Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids
Xiaoyuan Luo, Yating Li, Xinyu Wang, Xinping Guan
Abstract
The cyber security of large-scale smart grid against false data injection attack (FDIA) is concerned in this article. FDIA can modify the sensor data and make internal states cause bias without being detected by the bad data detection system. We propose a method for FDIA detection and localization in the smart grid in this article. First, a series of interval observers are designed by considering the bounds of internal states, modeling errors, and disturbances to estimate the interval states of the grid physical system. By using the interval residuals of interval observers, a detection scheme against FDIA is proposed. For FDIA localization, the measurement data of the corresponding sensor is used as the input of the interval observer. Therefore, each interval observer is responsible for FDIA detection and localization of the corresponding sensor. Furthermore, the logic localization judgment matrix is constructed for localizing the sensor in which FDIA is injected. Then, the detection and localization scheme against FDIA is proposed based on the interval observer and the logic localization judgment matrix. Finally, simulations on the IEEE 36-bus grid are performed to illustrate the effectiveness of the proposed interval observer-based FDIA detection and localization algorithm.