...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Entropy >Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine
【24h】

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

机译：基于多尺度色散熵和超球面多类支持向量机的局部放电故障诊断

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.
机译：局部放电（PD）故障分析是电气设备绝缘状况诊断的重要工具。为了克服传统PD故障诊断的局限性，提出了一种基于变分模式分解（VMD）和多尺度色散熵（MDE）的特征提取方法。此外，将超球面多类支持向量机（HMSVM）用于具有提取的PD特征的PD模式识别。首先，将原始PD信号用VMD分解以获得固有模式函数（IMF）。其次，根据中心频率观测值选择适当的IMF，并计算每个IMF中的MDE值。然后引入主成分分析（PCA）来提取MDE中的有效主成分。最后，将提取的主因子用作PD特征，并发送到HMSVM分类器。实验结果表明，基于VMD-MDE的局部放电特征提取方法可以有效地提取代表局部局部放电特征的有效特征参数。识别结果验证了所提出的PD故障诊断方法的有效性和优越性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号