采用改进互补集总经验模态分解的电能质量扰动检测方法

发布时间：2018-04-14 14:32

本文选题：集总经验模态分解(EEMD) + 自适应快速互补EEMD(AFCEEMD)　；参考：《浙江大学学报(工学版)》2017年09期

【摘要】：针对集总经验模态分解(EEMD)方法加噪参数(噪声幅值、集总次数)需人为确定、分解残余噪声大以及计算耗时长的缺点,提出一种自适应快速互补集总经验模态分解(AFCEEMD)方法.该方法分析不同频率形式噪声对极值点分布的影响,确定加噪频率采用高频辅助分解的优势,并以极值点分布特性作为评价指标自适应选择最优加噪频率.通过对EEMD加噪准则的研究,推导出加噪幅值和分解次数采取固定值:0.01SD和2次,且以正负成对的形式加入到原始信号中.通过仿真实验和搭建的电能质量扰动平台的实测数据验证了所提方法的自适应性和计算性能,而且适用于电能质量扰动检测与分析.
[Abstract]:Aiming at the disadvantages of adding noise parameters (noise amplitude, lumped number) in the lumped empirical mode decomposition (EMD) method, which requires artificial determination, large residual noise and long computation time, an adaptive and fast complementary lumped empirical mode decomposition (AFCEEMD) method is proposed.In this method, the influence of noise in different frequency forms on the distribution of extreme points is analyzed, and the advantages of high frequency auxiliary decomposition are determined. The characteristics of extreme point distribution are used as the evaluation index to adaptively select the optimal noise adding frequency.Based on the study of the EEMD noise adding criterion, it is deduced that the noise adding amplitude and decomposition times are fixed value: 0.01SD and 2 times, and are added to the original signal in the form of positive and negative pairs.The self-adaptability and computational performance of the proposed method are verified by simulation experiments and measured data of the power quality disturbance platform, and are suitable for power quality disturbance detection and analysis.
【作者单位】：中国矿业大学信息与电气工程学院;
【基金】：国家自然科学基金资助项目(61374043) 教育部中央高校基本科研业务费专项资金资助项目(2013QNA50)
【分类号】：TM930

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