Fault diagnosis of transformer based on XGBoost

Abstract. With the continuous development of technology, the fault diagnosis of transformer is also continuously advancing. Traditional fault diagnosis methods such as the three-ratio method and the characteristic gas method have the disadvantages of missing coding and limited identification capabilities. Data-driven fault diagnosis methods can integrate various information for data analysis to accurately diagnose the type of fault. This paper proposes a fault diagnosis method for transformers based on the XGBoost model. First, the input data is selected for the ratio feature engineering, and the obtained ratios are also used as input data, and then the input data is normalized for data preprocessing. Secondly, the XGBoost model is introduced, which minimizes the prediction errors in each round of gradient boosting, thereby improving the accuracy and recall rate. Finally, through case analysis, comparisons are made with other methods in various performance indicators, and the accuracy is improved. The results show that the XGBoost is more accurate in the fault diagnosis of transformers, proving that the research has certain value and significance.