Transformer DGA Fault Diagnosis Method Based on Stacked Sparse Auto-Encoders

Abstract In the field of transformer fault diagnosis, the imbalance of fault samples seriously affects the fault identification performance of the diagnosis model. Focusing on the problems of low accuracy and high leakage rate of diagnosis model caused by unbalanced fault samples of transformer, a transformer fault diagnosis method is proposed. First of all, the Borderline SMOTE algorithm is used to balance the fault data set from a few samples on the boundary, so as to achieve the effect of power transformer fault sample equalization. Secondly, a diagnosis model of the stacked sparse auto-Encoders with the gas in oil as input characteristic parameter is built. Finally, an evaluation system consisting of accuracy rate, recall rate and F1 score is selected to compare the diagnosis effect of the model before and after category equalization, the experiment proves the effectiveness of this method.