| 引用本文: | 丁晓慧,刘俊杰,邢强.基于自适应局部均值的EMD方法及其在谐波检测中的应用[J].电力系统保护与控制,2017,45(14):17-25.[点击复制] |
| DING Xiaohui,LIU Junjie,XING Qiang.Adaptive local mean based empirical mode decomposition and its application in harmonic detections[J].Power System Protection and Control,2017,45(14):17-25[点击复制] |
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| 摘要: |
| 针对经验模态分解(EMD)方法处理非线性非稳态信号的不足，提出了一种自适应局部均值的EMD分解方法(ALMEMD)。该方法分析了信号极值点分布特性和均值曲线拟合对分解结果的影响，只使用一次样条插值拟合局部均值曲线，且运用积分中值定理利用全部零极值点间的数据作为局部特征时间尺度。针对信号多分量特点，确定了采用高阶极值的局部均值曲线筛选低频分量、低阶局部均值曲线筛选高频分量的优势，并以正交系数作为评价指标自适应选择最优极值阶次。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可行性和有效性，而且该方法适用于谐波检测与分析。 |
| 关键词: 经验模态分解 局部均值 极值点分布 参数优化 谐波检测 |
| DOI：10.7667/PSPC161087 |
| 投稿时间：2016-07-16修订日期：2016-09-08 |
| 基金项目:国家自然科学基金项目(61374043) |
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| Adaptive local mean based empirical mode decomposition and its application in harmonic detections |
| DING Xiaohui,LIU Junjie,XING Qiang |
| (Huaibei Branch of China Unicom, Huaibei 235000, China;School of Electronic and Information Engineering, Shangqiu College, Shangqiu 476000, China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China) |
| Abstract: |
| In order to deal with the shortcoming that Empirical Mode Decomposition (EMD) is not insufficient in processing non-linear and non-stationary signals, an adaptive local mean EMD (ALMEMD) method is proposed in this paper. In the proposed method, the influence of how the distribution characteristics of signal extrema as well as the mean curve fitting affect decomposition results is analyzed, and only one spline interpolation is utilized to construct local mean curves, in which all of the data located between the zeros and extrema are employed as local characteristic time-scale by using mean value theorem for integrals. Furthermore, aiming at signal multicomponent feature, the advantages that low-frequency components and high-frequency components are respectively sifted out by local mean curves derived by higher-order extrema and low-order extrema, and orthogonality coefficient is taken as an evaluation index to adaptively select the optimal extreme order. Both simulation test and experiments data measured from the built power quality disturbance platform demonstrate the feasibility and effectiveness of the proposed method, which is suitable for applying in detection and analysis of harmonics as well. This work is supported by National Natural Science Foundation of China (No. 61374043). |
| Key words: EMD local mean distribution of extreme points parameter optimum harmonic detection |
