The precise localization of False Data Injection Attacks (FDIA) is vital to ensure the stable operation of smart grids. However, the intermittency and uncertainty of renewable energy can lead to confusion with unknown FDIA. As a result, previous works encountered difficulties in extracting distinguishable spatiotemporal features to construct accurate behavior models, thereby affecting the effectiveness of the localization task. To address this challenge, we establish a more practical dataset for FDIA localization that takes renewable energy into account. Subsequently, we propose a spatiotemporal sequence analysis framework for the task. Specifically, we propose a factorized module to mitigate the impact of temporal fluctuations, which processes data sequence with down sampling and feature aggregation. Additionally, we introduce a fine-tuning matrix to take regional correlations of renewable energy into consideration, where the weights of spatial information aggregation are adjusted. We evaluate the effectiveness of our approach through comprehensive case studies on IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus standard test systems. The experimental results indicate that our method outperforms the compared methods by an average of 2.52% and 3% in terms of recall and F1-score, respectively.

中文翻译：

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通过时空网络对可再生能源并网智能电网中的虚假数据注入攻击进行本地化


虚假数据注入攻击（FDIA）的精确定位对于保障智能电网的稳定运行至关重要。然而，可再生能源的间歇性和不确定性可能会导致与未知的 FDIA 的混淆。因此，之前的工作在提取可区分的时空特征以构建准确的行为模型方面遇到了困难，从而影响了定位任务的有效性。为了应对这一挑战，我们为 FDIA 本地化建立了一个更实用的数据集，其中考虑了可再生能源。随后，我们提出了该任务的时空序列分析框架。具体来说，我们提出了一个分解模块来减轻时间波动的影响，该模块通过下采样和特征聚合来处理数据序列。此外，我们引入了一个微调矩阵来考虑可再生能源的区域相关性，其中调整空间信息聚合的权重。我们通过对 IEEE 14 总线、IEEE 57 总线和 IEEE 118 总线标准测试系统的综合案例研究来评估我们方法的有效性。实验结果表明，我们的方法在召回率和 F1 分数方面平均优于对比方法 2.52% 和 3%。