| 引用本文: | 胡明,郭健鹏,李富强,刘建华.基于自适应CEEMD方法的电能质量扰动检测与分析[J].电力系统保护与控制,2018,46(21):103-110.[点击复制] |
| HU Ming,GUO Jianpeng,LI Fuqiang,LIU Jianhua.Power quality disturbance detection and analysis based on adaptively complementary ensemble empirical mode decomposition method[J].Power System Protection and Control,2018,46(21):103-110[点击复制] |
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| 摘要: |
| 针对现有时频分析方法处理非线性、非稳态信号自适应性的不足，提出了一种自适应互补集总经验模态分解(ACEEMD)方法。该方法通过对加噪辅助分解方法噪声准则的研究，引入相关均方根误差与信噪比两个参数作为加噪评价指标，自适应确定最优加噪幅值和集总分解次数。且加入的噪声以正负成对的形式加到目标信号中，克服了原始分解方法存在的模态混叠问题、端点效应以及残余噪声大的缺点。最后将改进的方法与Hilbert变换相结合运用在电能质量扰动检测中，通过仿真实验验证所提方法既可以有效提取扰动的频率、幅值等特征参数，也可以准确定位扰动的时间，为电能质量检测与分析提供了一种新思路。 |
| 关键词: 电能质量扰动 互补集总经验模态分解 加噪参数优化 自适应性 特征提取 |
| DOI：10.7667/PSPC171573 |
| 投稿时间：2017-10-25修订日期：2018-01-15 |
| 基金项目:江苏省高校自然科学基金(15KJB510033);江苏省青蓝工程资助课题 |
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| Power quality disturbance detection and analysis based on adaptively complementary ensemble empirical mode decomposition method |
| HU Ming,GUO Jianpeng,LI Fuqiang,LIU Jianhua |
| (Xuhai College of China University of Mining and Technology, Xuzhou 221008, China) |
| Abstract: |
| In order to deal with the adaptive shortcoming that the existing time-frequency analysis methods are not insufficient in processing non-linear and non-stationary signal, an Adaptively Complementary Ensemble Empirical Mode Decomposition (ACEEMD) method is proposed. In the proposed method, through the study of the added-noise principle of noise assisted decomposition method, where the two parameters of the relative RMSE and the signal-to-noise are introduced as added-noise evaluation indexes to adaptively determine the optimal amplitude of added-noise and the number of ensemble trials respectively. And the additive noise are added in the form of positive and negative pairs to the targeted signal, which overcomes the disadvantages of the mode mixing problem, the endpoint effect and the large residual noise existing in original decomposition method. Finally, the modified method combined with Hilbert transform is utilized in the power quality disturbance detection. Simulation experiments verify that the developed method is not only able to effectively extract disturbance frequency, amplitude and other characteristic parameters, but also accurately locate disturbances time, which provides a novel method for power quality detection and analysis. This work is supported by Natural Science Foundation of Jiangsu Universities (No. 15KJB510033). |
| Key words: power quality disturbance CEEMD additive noise parameter optimum adaptability feature extraction |
