Power Systems Stability Control : Reinforcement Learning Framework

Damien, Ernst , Mevludin Glavic and Louis Wehenkel
Power Systems Stability Control : Reinforcement Learning Framework
IEEE transactions on Power Systems

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 on-line mode in which the interaction occurs with the real power system and the off-line mode in which the interaction occurs with a simulation model of the real power system. We present two case studies made on a 4-machine power system model. The first one concerns the design by means of RL algorithms used in off-line mode of a dynamic brake controller. The second concerns RL methods used in on-line mode when applied to control a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.