基于稀疏分段压缩感知电能质量重构研究  

Research on power quality reconstruction based on sparse segmented compression perception

作　　者：张瀚文 刘玥 许多 ZHANG Hanwen;LIU Yue;XU Duo(Department of Electrical Engineering,Zhengzhou Electric Power College,Zhengzhou 450000,China;Modo Institute of Technology,Henan University of Science and Technology,Luoyang 471000,China)

机构地区：[1]郑州电力高等专科学校电力工程学院,河南郑州450000 [2]河南科技大学莫动理工学院,河南洛阳471000

出　　处：《中原工学院学报》2024年第4期1-7,17,共8页Journal of Zhongyuan University of Technology

基　　金：河南省科技厅科技攻关项目(24210224019);郑州电力高等专科学校校级科研项目(ZEPCKY2023-05)。

摘　　要：随着智能电网和电力物联网的发展,为了提高电能质量数据压缩采样的效率,利用压缩感知理论对电能质量数据进行压缩重构。在研究过程中发现虽然压缩感知理论能够减少数据的冗余性,但重构过程中的测量矩阵较大,占用过多存储空间和计算时间。研究利用电能质量数据的周期性和冗余性将电能质量数据看作一列矩阵,均匀切分为大小相同的列,同时采用模式相似性检测筛选对分段电能质量数据进行分类,依据数据与标准正弦函数之间的关系分为:标准电能质量和畸变电能质量,对相应的分段筛选矩阵分别构造随机高斯矩阵,以降低测量矩阵的大小和数量,一定程度上解决重构算法计算复杂、时间过长的问题,提高压缩感知重构电能质量数据算法的执行速度。With the development of smart grids and the electric internet of things,compressed sensing theory is used to compress and reconstruct power quality data to improve the efficiency of its compressed sampling.During the research process,it was found that although compressive sensing theory can reduce data redundancy,the measurement matrix used in the reconstruction process is too large,which occupies more storage space and computational time.The study utilizes the periodicity and redundancy of power quality data to treat it as a column matrix,evenly dividing it into columns of the same size.Meanwhile the experiment adopts pattern similarity detection and screening to classify segmented power quality data into standardize power quality and distort power quality based on the relationship between the data and the standard sine function.A random Gaussian matrix is constructed for the corresponding segmented screening matrix to reduce the size and quantity of the measurement matrix,which to some extent solves the problem of complex computation and long time in reconstruction algorithms and improves the execution speed of compressed sensing reconstruction algorithms.

关 键 词：电能质量数据 压缩感知 稀疏分段 压缩采样 

分 类 号：TM711[电气工程—电力系统及自动化]