on the knowledge of a specialist. In order to solve the difficulties related to conventional methods, some systems based on Computational Intelligence have been proposed in the literature and have presented promising results. This paper presents the results of the study developed of the application of deep neural networks to fault diagnosis, considering then the importance of fault diagnosis in transformers. Two models are proposed using Convolutional Neural Networks and Stacked Autoencoding Neural Networks. For the development of the systems we used the TC 10 database with faulty transformer situations. This base was used to develop the IEC 60599 method, which is one of the main methods used by power utilities for transformer diagnostics through the analysis of dissolved gases in oil. The promising results achieved with the two proposed models (100% accuracy in the test base) show the great applicability of deep neural networks to the problem of incipient transformer fault diagnosis, however showing a great alternative to the conventional methods commonly used.