Abstract:Transformer bushings, as an indispensable part of the power system, are prone to potential defects in their oil-paper insulation structure due to various factors. This study aims to predict the content of dissolved gases in transformer bushings oil and evaluate their insulation status. To this end, we used the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) neural network, and the grey prediction (DGGM) model to predict the dissolved gas content of transformer bushings oil in the future time points. By comparing the performance of these models, we found that the optimized ARIMA model performed best in predicting gas content and evaluating insulation status, while the DGGM model''s average absolute error (MAE) in predicting CO2 was about 2.5 times that of the optimized ARIMA model. This research result can provide important technical support for the safe and stable operation of the power system.