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基于小波包变换-主元分析-神经网络算法的多电平逆变器故障诊断
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  • 英文篇名:Multi-level inverter fault diagnosis based on WPT-PCA-NN algorithm
  • 作者:宋保业 ; 徐继伟 ; 许琳
  • 英文作者:SONG Baoye;XU Jiwei;XU Lin;College of Electrical Engineering and Automation,Shandong University of Science and Technology;
  • 关键词:多电平逆变器 ; 小波包变换 ; 主元分析 ; 神经网络 ; 故障诊断
  • 英文关键词:multi-level inverter;;wavelet packet transform;;principal component analysis;;neural network;;fault diagnosis
  • 中文刊名:SDKY
  • 英文刊名:Journal of Shandong University of Science and Technology(Natural Science)
  • 机构:山东科技大学电气与自动化工程学院;
  • 出版日期:2019-01-23 14:27
  • 出版单位:山东科技大学学报(自然科学版)
  • 年:2019
  • 期:v.38;No.180
  • 基金:国家自然科学基金项目(61703242);; 山东省高等学校科技计划项目(J14LN34)
  • 语种:中文;
  • 页:SDKY201901013
  • 页数:10
  • CN:01
  • ISSN:37-1357/N
  • 分类号:115-124
摘要
针对级联多电平逆变器的故障诊断问题,提出了一种基于小波包变换-主元分析-神经网络算法的故障诊断方法。首先,通过小波包变换对故障信号进行多尺度小波包分解,并重构提取小波包能量作为故障特征向量;然后,采用主元分析方法提取主元信息以降低故障特征的维数;最后,利用BP神经网络进行故障分类和识别。通过仿真实验,验证了该方法相对于传统方法具有更高的故障诊断和故障识别的准确率,并且对噪声具有较好的鲁棒性。
        To handle the fault diagnosis of cascaded multi-level inverters,a fault diagnosis based on WPT-PCA-NN algorithm was proposed in this paper.Firstly,the wavelet packet transform(WPT)was applied to the multi-scale wavelet packet decomposition of fault signals,and the fault feature vector was extracted from the reconstructed wavelet packet energy.Then,the principal component analysis(PCA)was employed to extract the principal information to reduce the dimensions of fault features.Finally,the BP neural network(NN)was used for fault diagnosis and identification.The simulation results demonstrate that the proposed method is more accurate in fault diagnosis and identification in comparison with traditional methods,and is more robust to noisy fault signals.
引文
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