论文标题
通过稳定性和安全保证的分布式瞬态频率控制的强化学习
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees
论文作者
论文摘要
本文提出了一种基于加强学习的方法，可在具有稳定性和安全保证的功率系统中进行最佳的瞬态频率控制。在Lyapunov稳定理论和安全关键控制的基础上，我们在分布式控制器设计上得出了足够的条件，以确保闭环系统的稳定性和瞬态频率安全性。我们对分布式动态预算分配的想法使这些条件的保守性不如最近的文献，因此它们可以对控制政策的搜索空间施加更严格的限制。我们构建神经网络控制器，该神经网络控制器参数化此类控制策略，并使用加强学习来训练最佳的控制策略。 IEEE 39-BUS网络上的仿真说明了控制器的确保稳定性和安全性，并显着改善了最优性。
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.
