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文章摘要
基于交叉小波变换的高压并联电抗器故障诊断方法
Fault diagnosis method of high voltage shunt reactor based on cross wavelet transform
Received:July 15, 2019  Revised:July 15, 2019
DOI:10.19753/j.issn.1001-1390.2021.01.007
中文关键词: 高压并联电抗器  交叉小波功率谱  特征频段  RGB参数  故障诊断
英文关键词: high voltage shunt reactor, cross-wavelet spectrum, characteristic frequency band, RGB parameters, fault diagnosis
基金项目:江苏省电力公司重点科技项目(J2018014);国家自然科学基金(51577050)
Author NameAffiliationE-mail
Pan Xincheng* School of Energy and Electrical Engineering, HoHai University, Nanjing 211100, China 1803782304@qq.com 
Ma Hongzhong School of Energy and Electrical Engineering, HoHai University, Nanjing 211100, China hhumhz@163.com 
Chen Xuan Maintenance Branch State of Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China sjchenxuan@js.sgcc.com.cn 
Hao Baoxin Maintenance Branch State of Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China haobaoxin@js.sgcc.com.cn 
Tan Fenglei Maintenance Branch State of Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China 220122094@seu.edu.cn 
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中文摘要:
      为了实现高压并联电抗器智能化诊断,提出一种基于交叉小波变换的电抗器故障诊断方法。搭建振动信号测试平台,采集不同条件下的电抗器振动信号,对比分析了电抗器三种状态下的交叉小波功率谱,通过图谱中颜色、显著性水平曲线与相角变化对电抗器故障进行定性分析;确定了信号的特征频段,提取特征频段中RGB参数与相角信息构造特征矩阵;采用矩阵相识度量化不同特征矩阵间的差异。实验分析结果表明,随着压紧力减弱,D2、D3、D4频段间相关性下降,D6频段间相关性上升。此外,电抗器不同状态下的特征矩阵有明显区别,矩阵相似度指标可准确量化松动前后交叉小波功率谱中的差异。
英文摘要:
      In order to realize intelligent diagnosis of high voltage shunt reactor, a fault diagnosis method based on cross-wavelet transform is proposed in this paper. Firstly, the vibration signal test platform is built to collect the reactor vibration signals under different conditions. The cross-wavelet power spectrum of reactor under three states is compared and analyzed. The reactor faults are qualitatively analyzed by the changes of color, saliency level curve and phase angle in the spectrum. The characteristic frequency band of the signal is determined and the characteristics are extracted. In frequency band, RGB parameters and phase angle information are extracted to construct feature matrices. Finally, matrix acquaintance degree is used to qualify the difference between different feature matrices. With the decrease of compression force, the correlation among D2, D3 and D4 bands decreases, while the correlation between D6 bands increases. In addition, the characteristic matrix of reactor under different states has obvious difference, and the matrix similarity index can quantify the difference in the cross-wavelet spectrum after loosening.
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