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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Bibliographic Details
Published in电测与仪表 Vol. 51; no. 16; pp. 108 - 111
Main Author 李冬明 陈杰 任志伟 安媛媛
Format Journal Article
LanguageChinese
Published 哈尔滨理工大学测控技术与通信工程学院,哈尔滨,150080 2014
Subjects
EEMD
IMF
在线监测
嵌入式多线程
混合
电能质量
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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ISSN1001-1390

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Summary: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。
Bibliography: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
ISSN:1001-1390