FACTS devices controlled by means of reinforcement learning algorithms

Reinforcement learning consists of a collection of methods for approximating solutions to deterministic and stochastic optimal control problems of unknown dynamics. These methods learn by experience how to adjust a closed-loop control rule which is a mapping from the system states to control actions. This paper proposes an application of reinforcement learning methods to the control of a FACTS device aimed to damp power system oscillations. A detailed case study is carried out on a synthetic four-machine power system.