A long short-term memory based prediction model for transformer fault diagnosis using dissolved gas analysis with digital twin technology

Abstract

The most significant tool for defect diagnostics in transformers is dissolved gas analysis (DGA). The time series prediction of dissolved gas levels in oil, when combined with dissolved gas analysis, provides a foundation for transformer fault diagnosis and an early warning. A long short-term memory (LSTM) based prediction model is developed in this paper to train the digital twin for identifying the essential fault in the transformer via DGA. The model is fed with three different gas concentrations as input. This study achieves the performance evaluation in terms of validation accuracy. The suggested model exhibits significant validation accuracy of 99.83%, as indicated by the analyses, thus the early prediction of transformer maintenance is aided. It can be validated that the LSTM model for fault identification and analysis using dissolved gas in the transformer has a lot of research potential.