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Power systems stability control: Reinforcement learning framework
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DOI：10.1109/TPWRS.2003.821457.png)
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Abstract
En 中文
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.
Keywords:
agent
optimal control
power system control
power system oscillations
reinforcement learning
transient stability
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W
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