An Inertia-Based Data Recovery Scheme for False Data Injection Attack
Jiaqi Ruan, Gaoqi Liang, Junhua Zhao, Jing Qiu, Zhao Yang Dong
Abstract
Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery.