Fault Diagnosis of Dissolved Gas Analysis for Transformer Based on XGBoost

Aiming at the fault diagnosis problem of oil-immersed transformers, this paper proposes a transformer oil dissolved gas analysis (DGA) fault diagnosis method based on the XGBoost algorithm. Traditional diagnostic methods have defects such as vague boundary values and poor interpretability. However, the XGBoost algorithm can effectively capture the nonlinear relationship between DGA data and fault types by iteratively optimizing the additive model. In the study, 1260 sets of DGA data were preprocessed, including mean normalization to eliminate the influence of dimensions, construction of gas ratio features to enhance sensitivity, and conversion of fault types into numerical labels. The model was optimized by setting hyperparameters such as learning_rate and max_ depth, and using 5-fold cross-validation and early stopping mechanism. Experimental results show that the accuracy of the XGBoost model on the test set reaches 93.6%, which is significantly higher than that of LSTM (82.3%) and PSO-LSTM (85.7%), and the RMSE and MAE indicators are better. The research shows that this method can accurately diagnose transformer faults and provide effective technical support for the safe operation of power systems.