Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter

作者：Guoqing Zhang, Wengen Gao, Yunfei Li, Xinxin Guo, Pengfei Hu, Jiaming Zhu · 发表于：Energies · 年份：2023 · DOI：10.3390/en16207203 · 被引用次数：21 · 研究领域：Smart Grid Security and Resilience、Network Security and Intrusion Detection、Electricity Theft Detection Techniques

An accurate power state is the basis of the normal functioning of the smart grid. However, false data injection attacks (FDIAs) take advantage of the vulnerability in the bad data detection mechanism of the power system to manipulate the process of state estimation. By attacking the measurements, then affecting the estimated state, FDIAs have become a serious hidden danger that affects the security and stable operation of the power system. To address the bad data detection vulnerability, in this paper, a false data attack detection method based on weighted least squares (WLS) and an adaptive interpolation extended Kalman filter (AIEKF) is proposed. On the basis of applying WLS and AIEKF, the Euclidean distance is used to calculate the deviation values of the two-state estimations to determine whether the current moment is subjected to a false data injection attack in the power system. Extensive experiments were conducted to simulate an IEEE-14-bus power system, showing that the adaptive interpolation extended Kalman filter can compensate for the deficiency in the bad data detection mechanism and successfully detect FDIAs.