A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response
<|reference_start|>A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response: Numerous research studies have been conducted to enhance the resilience of cyber-physical systems (CPSs) by detecting potential cyber or physical disturbances. However, the development of scalable and optimal response measures under power system contingency based on fusing cyber-physical data is still in an early stage. To address this research gap, this paper introduces a power system response engine based on reinforcement learning (RL) and role and interaction discovery (RID) techniques. RL-RID-GridResponder is designed to automatically detect the contingency and assist with the decision-making process to ensure optimal power system operation. The RL-RID-GridResponder learns via an RL-based structure and achieves enhanced scalability by integrating an RID module with reduced action and state spaces. The applicability of RL-RID-GridResponder in providing scalable and optimal responses for CPSs is demonstrated on power systems in the context of Denial of Service (DoS) attacks. Moreover, simulations are conducted on a Volt-Var regulation problem using the augmented WSCC 9-bus and augmented IEEE 24-bus systems based on fused cyber and physical data sets. The results show that the proposed RL-RID-GridResponder can provide fast and accurate responses to ensure optimal power system operation under DoS and can extend to other system contingencies such as line outages and loss of loads.<|reference_end|>
@article{sun2024a,
    title={A Reinforcement Learning Engine with Reduced Action and State Space for
  Scalable Cyber-Physical Optimal Response},
    author={Shining Sun, Khandaker Akramul Haque, Xiang Huo, Leen Al Homoud,
  Shamina Hossain-McKenzie, Ana Goulart, Katherine Davis},
    journal={arXiv preprint arXiv:2410.04518},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04518},
    primaryClass={eess.SY cs.SY}
}