[1]刘晨斐,崔昊杨,李 鑫,等.不对称样本下基于支持向量机的变压器故障诊断[J].高压电器,2019,55(07):216-220.[doi:10.13296/j.1001-1609.hva.2019.07.031]
 LIU Chenfei,CUI Haoyang,LI Xin,et al.Transformers Fault Diagnosis Based on SVM for Unbalanced Data[J].High Voltage Apparatus,2019,55(07):216-220.[doi:10.13296/j.1001-1609.hva.2019.07.031]
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不对称样本下基于支持向量机的变压器故障诊断()
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《高压电器》[ISSN:1001-1609/CN:61-11271/TM]

卷:
第55卷
期数:
2019年07期
页码:
216-220
栏目:
技术讨论
出版日期:
2019-07-15

文章信息/Info

Title:
Transformers Fault Diagnosis Based on SVM for Unbalanced Data
作者:
刘晨斐 崔昊杨 李 鑫 束 江 李 亚
(上海电力学院电子与信息工程学院, 上海 200090)
Author(s):
LIU Chenfei CUI Haoyang LI Xin SHU Jiang LI Ya
(College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China)
关键词:
故障诊断 支持向量机 不对称样本 上采样
Keywords:
fault diagnosis SVM unbalanced data over sampling
DOI:
10.13296/j.1001-1609.hva.2019.07.031
摘要:
为解决基于支持向量机(SVM)的变压器故障诊断中因样本不对称导致诊断准确率降低的问题,提出了一种改进的向上采样策略和SVM结合的方法。首先通过K-近邻算法提取少数类样本数据中的边界数据集并生成新的少数类随机样本,在此基础上向少数类样本中添加人工生成的随机新样本使得两类样本数量达到基本均衡。对比均衡样本和不对称样本下的SVM分类模型的性能,结果表明:该方法能够有效降低SVM分类平面的偏移程度,进一步提高了SVM变压器故障诊断的准确率。
Abstract:
In the process of power transformer fault diagnosis based on support vector machine (SVM), unbalanced data makes the classifying hyper plane of SVM shift to the minority samples, which decreases the diagnostic accuracy. This paper proposed a method combining over sampling strategy and SVM to solve the problem. K- nearest neighbor algorithm is used to extract the boundary data sets from the minority samples. On this basis, the new random samples are generated and added into the minority samples to make the two kinds of samples balanced consequently. Comparing the performance of SVM classification model with balanced data sets and unbalanced data sets, the experiment results show that the proposed method can reduce the deviation of SVM classifying hyper plane effectively.

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备注/Memo

备注/Memo:
刘晨斐(1991—),男,硕士研究生,研究领域为电力设备状态监测与故障诊断。 崔昊杨(1978—),男,教授,博士,从事电力设备状态检测研究(通讯作者)。 收稿日期:2018-11-21; 修回日期:2019-01-25 基金项目:国家自然科学基金资助项目(61107081,61401269);上海市地方能力建设项目资助课题(15110500900, 14110500900)。 Project Supported by National Natural Science Foundation of China(61107081,61401269),Shanghai Local Colleges and Universities Capacity Building Program(15110500900,14110500900).
更新日期/Last Update: 2019-07-15