基于压缩感知的智能电网高级量测体系  

Advanced Metering Infrastructure Based on Compressed Sensing in Smart Grid

作　　者：袁博 葛少云 刘洪 冯喜春 魏孟举 YUAN Bo;GE Shaoyun;LIU Hong;FENG Xichun;WEI Mengju(School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China;Economic and Technology Research Institute of State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050021,China)

机构地区：[1]天津大学电气自动化与信息工程学院,天津300072 [2]国网河北省电力有限公司经济技术研究院,石家庄050021

出　　处：《高电压技术》2024年第5期2088-2096,共9页High Voltage Engineering

摘　　要：针对高级量测体系中的海量数据问题,首次引入压缩感知以克服传统数据压缩方法的不足,深入探索了基于压缩感知的高级量测体系(advanced metering infrastructure based on compressed sensing,AMI-CS)。首先,在分析各类数据特点的基础上,提出了基于时间和基于空间的2种基本模型及其选取原则;然后,设计模型中的关键要素,提出分类K-SVD稀疏基和适用于时间模型的优选重构算法,并设置二进稀疏测量方式、通用重构算法及适用采集参数;基于此,形成了AMI-CS具体构建方案。实验结果表明,所提出的AMI-CS方案关键要素均具合理性,优于CS传统要素且较传统压缩提升了抗丢包性,通过合理选择压缩比,数据重构信噪比在58 dB以上、重构误差在0.24%以下,满足AMI要求。To solve massive data issues for advanced metering infrastructure(AMI),this paper introduces a compressed sensing(CS)technique to overcome the deficiencies of traditional data compression method and explore the advanced metering infrastructure based on compressed sensing(AMI-CS).Firstly,on the basis of analyzing the characteristics of various types of AMI data,this paper proposes two basic models based on time and based on space as well as their selection principles.Then,the key elements of the construction scheme are designed,a classified K-SVD sparse basis and a optimal reconstruction algorithm are proposed.Binary sparse matrix is set as measurement way while reconstructing algorithm and acquisition parameters are set.A specific construction scheme for AMI-CS is formulated and the experimental results show that key elements in scheme are all reasonable.By choosing compression ratio reasonably,the data reconstruction signal-to-noise ratio is above 58 dB and the reconstruction error is below 0.24%,which meet requirements of AMI.

关 键 词：压缩感知 高级量测体系 基本模型 具体构建方案 分类K-SVD稀疏基 重构算法 

分 类 号：TM76[电气工程—电力系统及自动化]