南方电网技术2025，Vol.19Issue(7)：62-71,89,11.DOI:10.13648/j.cnki.issn1674-0629.2025.07.005
基于端到端自监督时序对比学习与异常合成的实时智能电表异常检测框架
Real-Time Smart Meter Abnormality Detection Framework via End-to-End Self-Supervised Time-Series Contrastive Learning with Anomaly Synthesis
摘要
Abstract
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes real-time detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.关键词
异常检测/物理信息安全/异常合成/对比学习/时间序列Key words
abnormality detection/cyber-physical security/anomaly synthesis/contrastive learning/time-series分类
信息技术与安全科学引用本文复制引用
王亿鑫,梁高琪,毕霁超,赵俊华..基于端到端自监督时序对比学习与异常合成的实时智能电表异常检测框架[J].南方电网技术,2025,19(7):62-71,89,11.基金项目
深圳市自然科学基金稳定支持面上项目(GXWD20231128112434001) （GXWD20231128112434001）
浙江省自然科学基金探索青年项目(LQ24F030015).Supported by the Stable Support General Project of Shenzhen Natural Science Fund(GXWD20231128112434001) （LQ24F030015）
Zhejiang Provincial Natural Science Foundation of China under Grant(LQ24F030015). （LQ24F030015）
