计算机工程与应用2019，Vol.55Issue(16)：255-258,264,5.DOI:10.3778/j.issn.1002-8331.1805-0046
基于低秩模型的电力状态数据异常检测
Low-Rank Representation for Outliers Detection in Power State Estimation
摘要
Abstract
Smart grid plays a critical role in national production. The stability and security of smart grid is essentially impor-tant. As a result, it is significant to detect the bad and malicious data from daily observations. This paper proposes a novel outlier detection method based on low-rank representation. Specifically, the observation is decomposed into two parts:a low-rank part for clean data and a sparse part for outliers. In addition, this paper deploys ALM(Augmented Lagrange Mul-tiplier)to optimize the objective. Extensive experiments on two popular benchmarks verify the advantages of the pro-posed method.关键词
低秩表示/数据挖掘/网络空间安全/电力状态估计Key words
low-rank representation/ data mining/ cyberspace security/ power state estimation分类
信息技术与安全科学引用本文复制引用
李永攀,门锟,吴俊阳..基于低秩模型的电力状态数据异常检测[J].计算机工程与应用,2019,55(16):255-258,264,5.基金项目
中国南方电网有限责任公司项目(No.0002200000031727). （No.0002200000031727）
