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Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertainty

arXiv 2305.16469 ↗First posted 25 May 2023cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

Voltage control is crucial to large-scale power system reliable operation, as timely reactive power support can help prevent widespread outages. However, there is currently no built in mechanism for power systems to ensure that the voltage control objective to maintain reliable operation will survive or sustain the uncertainty caused under adversary presence. Hence, this work introduces a Bayesian Reinforcement Learning (BRL) approach for power system control problems, with focus on sustained voltage control under uncertainty in a cyber-adversarial environment. This work proposes a data-driven BRL-based approach for automatic voltage control by formulating and solving a Partially-Observable Markov Decision Problem (POMDP), where the states are partially observable due to cyber intrusions. The techniques are evaluated on the WSCC and IEEE 14 bus systems. Additionally, BRL techniques assist in automatically finding a threshold for exploration and exploitation in various RL techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 25 May 2023.

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References · 13

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  4. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
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  6. 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Gridscited by 24
  7. 2018Bayesian Reinforcement Learning in Factored POMDPscited by 15
  8. 2020Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defensecited by 15
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  10. 2020Adversarial Training for a Continuous Robustness Control Problem in Power Systemscited by 8
  11. 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 8
  12. 2021Multi-Source Data Fusion for Cyberattack Detection in Power Systemscited by 4
1 more
  1. 2020Design and Evaluation of A Cyber-Physical Resilient Power System Testbedcited by 3

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