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文章摘要
基于加窗插值压缩感知的谐波/间谐波检测方法
Harmonic/interharmonic detection method based on compressed sensing with windowed interpolation
Received:September 30, 2020  Revised:October 17, 2020
DOI:10.19753/j.issn1001-1390.2024.03.024
中文关键词: 谐波  间谐波  窗稀疏测量  压缩感知  SACoSaMP
英文关键词: harmonics, interharmonics, window sparse measurement, compressed sensing, SACoSaMP
基金项目:国家自然科学基金资助项目( 51777057);河北省教育厅资助科研 项目(ZD2016108)
Author NameAffiliationE-mail
DU Taihang School of Artificial Intelligence, Hebei University of Technology
State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology 
thdu@hebut.edu.cn 
LIANG Qianwei School of Artificial Intelligence, Hebei University of Technology
State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology 
17332339201@163.com 
SUN Shuguang* School of Artificial Intelligence, Hebei University of Technology
State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology 
sunshuguang_2000@163.com 
WANG Jingqin School of Artificial Intelligence, Hebei University of Technology
State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology 
jqwang@hebut.edu.cn 
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中文摘要:
      针对压缩感知在傅里叶变换基下,对谐波畸变信号进行变换时会产生频谱泄漏和栅栏效应的问题,提出一种基于加窗插值的压缩感知(WI-CS)算法。该算法结合二进制稀疏测量矩阵和六项五阶余弦窗,构造出一种新型窗稀疏测量(WSM)矩阵,经重构得到稀疏向量后,利用四谱线插值修正公式,对谐波间谐波各参数进行精确检测。通过MATLAB仿真,结果表明文章所提方法能够在较少的采样数据下,具有较高的检测效率以及一定的抗噪声能力。
英文摘要:
      Aiming at the problem of spectrum leakage and fence effect when transforming harmonic distortion signals with compressed sensing based on DFT base, a windowed and interpolated compressed sensing (WI-CS) algorithm is proposed. The algorithm combines the binary sparse measurement matrix and the six-term fifth-order cosine window to construct a new window sparse measurement (WSM) matrix. After the sparse vector is reconstructed, the quadratic spectral line interpolation is used to accurately detect the parameters of harmonics and interharmonics. Through MATLAB simulation, the results show that the proposed method can have higher detection efficiency and certain anti-noise ability under the condition of less sampling data.
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