基于RFE和SA-SVM的变压器故障诊断

李育恒; 赵 峰

李育恒,赵峰.基于RFE和SA-SVM的变压器故障诊断[J].电测与仪表,2014,51(12):.
Li Yuheng,Zhao Feng.Transformer Fault Diagnosis Based on SVM-RFE Algorithm[J].Electrical Measurement & Instrumentation,2014,51(12):.

基于RFE和SA-SVM的变压器故障诊断

Transformer Fault Diagnosis Based on SVM-RFE Algorithm

DOI：

中文关键词: 特征选择基因选择算法支持向量机故障诊断

英文关键词:Feature selection Recursive feature elimination Support vector machine Fault disgnosis

基金项目:

作者中文名	作者英文名	单位
李育恒	Li Yuheng	兰州交通大学
赵峰	Zhao Feng	兰州交通大学自动化与电气工程学院

摘要点击次数: 1711

中文摘要:

通过对变压器油中溶解气体进行分析,可以及早的发现变压器的故障。为了全面地反映变压器内部故障与特征气体之间的关系,提出采用5中特征气体浓度比值作共计15组作为特征预输入量,并采用基因选择(Recursive Feature Elimination,RFE)算法对15个特征量进行筛选,将筛选后特征量作为支持向量机(Support Vector Machine,SVM)模型输入。在SVM模型中,采用模拟退火(Simulated Annealing,SA)算法对SVM的参数进行优化,给出其GUI界面。最后,通过数据验证基于RFE-SA-SVM模型故障诊断率要高于单一模型。

英文摘要:

Based on the analysis of gases dissovled in transformer oil, it can disscover the fault of transformer.In order to fully reflect the relationship between the internal fault of transformer and the characteristic of gas,it put forward a pre input witch uesd the characteristic of gas of 5 concentration ratio for a total of 15 sets of characteristic and adopted RFE algorithm on the 15 feature selection,using the selected feature as the input for the SVM model.In the SVM model,it optimized the parameters by using the SA algorithm and provided the GUI interface.Finally,the RFE-SA-SVM model fault diagnosis rate is better than that of single model through data validation.

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