基于Linux多线程的叠加电能质量扰动识别系统设计
针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解（EEMD）方法。先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰动做Hlibert变换,最后由得到的瞬时频率、幅值特性图谱判断扰动类别。采用Linux下多线程编程方法实现聚类模态经验分解算法,仿真结果表明系统达到了对混合电能质量扰动的分离和识别的目的。...
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| Published in | 电测与仪表 Vol. 51; no. 16; pp. 108 - 111 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
哈尔滨理工大学测控技术与通信工程学院,哈尔滨,150080
2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1390 |
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| Abstract | 针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解（EEMD）方法。先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰动做Hlibert变换,最后由得到的瞬时频率、幅值特性图谱判断扰动类别。采用Linux下多线程编程方法实现聚类模态经验分解算法,仿真结果表明系统达到了对混合电能质量扰动的分离和识别的目的。 |
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| AbstractList | 针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解（EEMD）方法。先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰动做Hlibert变换,最后由得到的瞬时频率、幅值特性图谱判断扰动类别。采用Linux下多线程编程方法实现聚类模态经验分解算法,仿真结果表明系统达到了对混合电能质量扰动的分离和识别的目的。 TM933.4; 针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解(EEMD)方法.先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰动做Hlibert变换,最后由得到的瞬时频率、幅值特性图谱判断扰动类别.采用Linux下多线程编程方法实现聚类模态经验分解算法,仿真结果表明系统达到了对混合电能质量扰动的分离和识别的目的. |
| Author | 李冬明 陈杰 任志伟 安媛媛 |
| AuthorAffiliation | 哈尔滨理工大学测控技术与通信工程学院,哈尔滨150080 |
| AuthorAffiliation_xml | – name: 哈尔滨理工大学测控技术与通信工程学院,哈尔滨,150080 |
| Author_FL | AN Yuan-yuan LI Dong-ming CHEN Jie REN Zhi-wei |
| Author_FL_xml | – sequence: 1 fullname: LI Dong-ming – sequence: 2 fullname: CHEN Jie – sequence: 3 fullname: REN Zhi-wei – sequence: 4 fullname: AN Yuan-yuan |
| Author_xml | – sequence: 1 fullname: 李冬明 陈杰 任志伟 安媛媛 |
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| ClassificationCodes | TM933.4 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitleAlternate | Design of Superposed Power Quality Disturbance Identification System Based on Linux Multithreading |
| DocumentTitle_FL | Design of Superposed Power Quality Disturbance Identification System Based on Linux Multithreading |
| EndPage | 111 |
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| Keywords | 电能质量 IMF embedded multi-threaded hybrid 嵌入式多线程 power quality EEMD 在线监测 混合 online monitoring |
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| Notes | LI Dong-ming, CHEN Jie, REN Zhi-wei, AN Yuan-yuan （Institute of Measurement and Control Technology and Communications Engineering, Harbin University of Science And Technology, Harbin 150080, China） 23-1202/TH The paper presents an ensemble empirical mode decomposition （EEMD） method for the separation and identification of hybrid power quality disturbances. First, disturbance separation is performed for mixed power quality disturbance signal added with white Gaussian noise. Then the Hilbert transform. Finally, the obtained instantaneous separated single disturbances are processed with frequency and amplitude characteristic map are used to analyze disturbance type. The EEMD algorithm is implemented using muhithreaded programming under Linux. Simulation results show that the system achieves the purpose of separation and identification for hybrid power Quality disturbances. power quality, EEMD, embedded multi-threaded, online monitoring, hybrid, IMF |
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| PublicationTitle | 电测与仪表 |
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| PublicationYear | 2014 |
| Publisher | 哈尔滨理工大学测控技术与通信工程学院,哈尔滨,150080 |
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| Snippet | 针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解（EEMD）方法。先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰动... TM933.4; 针对混合电能质量扰动分离和识别问题,提出了一种聚类模态经验分解(EEMD)方法.先对添加高斯白噪声的混合电能质量扰动信号进行扰动分离,再对分离出来的几种单一扰... |
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| SubjectTerms | EEMD IMF 在线监测 嵌入式多线程 混合 电能质量 |
| Title | 基于Linux多线程的叠加电能质量扰动识别系统设计 |
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