Study of Learning of Power Grid Defense Strategy in Adversarial Stage Game

Abstract

Security of electric power transmission and distribution systems is currently one of the most challenging issues due to rising concerns regarding increased cyber-attacks in the energy sector. In the smart electric power transmission and distribution system, cyber-attackers are capable of causing large-scale damage (including blackout). In response to these attacks in the energy sector, different machine learning based game theory approaches are used to mimic the complex interactions between adversaries (the attacker and defender) in a smart electric power system. Most of the existing works fail to replicate the real-time interactions by verifying the criticality of the identified contingencies or by reflecting the attack impacts on the power system. In this paper, we identify the critical contingencies of an electric power transmission and distribution system adopting an adversarial stage game with value iteration. We adjust the defense strategy from attacker’s learned attack action (eventually reduces the generation loss) and provide alternative action choices in case of limited access to the system. Then, we analyze the impact of the learned attack policies in a simulated power system using the PowerWorld simulator in two case studies. All the experiments are conducted on two standard power system test cases (W & W 6 bus system and IEEE 39 bus system). The effectiveness of the learned policy is verified by adjusting the defender’s policy according to the attacker’s learned policy. The simulation results successfully prove the efficiency of the proposed research in learning critical contingencies, providing defense strategies, and replicating the attack impacts on power systems.