Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid

作者：Xu, Ruzhi; Wang, Rui; Guan, Zhitao^*; Wu, Longfei; Wu, Jun; Du, Xiaojiang

来源：IEEE Access, 2017, 5: 13787-13798.

DOI：10.1109/ACCESS.2017.2728681

• Smart grid
• state estimation
• false data injection attack
• control signal
• node voltage stability index

Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively.

• 出版日期2017
• 单位华北电力大学（保定）; 上海交通大学; 华北电力大学