This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stability theory and safety-critical control, we derive sufficient conditions on the distributed controller design that ensure the stability and transient frequency safety of the closed-loop system. Our idea of distributed dynamic budget assignment makes these conditions less conservative than those in recent literature, so that they can impose less stringent restrictions on the search space of control policies. We construct neural network controllers that parameterize such control policies and use reinforcement learning to train an optimal one. Simulations on the IEEE 39-bus network illustrate the guaranteed stability and safety properties of the controller along with its significantly improved optimality.

中文翻译：

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分布式瞬态频率控制的强化学习，稳定性和安全性有保证

本文提出了一种基于强化学习的方法，用于在保证稳定性和安全性的情况下实现电力系统的最佳暂态频率控制。基于李亚普诺夫稳定性理论和安全关键控制，我们推导了分布式控制器设计的充分条件，以确保闭环系统的稳定性和瞬态频率安全。我们的分布式动态预算分配的想法使这些条件比最近文献中的条件不那么保守，因此它们可以对控制策略的搜索空间施加不太严格的限制。我们构建了参数化此类控制策略的神经网络控制器，并使用强化学习来训练最佳策略。对 IEEE 39 总线网络的仿真说明了控制器的稳定性和安全性得到保证，并且优化性显着提高。