英语翻译Power Systems Stability Control:Reinforcement Learning FrameworkAbstract—In this paper,we explore how a computational approachto learning from interactions,called reinforcement learning(RL),can be applied to control power systems.We des

英语翻译Power Systems Stability Control:Reinforcement Learning FrameworkAbstract—In this paper,we explore how a computational approachto learning from interactions,called reinforcement learning(RL),can be applied to control power systems.We des
英语翻译
Power Systems Stability Control:
Reinforcement Learning Framework
Abstract—In this paper,we explore how a computational approach
to learning from interactions,called reinforcement learning
(RL),can be applied to control power systems.We describe some
challenges in power system control and discuss how some of those
challenges could be met by using these RL methods.The difficulties
associated with their application to control power systems are
described and discussed as well as strategies that can be adopted
to overcome them.Two reinforcement learning modes are considered:
the online mode in which the interaction occurs with the real
power system and the offline mode in which the interaction occurs
with a simulation model of the real power system.We present two
case studies made on a four-machine power system model.The first
one concerns the design by means of RL algorithms used in offline
mode of a dynamic brake controller.The second concerns RL
methods used in online mode when applied to control a thyristor
controlled series capacitor (TCSC) aimed to damp power system
oscillations.

英语翻译Power Systems Stability Control:Reinforcement Learning FrameworkAbstract—In this paper,we explore how a computational approachto learning from interactions,called reinforcement learning(RL),can be applied to control power systems.We des
电力系统稳定控制
强化学习框架
摘要：本论文探讨了如何把强化学习（RL）这种互动学习计算方法用于控制电力系统.我们介绍了电力系统控制中存在的挑战,并探讨了如何利用强化学习法加以应对.本论文也描述了利用强化学习法控制电力系统时存在的困难和应对策略.这里探讨了两种强化学习模式：一是与真实电力系统进行互动的在线模式；二是与真实电力系统的仿真模型进行互动的离线模式.我们介绍了涉及四机电力系统模型的两个案例：第一个案例是将强化学习计算法用于动态闸控制器的离线模式；第二个案例将强化学习法用于在线模式,对晶闸管控制的串联电容器进行控制,目的是为了减少电力系统振荡.