基于CD-BSMOTE的D-S证据融合变压器故障诊断
CD-BSMOTE Based D-S Evidence Fusion Transformer Fault Diagnosis
鲁玲 1高诚 1熊威 2龚康 2马辉 1张鑫1
作者信息
- 1. 三峡大学电气与新能源学院,湖北 宜昌 443002
- 2. 国网宜昌供电公司,湖北 宜昌 443000
- 折叠
摘要
针对变压器油中溶解气体数据集不均衡特性对故障诊断结果的影响,提出一种基于清除临界点改进的边界合成少数类过采样算法均衡数据集和Pearson冲突距离改进 D-S证据融合的变压器故障诊断模型.首先,对少数类样本进行均衡化处理,根据 K-means 聚类结果清除处于临界位置的样本;其次,搭建梯度提升树、随机森林、BP神经网络的故障诊断模型,实现变压器故障初步诊断;接着引入 Pearson冲突距离改进 D-S证据融合模型,实现诊断结果的融合决策;最后,经实际算例分析,诊断精确率达到 92.65%.结果表明,所建模型能有效解决数据不平衡对诊断结果的影响,提高故障诊断精度.
Abstract
Aiming at the solving the unbalanced characteristics of the dissolved gas data set in transformer oil on the fault diagnosis results,a transformer fault diagnosis model is proposed based on the fusion of critical removal improved boundary synthesis minority class oversampling algorithm equalized data set and Pearson conflict distance improved D-S evidence.Firstly,the minority class samples are equalized,and the samples in critical position are removed according to K-means clustering results.Secondly,the fault diagnosis model of gradient boost decision tree,random forest,and BP neural network is built to realize the preliminary diagnosis of transformer faults.Then,Pearson conflict distance is ap-plied to improve the D-S evidence fusion model to realize the fusion decision of preliminary diagnosis results.Finally,af-ter analyzing the cases,the precision rate of the diagnosis results reached 92.65%.The results show that the proposed model can effectively eliminate the influence of data imbalance on the diagnostic results and improve the fault diagnosis precision.
关键词
故障诊断/油中溶解气体分析/边界合成少数类过采样/Pearson冲突距离/D-S证据融合Key words
fault diagnosis/dissolved gas analysis/borderline synthetic minority over-sampling technique/Pearson conflict distance/D-S evidence fusion引用本文复制引用
基金项目
国家自然科学基金(52377191)
国家电网湖北省电力公司管理科技项目(5215H0220002)
出版年
2024
NETL
NSTL
万方数据