| 刘天正,李春雨,王志明,孙颀皓,卜雄洙.基于经验小波变换与相空间重构的故障电弧检测[J].火灾科学,2024,33(4):263-270. |
| 基于经验小波变换与相空间重构的故障电弧检测 |
| Fault arc detection based on empirical wavelet transform and phase space reconstruction |
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| DOI：10.3969/j.issn.1004-5309.2024.04.07 |
| 基金项目:国家重点研发计划项目(2021YFC1523500) |
| 作者 | 单位 | | 刘天正 | 1.南京理工大学机械工程学院,南京,210094 | | 李春雨 | 2.南京理工大学电子工程与光电技术学院,南京,210094 | | 王志明 | 1.南京理工大学机械工程学院,南京,210094 | | 孙颀皓 | 1.南京理工大学机械工程学院,南京,210094 | | 卜雄洙 | 1.南京理工大学机械工程学院,南京,210094 |
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| 中文关键词: 经验小波变换 维度重构 故障电弧 |
| 英文关键词:Empirical wavelet transform Phase space reconstruction Faulty arcs |
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| 中文摘要: |
| 针对交流回路中串联故障电弧检测的困难问题,根据国标搭建试验平台采集四种典型负载的电流波形,使用经验小波变换分解提取特征分量。为了拓展单一特征分量中的故障电弧特征,引入相空间重构技术将一维特征分量嵌入到三维空间中,最后制成数据集输入到CNN-LSTM网络中进行训练,结果表明该方法可以准确地识别出故障电弧,识别准确率最高可达98.7%。结合通用工具型地理信息系统软件MHMapGIS,探析了将故障电弧识别技术传输到机器视觉监控系统,实现网格型可视化预警。 |
| 英文摘要: |
| Given the difficulty of detecting series arc faults in AC circuits, a test platform is set up according to the national standard to collect the current waveforms of four typical loads, and the feature components are extracted using empirical wavelet decomposition. Phase space reconstruction is introduced to embed the one-dimensional feature component into the three-dimensional space to expand the fault arc features in a single feature component. Finally, the data set is made into a CNN-LSTM network for training. The results show that the method can accurately recognize the fault arcs, and the highest recognition accuracy can reach up to 98.7%. Combined with the general tool-based geographic information system software MHMapGIS, this paper explores the transmission of fault arc recognition technology to machine vision monitoring systems, to achieve grid-based visual warning. |
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