Article (Scientific journals)
Approximate value iteration in the reinforcement learning context. Application to electrical power system control
Ernst, Damien; Glavic, Mevludin; Geurts, Pierre et al.
2005In International Journal of Emerging Electrical Power Systems, 3 (1)
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Keywords :
reinforcement learning; power system control; electrical power oscillations damping; TCSC control; approximate value iteration
Abstract :
[en] In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations.
Disciplines :
Computer science
Electrical & electronics engineering
Author, co-author :
Ernst, Damien  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Glavic, Mevludin 
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Approximate value iteration in the reinforcement learning context. Application to electrical power system control
Publication date :
2005
Journal title :
International Journal of Emerging Electrical Power Systems
ISSN :
1553-779X
Publisher :
Berkeley Electronic Press
Volume :
3
Issue :
1
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
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