摘要
针对直流微电网中虚假数据注入攻击(false data injection attack, FDIA)隐蔽性强、难以精准检测的问题,提出一种基于深度时空特征学习的FDIA检测方法。首先,构建并行双分支检测模型。一支引入Transformer模块,利用自注意力机制提取全局信息与跨节点特征;另一支引入门控循环单元(gated recurrent unit, GRU),捕捉量测数据中的时间依赖性与动态演化模式。其次,通过特征尺度对齐与自适应加权实现空间与时间表征的特征级融合,并配合归一化与残差抑制冗余与噪声。然后,将融合后的特征输入神经网络分类器,实现对多类型攻击的一体化检测。最后,在典型直流微电网场景下构建多类型攻击数据集并开展对比实验。结果表明,该方法各项指标整体优于对比模型,且表现出较强的鲁棒性与泛化能力。
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.
作者
王义
罗胜耀
唐靓
李忠文
张世达
WANG Yi;LUO Shengyao;TANG Liang;LI Zhongwen;ZHANG Shida(School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Henan Provincial Engineering Technology Research Center of Power Electronics and Energy Systems,Zhengzhou 450001,China;State Grid Zhenjiang Power Supply Company,Zhenjiang 212000,China)
出处
《电力系统保护与控制》
北大核心
2026年第6期94-103,共10页
Power System Protection and Control
基金
河南省自然科学基金项目资助(242300421167)
国家自然科学基金项目资助(62203395)
中国博士后科学基金特别资助(2023TQ0306)
河南省青年人才托举工程项目资助(2025HYTP028)
中原科技创新青年拔尖人才项目资助。
