电力系统保护与控制2026，Vol.54Issue(6)：94-103,10.DOI:10.19783/j.cnki.pspc.250872
基于深度时空特征学习的直流微电网虚假数据注入检测方法
A false data injection detection method for DC microgrids based on deep spatiotemporal feature learning
摘要
Abstract
To address the strong stealthiness and low detectability of false data injection attacks(FDIAs)in DC microgrids,this paper proposes a FDIA detection method based on deep spatiotemporal feature learning.First,a parallel dual-branch detection model is constructed.One branch incorporates a Transformer module to extract global information and cross-node features through a self-attention mechanism,while the other branch adopts a gated recurrent unit(GRU)to capture temporal dependencies and dynamic evolution patterns in measurement data.Second,feature-scale alignment and adaptive weighting are employed to achieve feature-level fusion of spatial and temporal representations,supplemented by normalization and residual mechanisms to suppress redundancy and noise.Then,the fused features are fed into a neural network classifier to enable unified detection of multiple types of FDIA.Finally,a multi-type attack dataset is constructed under typical DC microgrid scenarios,and comparative experiments are conducted.The results demonstrate that the proposed method outperforms baseline models across overall evaluation metrics and exhibits strong robustness and generalization capability.关键词
直流微电网/虚假数据注入攻击/攻击检测/深度学习/Transformer-GRUKey words
DC microgrid/false data injection attack/attack detection/deep learning/Transformer-GRU引用本文复制引用
王义,罗胜耀,唐靓,李忠文,张世达..基于深度时空特征学习的直流微电网虚假数据注入检测方法[J].电力系统保护与控制,2026,54(6):94-103,10.基金项目
This work is supported by the Natural Science Foundation of Henan Province(No.242300421167). 河南省自然科学基金项目资助(242300421167) （No.242300421167）
国家自然科学基金项目资助(62203395) （62203395）
中国博士后科学基金特别资助(2023TQ0306) （2023TQ0306）
河南省青年人才托举工程项目资助(2025HYTP028) （2025HYTP028）
中原科技创新青年拔尖人才项目资助 （）
