Diagnosing Faults in Power Transformers With Variational Autoencoder, Genetic Programming, and Neural Network

This work presents a new approach for the diagnosis of incipient faults in power transformers by considering dissolved gas analysis (DGA). A multilayer perceptron (MLP) neural network was trained to diagnose the type of transformer fault. For training and testing of the classifier, data were used from in-service transformers obtained from the IEC TC 10 database and other data obtained from the literature. To address the imbalance of the data from the database adopted and thus improve the generalization power of the classifier, a data augmentation technique based on a variational autoencoder neural network was used. For the selection and extraction of characteristics from the inputs to the classifier, a technique based on genetic programming (GP) is proposed, which allows the creation of a new n-dimensional space of characteristics, providing a greater ability to increase interclass distances and intraclass compaction. For the performance analysis of the proposed classifier, comparisons were made using the classification results obtained through the IEC 60599 conventional fault diagnosis method and other trained MLPs without the use of data augmentation and the proposed characteristics extractor. The results obtained demonstrate the applicability of the proposed methodology for fault diagnosis, with the proposed system obtaining an accuracy of 95.18% in the test basis, which is higher than the results achieved by the other methods used to perform a comparison and analysis of results.