Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

Abstract

Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians.