巢湖学院学报2024，Vol.26Issue(6)：87-93,128,8.DOI:10.12152/j.issn.1672-2868.2024.06.011
基于DGA的NRBO-XGBoost变压器故障诊断方法
NRBO-XGBoost Transformer Fault Diagnosis Method Based on DGA
摘要
Abstract
To enhance the accuracy of transformer fault diagnosis based on machine learning,a transformer fault diagnosis method using NRBO-XGBoost based on Dissolved Gas Analysis(DGA)is proposed.The Extreme Gra-dient Boosting(XGBoost)model is selected.The Newton-Raphson-based optimizer(NRBO)is combined with XG-Boost to iteratively search for the optimal model parameters.In each iteration,the performance of the current solu-tion is evaluated,and the XGBoost model is retrained using the optimal parameters obtained.A significant improve-ment in model performance is observed by comparing the results before and after optimization.The performance of the NRBO-XGBoost method is evaluated through a case study,demonstrating the effectiveness of the proposed method for transformer fault diagnosis,with good convergence and high accuracy.关键词
变压器/故障诊断/牛顿-拉夫逊算法/极度梯度提升决策树Key words
transformer/fault diagnosis/Newton-Raphson-based optimizer/Extreme Gradient Boosting分类
信息技术与安全科学引用本文复制引用
阮义,张浩天,孙建,刘翔,夏亮亮,孙阿欢,方愿捷..基于DGA的NRBO-XGBoost变压器故障诊断方法[J].巢湖学院学报,2024,26(6):87-93,128,8.基金项目
国家自然科学基金青年基金项目(项目编号:62303076) （项目编号:62303076）
安徽省高校自然科学研究项目(项目编号:2023AH052109、2023AH052105) （项目编号:2023AH052109、2023AH052105）
