基于贝叶斯优化XGBoost算法的变压器故障诊断

Diagnosis on Transformer Fault Based on Bayesian Optimization XGBoost Algorithm

为提升对高能放电等小样本故障诊断的敏感度,提出基于贝叶斯优化极端梯度提升算法(BO-XGBoost)的变压器故障诊断模型。分析了贝叶斯优化XGBoost算法的基本原理和基于该算法进行变压器故障诊断的流程,选取259组故障样本,探讨了该模型的具体应用,并将其与XGBoost、支持向量机(SVM)、随机森林(RF)、K邻近法(KNN)等模型进行对比。结果表明,BO-XGBoost模型在变压器故障诊断中的精度为98.08%,比前述模型的诊断精度分别提高了5.77%、27.42%、22.58%、19.5%。

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.