New developments in the application of automatic learning to power system control

In this paper we present the basic principles of supervised learning and reinforcement learning as two complementary frameworks to design control laws or decision policies within the context of power system control. We also review recent developments in the realm of automatic learning methods and discuss their applicability to power system decision and control problems. Simulation results illustrating the potentials of the recently introduced fitted Q iteration learning algorithm in controlling a TCSC device aimed to damp electro-mechanical oscillations in a synthetic 4-machine system, are included in the paper.