False Data Injection Attacks Detection in Smart Grid: A Structural Sparse Matrix Separation Method

Smart grid has become the trend of future power system owing to its efficient allocation of resources, real-time monitoring and decision-making ability. However, due to the deep integration of power physical system and information system, smart grid is under serious threat such as malicious attacks and so on. The false data injection attack (FDIA), which can circumvent the traditional bad data detection technology, is a practical significant yet challenging problem for the operation of smart grid. In this paper, a novel attack detection method based on the matrix separation theory was proposed, which can detect the FDIA efficiently by exploring the low-rank feature of the unattacked measurement matrix and the structural sparsity feature of the attack matrix. Furthermore, a structural sparse matrix separation algorithm is proposed to improve the attack identification accuracy. In order to verify the effectiveness of the proposed method, we carried out experiments under three types of attack. Compared with the existing methods, the proposed method has significantly improved the performance of FDIA detection. At the same time, it also has good robustness for noise and good scalability in large networks.