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New developments in the application of automatic learning to power system control
Wehenkel, Louis; Glavic, Mevludin; Ernst, Damien
2005In Proceedings of the 15th Power System Computation Conference (PSCC 2005)
Peer reviewed
 

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Keywords :
optimal control; sequential decision making; Monte-Carlo methods; supervised learning; reinforcement learning; anytime algorithms
Abstract :
[en] 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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Glavic, Mevludin 
Ernst, Damien  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
New developments in the application of automatic learning to power system control
Publication date :
2005
Event name :
15th Power System Computation Conference (PSCC 2005)
Event place :
Liège, Belgium
Event date :
22-26 August 2005
Audience :
International
Main work title :
Proceedings of the 15th Power System Computation Conference (PSCC 2005)
Peer reviewed :
Peer reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Available on ORBi :
since 27 May 2009

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