| 引用本文: | 赵若妤,马宏忠,魏 旭,等.基于EWT及多尺度形态谱的高压并联电抗器故障诊断研究[J].电力系统保护与控制,2020,48(17):68-75.[点击复制] |
| ZHAO Ruoyu,MA Hongzhong,WEI Xu,et al.Research on fault diagnosis of a high voltage shunt reactor based on EWT and multiscale spectral spectrum[J].Power System Protection and Control,2020,48(17):68-75[点击复制] |
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| 摘要: |
| 针对高压并联电抗器故障诊断问题，提出一种基于经验小波变换(EWT)、多尺度数学形态谱进行特征提取，采用Kernel K-means聚类进行故障模式识别的诊断新方法。首先，将实测三种工况下的电抗器振动信号经EWT分解得到数个模态分量。然后分别计算每个模态分量与原信号的相关系数并按系数大小降序排列，取前4个模态分量构成有效分量向量。再利用多尺度形态谱对有效分量向量进行分析计算，构成一个四维特征向量。最后利用Kernel K-means聚类对样本特征集进行分类识别。实验验证，该方法能有效提取电抗器振动信号特征量，能正确识别电抗器所属的不同工况。 |
| 关键词: 高压电抗器 特征提取 经验小波变换 形态谱 Kernel K-means聚类 |
| DOI：DOI: 10.19783/j.cnki.pspc.190353 |
| 投稿时间：2019-07-23 |
| 基金项目:国家自然科学基金项目资助(51577050)；国网江苏省电力有限公司2018年重点科技项目资助(J2018014) |
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| Research on fault diagnosis of a high voltage shunt reactor based on EWT and multiscale spectral spectrum |
| ZHAO Ruoyu,MA Hongzhong,WEI Xu,JIANG Ning,CHEN Xuan,TAN Fenglei |
| (1. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;
2. State Grid Jiangsu Electric Power Co., Ltd. Operation and Maintenance Department, Nanjing 210008, China;
3. State Grid Jiangsu Electric Power Co., Ltd. Maintenance Branch Company, Nanjing 211102, China) |
| Abstract: |
| To tackle the problem of fault diagnosis of a high voltage shunt reactor, a new method based on Empirical Wavelet Transform (EWT) and a multi-scale mathematical morphological spectrum for feature extraction is proposed. Kernel K-means clustering is used to diagnose fault pattern recognition. First, the vibration signal of the reactor under the three conditions is decomposed by EWT to obtain several modal components. Then, the correlation coefficients of each modal component and the original signal are calculated separately and arranged in descending order of the coefficient size. The first four modes are taken. The state components constitute the effective component vector; the multi-scale morphological spectrum is used to analyze and calculate the effective component vector to form a four-dimensional eigenvector. Finally, Kernel K-means clustering is used to classify and identify the sample feature set. The experiment proves that the method can effectively extract the characteristic quantity of the vibration signal of the reactor and can correctly identify the different working conditions of the reactor.
This work is supported by National Natural Science Foundation of China (No. 51577050) and Key Science and Technology Project of State Grid Jiangsu Electric Power Company in 2018 (No. J2018014). |
| Key words: high voltage reactor feature extraction empirical wavelet transform morphological spectrum Kernel K-means clustering |
