Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control

This paper investigates the use of reinforcement learning in electric power system emergency control. The approach consists of using numerical simulations together with on-policy Monte Carlo control to determine a discrete switching control law to trip generators so as to avoid loss of synchronism. The proposed approach is tested on a model of a real large scale power system and results are compared with a quasioptimal control law designed by a brute force approach for this system.

• Type: Conference
• Year: 2000
• Where: PKDD
• Authors: Christophe Druet, Damien Ernst, Louis Wehenkel
• Keywords: Control Law, Data Mining, Discrete Switching Control, Monte Carlo Control, PKDD 2000