Diagnosis on Transformer Fault Based on Bayesian Optimization XGBoost Algorithm

In order to improve the sensitivity of small sample fault diagnosis, such as high energy discharge, a transformer fault diagnosis model is proposed based on Bayesian optimization extreme gradient lifting algorithm (BO-XGBoost).The basic principle of Bayesian optimization XGBoost algorithm and the flow of transformer fault diagnosis based on this algorithm are analyzed.Two hundred and fifty-nine groups of fault samples are selected.The specific application of this model is discussed.The model is compared with XGBoost, Support Vector Machine (SVM), Random Forest (RF)and K proximity method (KNN).The results show that the accuracy of BO-XGBoost model in transformer fault diagnosis is 98.08%, which is 5.77%, 27.42%, 22.58% and 19.5% higher than that of the aforementioned model, respectively.