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 selec-tion 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 al-gorithm 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 require-ments of AMI.
袁博;葛少云;刘洪;冯喜春;魏孟举
天津大学电气自动化与信息工程学院,天津 300072||国网河北省电力有限公司经济技术研究院,石家庄 050021天津大学电气自动化与信息工程学院,天津 300072国网河北省电力有限公司经济技术研究院,石家庄 050021
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
compressed sensing;advanced metering infrastructure;basic model;specific construction scheme;catego-rized K-SVD sparse base;reconstruction algorithm
《高电压技术》 2024 (005)
2088-2096 / 9
10.13336/j.1003-6520.hve.20221227
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