基于集合经验模态分解法的局部放电信号去噪  

Partial Discharge De-noising based on Ensemble Empirical Mode Decomposition

作　　者：尚海昆[1] 王坤[1] 李峰[2] 

机构地区：[1]东北电力大学电气工程学院,吉林吉林132012 [2]国网新疆电力公司电力科学研究院,新疆830011

出　　处：《东北电力大学学报》2016年第4期32-38,共7页Journal of Northeast Electric Power University

基　　金：东北电力大学博士科研启动基金项目(BSJXM-201406)

摘　　要：针对局部放电检测中存在较多白噪声干扰的问题,采用基于集合经验模态分解的方法对放电信号进行消噪处理。该方法首先利用集合经验模态分解(EEMD)把信号分解成多个经验模态函数分量(IMFs),然后利用3σ法则对各分量进行细节信息提取和能量估计,最后对IMF分量进行PCA变换,并根据IMF所含噪声能量选择主成分分量进行重构。EEMD建立在经验模态分解(EMD)基础之上,通过人为添加白噪声成分,并利用多次重复取均值的方式去除白噪声,同时抑制模态混叠现象。仿真数据分析表明,所提消噪方法可以有效抑制局部放电噪声干扰,成功提取出有效的局部放电信号。To overcome the influence of white noise in partial discharge detection,a novel method based on Ensemble Empirical Mode Decomposition（ EEMD） was proposed for signal de-nosing. By introducing extra noise into the decomposition process,EEMD can effectively separate the original signal into different intrinsic mode functions（ IMFs） with distinctive frequency scales. Signal details in IMFs could be preserved with 3σrule. Principal Component Analysis was then utilized for IMFs and principal components were extracted for reconstruction based on noise energy calculation. On the basis of EMD,EEMD is able to solve mode mixing problems through adding white noise manually. The results on simulated partial discharge signals show that the proposed signal de-noising technique can be effectively used for noise suppression and it can extract partial discharge signals successfully.

关 键 词：EEMD PCA 局部放电 消噪 

分 类 号：TM83[电气工程—高电压与绝缘技术]