| 引用本文: | 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.[点击复制] |
| WANG Nana,LI Wenyi,LI Xiaolong.An oil-immersed transformer fault diagnosis method based on DGA unbalanced limitedsample processing and improved CatBoost[J].Power System Protection and Control,2024,52(23):167-176[点击复制] |
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| 摘要: |
| 针对油中溶解气体分析(dissolved gasses analysis, DGA)数据集小样本及不平衡特性导致故障诊断准确率不高的问题，提出一种布谷鸟搜索优化类别型特征提升算法(cuckoo search-categorical boosting, CS-CatBoost)和改进少数过采样技术(synthetic minority over-sampling technique, SMOTE)的油浸式变压器故障诊断方法。首先，使用中心偏移权重(center offset weight, COW)优化SMOTE增强不均衡故障样本，获得均衡数据集。然后，通过CatBoost构建基于集成学习框架的基分类器，并针对CatBoost模型分类性能受其初始参数影响大、参数选择不正确后易发生过拟合或欠拟合现象，引入CS优化其初始参数，进一步提高其分类性能。实验结果表明，在小样本不均衡条件下所提出的SMOTE-CS-CatBoost模型相比其他方法故障诊断精度明显提升，可准确判别变压器故障信息。 |
| 关键词: 油浸式变压器 故障诊断 平衡数据集 布谷鸟搜索 SMOTE CatBoost |
| DOI：10.19783/j.cnki.pspc.240478 |
| 投稿时间：2024-04-20修订日期：2024-07-13 |
| 基金项目:内蒙古自治区“揭榜挂帅”项目资助(2022 JBGS0043)；内蒙古自治区直属高校基本科研费项目资助(JY20220421) |
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| An oil-immersed transformer fault diagnosis method based on DGA unbalanced limitedsample processing and improved CatBoost |
| WANG Nana1,2,LI Wenyi1,3,LI Xiaolong1 |
| (1. College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010080, China;
2. Inner Mongolia Power (Group) Co., Ltd. Training Center, Hohhot 010010, China; 3. College of Electric Power,
Inner Mongolia University of Technology, Hohhot 010080, China) |
| 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. |
| Key words: oil-immersed transformer fault diagnosis balanced data set cuckoo search SMOTE CatBoost |
