基于实域粗糙集和NRBO-XGBoost的变压器故障诊断  

Research on transformer fault diagnosis method based on real domainrough set and NRBO-XGBoost

作　　者：杨勇[1] 胡东[1] 代浩 唐超[1] 谢菊芳[1] Yang Yong;Hu Dong;Dai Hao;Tang Chao;Xie Jufang(School of Engineering and Technology,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学工程技术学院,重庆400715

出　　处：《电子测量技术》2025年第5期30-39,共10页Electronic Measurement Technology

基　　金：国家自然科学基金(51977179)项目资助。

摘　　要：针对基于油中气体分析(DGA)数据的传统变压器智能诊断模型准确率容易受到输入特征维度以及超参数的选择影响,本研究提出了基于实域粗糙集和NRBO-XGBoost相结合的变压器智能故障诊断模型。首先,基于实域粗糙集的概念提出了一种具有自适应性能的特征提取算法用于对变压器初始故障特征进行特征提取;其次针对变压器故障诊断中XGBoost受超参数选择影响的缺陷,本研究利用NRBO算法高收敛速度和有效避免局部最优的特点对XGBoost的超参数进行全局寻优,从而提出NRBO-XGBoost模型进行变压器故障诊断;最后通过多组实验对比,相较于其他传统特征,使用本研究所提取特征在多种分类器中的性能都得到了提升,证明了本文所提特征提取算法能有效提取特征中的信息增强模型表现性能。并且NRBO-XGBoost在变压器故障诊断领域相较于其他对比模型仅收敛20次的同时就达到了92.09%的准确率,拥有更优越的表现性能。In response to the fact that the accuracy of traditional transformer intelligent diagnosis models based on dissolved gas analysis(DGA)data is easily affected by the selection of input feature dimensions and hyperparameters,this study proposes a transformer intelligent fault diagnosis model based on the combination of real domain rough sets and NRBO-XGBoost.Firstly,a feature extraction algorithm with adaptive performance is proposed based on the concept of real domain rough set for extracting initial fault features of transformers;secondly,in response to the limitation of XGBoost being affected by hyperparameter selection in transformer fault diagnosis,this study utilizes the high convergence speed and effective avoidance of local optima of the NRBO algorithm to globally optimize the hyperparameters of XGBoost,and proposes the NRBO-XGBoost model for transformer fault diagnosis;finally,through multiple experimental comparisons,compared with other traditional features,the performance of the feature extraction algorithm proposed in this study has been improved in various classifiers,proving that the feature extraction algorithm proposed in this paper can effectively extract information from the features to enhance the performance of the model.Moreover,NRBO-XGBoost achieves an accuracy of 92.09%in transformer fault diagnosis with only 20 convergences compared to other comparative models,demonstrating superior performance.

关 键 词：变压器智能诊断模型 实域粗糙集 超参数寻优 

分 类 号：TM41[电气工程—电器] TN06[电子电信—物理电子学]