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.

本研究提出一种数据驱动的贝叶斯强化学习 (BRL) 方法来解决大规模电力系统下的电压控制问题，并通过在部分可观测马尔科夫决策问题中自动捕捉因网络攻击所导致的不确定性来保持电压控制持续性，同时评估该技术在WSCC和IEEE 14总线系统上的应用效果，以及自动寻找多种RL技术中探索和开发的阈值。

面向网络攻击不确定性的贝叶斯强化学习自动电压控制