Dissolved Gas Analysis of Insulating Oil for Power Transformer Fault Diagnosis with Bayesian Neural Network

Abstract

Dissolved gas analysis is widely used for preventative maintenance techniques and fault diagnoses of oil-immersed power transformers. There are also various conventional methods of dissolved gas analysis for insulating oil in power transformers including methods of Doernenburg ratios, Rogers ratios and Duval’s triangle. The Bayesian techniques have been developed over many years and applied to a range of different fields including the problem of training in artificial neural networks. In particular, the Bayesian approach can solve the problem of over-fitting of artificial neural networks after being trained. The Bayesian framework can be also utilised to compare and rank different architectures and types of artificial neural networks. This research aims at deploying a detailed procedure of training artificial neural networks with the Bayesian inference, also known as Bayesian neural networks, to classify power transformer faults based on Doernenburg and Rogers gas ratios. In this research, the IEC TC 10 databases were used to form training and test data sets. The results obtained from the performance of trained Bayesian neural networks show that despite the limitation of the available dissolved gas analysis data, Bayesian neural networks with an appropriate number of hidden units can successfully classify power transformer faults with accuracy rates greater than 80%.