基于EEMD自适应阈值去噪的电能质量扰动检测与定位研究  

Research on Denosing of Power Quality Disturbance Detection and Location Based on EEMD Adaptive Thresholding

作　　者：韩刚[1] 张建文[1] 褚鑫[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《电测与仪表》2014年第2期45-49,57,共6页Electrical Measurement & Instrumentation

摘　　要：为了改善电能质量扰动信号的去噪效果,实现扰动信号的检测与准确定位,提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)自适应阈值的电能质量扰动信号去噪方法。首先利用集合经验模态分解将含噪的扰动信号分解成一些相互独立的固有模态函数(Intrinsic Mode Function,IMF)分量,然后对所得的IMF进行自适应阈值去噪,从而抑制噪声干扰。采用希尔伯特黄变换(HHT)提取去噪后扰动信号的起止时刻、瞬时频率和幅值信息。相比于小波去噪的启发式阈值、自适应阈值、固定阈值、极大极小阈值等方法,该方法在去噪的同时减少了信息损失,信噪比SNR和均方误差MSE均有明显提高。仿真结果验证了该方法在电能质量扰动检测与定位中的有效性和可行性。To improve denoising performance of power quality disturbance （PQD） signals and solve the problem of detection and accurate location for PQD, a new method using ensemble empirical mode decomposition （EEMD） based adaptive thresholding for PQD signals is proposed. First, the PQD signals with noise are decomposed into a number of intrinsic mode function （IMF） components. Then the noise in the components is suppressed through thresholding and reconstructing each intrinsic mode function with adaptive thresholds. The instantaneous attributes and start-stop time of the PQD signals can be extracted with Hilbert-Huang transform（HHT）. Compared with other wavelet thresholding techniques, this method can reduce information loss in denoising, and offer better SNR and MSE. Simulation results verify the correctness and effectiveness of this method for PQD detection.

关 键 词：电能质量扰动 信号消噪 自适应阈值 集合经验模态分解 希尔伯特-黄变换 扰动检测 

分 类 号：TM933[电气工程—电力电子与电力传动]