Research on the Transformer Fault Diagnosis Method Based on LSTM Artificial Neural Network and DGA

The concentration of dissolved gas in transformers is closely related to their operating status. Aiming at dissolved gas analysis (DGA) in transformer oil, this paper proposes a fault diagnosis method for transformer DGA based on long-term and short-term memory (LSTM) artificial neural networks. The method uses 240 sets of samples collected by China Southern Power Grid Corporation, with 180 sets as training data and the remaining 60 sets as test data. The input consists of five kinds of dissolved gases in oil, and the output is the corresponding fault type. The hyperparameters (H1=H2=50) of the network are determined through experimentation to establish a transformer DGA fault diagnosis model based on LSTM. The research results indicate that the LSTM diagnosis model has higher consistency with actual fault types compared to the traditional neural network diagnosis model. These findings demonstrate the promising application prospects of LSTM in the field of transformer DGA fault diagnosis.