电工技术学报  2021, Vol. 36 Issue (zk2): 467-476    DOI: 10.19595/j.cnki.1000-6753.tces.L90178
电机与电器 |
基于小波变换与逻辑斯蒂回归的混合式配电变压器故障辨识
张立石1,2, 梁得亮1,2, 刘桦1,2, 柳轶彬1,2, 李大伟1,2
1.西安交通大学电力设备与电气绝缘国家重点实验室 西安 710049;
2.西安交通大学陕西省智能电网重点实验室 西安 710049
Fault Identification of Hybrid Distribution Transformer Based on Wavelet Transform and Logistic Regression
Zhang Lishi1,2, Liang Deliang1,2, Liu Hua1,2, Liu Yibin1,2, Li Dawei1,2
1. State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China;
2. Shaanxi Key Laboratory of Smart Grid Xi’an Jiaotong University Xi’an 710049 China
全文: PDF (4783 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 混合式配电变压器(HDT)在智能配电网中能够代替传统配电变压器实现无功功率补偿、谐波治理和电压调节等功能。为了区分当HDT发生故障的时候,是变压器内部故障还是电力电子故障,该文首先通过仿真得到HDT不同故障工况下的大量一次侧、二次侧、三次侧、四次侧电流特征量数据,然后借助小波变换理论,对得到的数据进行四层离散小波变换,从中抽取小波域下数据的归一化能量、归一化能量矩和样本熵作为电流特征量数据的特征值。利用机器学习的方法,构建逻辑斯蒂回归分类器,将特征值组成的特征矩阵作为分类器的输入、训练模型,得到受试者工作特征(ROC)曲线和混淆矩阵表现很好的分类器模型。最后多次随机抽取数据,统计训练得到的机器学习模型的HDT故障识别的准确率均在90%左右。
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
张立石
梁得亮
刘桦
柳轶彬
李大伟
关键词 混合式配电变压器小波变换机器学习故障辨识    
Abstract:Hybrid distribution transformer (HDT) can replace traditional distribution transformers in intelligent distribution networks to achieve reactive power compensation, harmonic control, voltage regulation and other functions. In order to distinguish between the internal fault of the transformer and the power electronic fault when the HDT fails, this paper first obtains a large amount of current characteristic data of primary side, secondary side, tertiary side and quaternary side under different fault conditions of HDT through simulation. Then, with the help of wavelet transform theory, four-layer discrete wavelet transform is performed on the obtained data and the normalized energy, the normalized energy moment and sample entropy of the data in the wavelet domain are used as the eigenvalues of the current characteristic data. Using machine learning technology, a logistic regression classifier is constructed, and the feature matrix composed of feature values is used as the input of the classifier, and the model is trained to obtain a classifier model with good receiver operating characteristic (ROC) curve and confusion matrix performance. Finally, the data was randomly extracted many times, and the accuracy of the HDT fault recognition of the machine learning model obtained by training was about 90%.
Key wordsHybrid distribution transformer (HDT)    wavelet transform    machine learning    fault identification   
收稿日期: 2020-06-30     
PACS: TM421  
基金资助:陕西省2018年重点研发计划资助项目(2018ZDCXL-GY-07-05)
通讯作者: 梁得亮 男,1967年生,教授,博士生导师,研究方向为智能配网系统协同设计及冗余高效控制。E-mail:dlliang@mail.xjtu.edu.cn   
作者简介: 张立石 男,1996年生,博士研究生,研究方向为智能变压器控制及其状态感知。E-mail:zhangls@stu.xjtu.edu.cn
引用本文:   
张立石, 梁得亮, 刘桦, 柳轶彬, 李大伟. 基于小波变换与逻辑斯蒂回归的混合式配电变压器故障辨识[J]. 电工技术学报, 2021, 36(zk2): 467-476. Zhang Lishi, Liang Deliang, Liu Hua, Liu Yibin, Li Dawei. Fault Identification of Hybrid Distribution Transformer Based on Wavelet Transform and Logistic Regression. Transactions of China Electrotechnical Society, 2021, 36(zk2): 467-476.
链接本文:  
https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.L90178          https://dgjsxb.ces-transaction.com/CN/Y2021/V36/Izk2/467