Power transformers fault diagnosis using graph neural networks based on dissolved gas data

Abstract

The Dissolved Gas Analysis (DGA) is an important way and is now extensively used to detect incipient faults in oil-immersed transformers. Some conventional methods for DGA, such as the Three-Ratio Method, usually misunderstood the gas data and failed to accurately detect multiple fault combinations. Recently, most researchers investigating DGA have utilized machine learning algorithms to improve the accuracy of fault diagnosis. The dissolved gas data are usually treated as tabular data by using machine learning algorithms to achieve better results compared to conventional methods. However, those machine learning methods ignore the internal connection of the dissolved gas data in the time dimension. Therefore, this paper adopted a new DGA method for transformer fault diagnosis based on Graph Neural Network (GNN) and Multivariate Time Series (MTS) model. The results finally showed that the model outperforms the existing machine learning baseline methods and conventional methods on the dataset.