The Power system is a crucial Cyber-Physical system and is prone to the False Data Injection Attack (FDIA). The existing FDIA detection mechanism focuses on DC state estimation. In this paper, we propose a phased AC FDIA targeting at generation rescheduling and load shedding. After injecting the false data into the measurements, the estimated states will be deviated from those in normal conditions. The proposed mechanism extracts the spatial and spectral features of the modes decomposed from the estimated states using variational mode decomposition (VMD). Then LSTM-Autoencoder is trained by learning the temporal correlations between the multi-dimensional feature vectors. The reconstruction error deviation vectors of the feature vectors are calculated and updated by LSTM-Autoencoder. Based on these error deviation vectors, the Logistic Regression (LR) classifier is trained to determine whether the error deviation vector is abnormal. We evaluate the performance of the proposed mechanism with comprehensive simulations on IEEE 14 and 118-bus systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy.

Power 系统是一个关键的网络物理系统，容易受到虚假数据注入攻击 (FDIA)。现有的 FDIA 检测机制侧重于 DC 状态估计。在本文中，我们提出了一种针对发电重新调度和减载的分阶段 AC FDIA。在将错误数据注入测量值后，估计的状态将与正常情况下的状态有所偏差。所提出的机制使用变分模式分解（VMD）提取从估计状态分解的模式的空间和光谱特征。然后通过学习多维特征向量之间的时间相关性来训练 LSTM-Autoencoder。特征向量的重构误差偏差向量由 LSTM-Autoencoder 计算和更新。基于这些误差偏差向量，训练逻辑回归（LR）分类器以确定误差偏差向量是否异常。我们通过对 IEEE 14 和 118 总线系统的综合模拟来评估所提出机制的性能。结果表明，该机制可以达到令人满意的攻击检测精度。