Two-tier data-driven intrusion detection for automatic generation control in smart grid

Abstract

Legacy energy infrastructures are being replaced by modern smart grids. Smart grids provide bi-directional communications for the purpose of efficient energy and load management. In addition, energy generation is adjusted based on the load feedback. However, due to the dependency on the cyber infrastructure for load monitoring and reporting, generation control is inherently vulnerable to attacks. Recent studies have shown that the possibility of data integrity attacks on the generation control can significantly disrupt the energy system. In this work, we present simple yet effective data-driven two-tier intrusion detection system for automatic generation control (AGC). The first tier is a short-term adaptive predictor for system variables, such as load and area control error (ACE). The first tier provides a real-time measurement predictor that adapts to the underlying changing behavior of these system variables, and flags out the abnormal behavior in these variables independently. The second tier provides deep state inspection to investigate the presence of anomalies by incorporating the overall system variable correlation using Markov models. Moreover, we expand our second tier inspection to include multi-AGC environment where a behavior of one AGC is validated against the behavior of the interconnected AGC. The combination of tier-1 light-weight prediction and tier-2 offline deep state inspection offers a great advantage to balance accuracy and real-time requirements of intrusion detection for AGC environment. Our results show high detection accuracy (95%) under different multi-attack scenarios. Second tier successfully verified all the injected intrusions.