%0 Journal Article
%T 基于压缩感知的微网谐波分析方法<br>Harmonic Analysis Method in Microgrid Based on Compressed Sensing
%A 杨
%A 挺
%A 徐明玉
%A 袁
%A 博
%J 天津大学学报（自然科学与工程技术版）
%D 2016
%R 10.11784/tdxbz201409036
%X 多类型分布式电源和电力电子装置的运用使微网中谐波分量更加复杂，针对目前 Nyquist 采样框架下前段 谐波采样数据量大、压缩复杂度高的问题，基于压缩感知理论提出了一种微网谐波分析和同步检测方法．首先理论 证明了电网谐波信号在 DFT 基下的稀疏性满足压缩感知必备条件；随后依据国标要求，选定采用稀疏测量压缩采样 方法和谱投影梯度恢复算法的压缩感知过程，使采样端存储空间降低为传统 Nyquist 采样的 M/N，且降低传统稠密 测量复杂度．实验结果表明：谱线插值修正算法可有效提升检测精度，新方法对微网谐波的频率、幅值和相位的检 测误差分别降低到 0.000 , 1%,、0.053%,和 0.05°以内，对间谐波的检测误差分别在 0.002%,、0.15%,和 0.2°以内<br>Distributed generations and power electronic devices make the harmonic environment of microgrid more and more complex. To solve the shortages of large volumes of stored data and high complexity of compression in sam- pling side with the framework of the Nyquist sampling theory，the compressed sensing techniques were integrated into harmonic analysis in this paper and then a novel method of microgrid harmonic analysis and synchronous detec- tion was proposed. Firstly，it was theoretically proven that the sparsity of the microgrid harmonic signals in the base of DFT，which met the necessary conditions of compressed sensing technology. Then，to satisfy the Chinese stan- dard，the sparse measurement and the spectral projected gradient algorithm were used in the processes of compressing and sensing. The new method reduced the storage space of sampling side to original M/N，and decreased the sampling complexity compared with the traditional dense measurement. The experimental results show that the spectrum inter- polation correction algorithm can effectively improve the detecting accuracy，with which the detecting errors of mi- crogrid harmonic’s frequency，amplitude and phase have been reduced to lower than 0.000,1%,，0.053%,，and 0.05° respectively；the detecting error of simple harmonic’s frequency，amplitude and phase are respectively within 0.002%,，0.15 %, and 0.2°，respectively
%K 压缩感知
%K 谐波分析方法
%K 谱线插值修正
%K 稀疏测量&lt
%K br&gt
%K compressed sensing
%K harmonic analysis method
%K spectral line interpolation correction
%K sparse measurement
%U http://journals.tju.edu.cn/zrb/oa/darticle.aspx?type=view&id=2016050006