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.