Accurate Detection of False Data Injection Attacks in Renewable Power Systems Using Deep Learning

Abstract

The rapid development of technology in the past decades created a society heavily dependent on electricity, where even short disturbances in the power supply can result in grave socio-economic consequences. Therefore, assuring a safe and reliable operation of the power system has become of utmost importance. False data injection attacks (FDIAs) represent a class of cyber-attacks targeting the power system state estimation. FDIAs alter the perspective of the power system’s state which can lead to inappropriate control actions. Thus, a reliable method for detecting FDIAs represents the main prerequisite to the safe operation of the power system in the context of cybersecurity. Noticing the scarce literature analyzing the detection of FDIAs in power systems with a high share of renewable energy sources, this paper demonstrates that the performance of the existing methods deteriorates when faced with the volatile nature of renewable energy sources. This paper presents a deep learning approach for detecting stealthy FDIAs concerning the power systems with high penetration of renewable energy sources. The performance of the proposed method is validated through different scenarios based on the modified versions of the IEEE 14-bus system and the IEEE 118-bus system. The proposed method is able to detect most of the attacks under different test scenarios, outperforming the benchmark techniques with an average detection rate of 99% for the IEEE 14-bus system and 97% for the IEEE 118-bus system.

Keywords

• False data injection attacks
• cyber-attack detection
• state estimation
• renewable energy sources
• deep learning