电力系统保护与控制2024，Vol.52Issue(23)：167-176,10.DOI:10.19783/j.cnki.pspc.240478
基于不均衡小样本DGA数据与改进CatBoost决策树的油浸式变压器故障诊断方法
An oil-immersed transformer fault diagnosis method based on DGA unbalanced limited sample processing and improved CatBoost
摘要
Abstract
To solve the issue of poor fault diagnosis accuracy induced by unbalanced and limited dissolved gas analysis(DGA)samples of oil-immersed transformer faults,the method based on cuckoo search optimized categorical boosting algorithm(CS-CatBoost)and improved synthetic minority over-sampling technique(SMOTE)is proposed.First,the center offset weight(COW)is used to optimize the SMOTE and enhance unbalanced fault samples,obtaining a balanced dataset.Then,a base classifier based on an ensemble learning framework is constructed using CatBoost.The classification performance of the CatBoost model is significantly influenced by its initial parameters or may select incorrect parameters,thereby leading to overfitting or underfitting.Thus CS is introduced to optimize its initial parameters,further enhancing its classification performance.Experimental results demonstrate that under conditions of small sample size and imbalance,the proposed SMOTE-CS-CatBoost model significantly improves fault diagnosis accuracy compared to other methods,accurately identifying transformer fault information.关键词
油浸式变压器/故障诊断/平衡数据集/布谷鸟搜索/SMOTE/CatBoostKey words
oil-immersed transformer/fault diagnosis/balanced data set/cuckoo search/SMOTE/CatBoost引用本文复制引用
王娜娜,栗文义,李小龙..基于不均衡小样本DGA数据与改进CatBoost决策树的油浸式变压器故障诊断方法[J].电力系统保护与控制,2024,52(23):167-176,10.基金项目
This work is supported by the Science and Technology Innovation Project of Inner Mongolia Autonomous Region(No.2022JBGS0043). 内蒙古自治区"揭榜挂帅"项目资助(2022 JBGS0043) （No.2022JBGS0043）
内蒙古自治区直属高校基本科研费项目资助(JY20220421) （JY20220421）
