The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. Finally, the research proposes a DDIA localization method based on spatio-temporal graph neural networks. The accuracy and effectiveness of the DDIA model are rigorously demonstrated through comprehensive analytical cases.

翻译：新型虚假数据注入攻击的出现对电力系统的安全稳定运行构成严重威胁。由于注入的恶意数据与合法数据之间欧氏空间距离极小，使得传统基于距离的方法难以精确检测，此类攻击尤为危险。此外，现有研究主要集中于各类机器学习技术，通常分析攻击后的时序数据序列或仅依赖欧氏空间特征。遗憾的是，这种思路往往忽视了电网数据非欧氏空间属性中固有的拓扑关联性，从而导致攻击定位精度下降。为解决此问题，本研究采取综合性方法：首先从攻击者视角出发，深入剖析新型虚假数据注入攻击在电力系统中的基本原理，考虑不完全拓扑知识与交流状态估计条件下，构建了复杂的虚假数据注入攻击数学模型；进而通过融合电网拓扑先验知识，考虑量测数据的时间关联性与电网的拓扑依赖属性，引入时间与空间注意力矩阵自适应捕获攻击中的时空关联性；利用门控堆叠因果卷积与图小波稀疏卷积联合提取攻击的时空特征；最终提出基于时空图神经网络的虚假数据注入攻击定位方法。通过全面的算例分析，严格验证了该虚假数据注入攻击模型的准确性与有效性。