电机与控制应用2023，Vol.50Issue(12)：21-31,11.DOI:10.12177/emca.2023.146
基于样本集成学习和SO-SVM的变压器故障诊断
Transformer Fault Diagnosis Based on Sample Integration Learning and SO-SVM
摘要
Abstract
Aiming at the problem of low accuracy of classification model caused by the unbalanced category of transformer fault samples,a transformer fault diagnosis model based on sample integration learning and snake optimisation algorithm(SO)optimised support vector machine(SVM)is proposed.The model first uses the EasyEnsemble sampler to generate multiple subsets with balanced categories after multiple under-sampling of the samples;then the SVM model optimised by SO with key parameters is trained with the Bagging strategy,and the final fault types are obtained by integrating the results of the classifiers.The validity of the proposed model is verified by the arithmetic example,and the data show that the diagnostic accuracy of SO-SVM's fault diagnosis is improved by 3.44％,6.89％,10.92％,and the AUC value is improved by 0.026 4,0.042 5,and 0.081 2,respectively,compared with the models of RF,SVM and KNN;in the same classifier,the SO-SVM model is more accurate than the SMOTE and ADASYN sample balancing methods,the diagnostic accuracy is improved by 4.59％and 2.87％,respectively,indicating that the SO-SVM model has better fault diagnosis capability for unbalanced samples.关键词
变压器/样本集成学习/故障诊断/蛇优化算法Key words
transformer/sample ensemble learning/fault diagnosis/snake optimization algorithm分类
动力与电气工程引用本文复制引用
刘可真,姚岳,赵现平,杨春昊,盛戈皞,王科..基于样本集成学习和SO-SVM的变压器故障诊断[J].电机与控制应用,2023,50(12):21-31,11.基金项目
云南省教育厅科学研究基金资助项目(2022J1279) （2022J1279）
云南电网有限责任公司科技项目(YNKJXM20180736)Funded by the Scientific Research Fund of Yunnan Provincial Department of Education(2022J1279) （YNKJXM20180736）
Yunnan Power Grid Co.,Ltd.,Science and Technology Project(YNKJXM20180736) （YNKJXM20180736）
