Automatic learning of sequential decision strategies for dynamic security assessment and control

Wehenkel, Louis; Glavic, Mevludin; Geurts, Pierre; Ernst, Damien

doi:10.1109/PES.2006.1708874

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Automatic learning of sequential decision strategies for dynamic security assessment and control

Wehenkel, Louis; Glavic, Mevludin; Geurts, Pierre et al.

2006 • In Proceedings of the IEEE Power Engineering Society General Meeting 2006

Peer reviewed

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https://hdl.handle.net/2268/13448

DOI
10.1109/PES.2006.1708874

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Keywords :

optimal control; sequential decision making; dynamic security assessment and control; automatic learning

Abstract :

[en] This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or information collected from real-life system measurements. The exploitation of these methods for the design of decision making strategies and control policies in the context of preventive and emergency mode dynamic security assessment and control is discussed and further opportunities for research in this area are highlighted.

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

Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Ernst, Damien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Language :

English

Title :

Automatic learning of sequential decision strategies for dynamic security assessment and control

Publication date :

2006

Event name :

IEEE Power Engineering Society General Meeting 2006

Event place :

Montréal, Canada

Event date :

2006

Audience :

International

Main work title :

Proceedings of the IEEE Power Engineering Society General Meeting 2006

ISBN/EAN :

1-4244-0493-2

Peer reviewed :

Peer reviewed

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]

Available on ORBi :

since 28 May 2009

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Bibliography

L. Wehenkel, "Emergency control and its strategies," in Proceedings of the 13th Power Systems Computation Conference, PSCC99, June 1999, pp. 35-48.
T. E. DyLiacco, "The adaptive reliability control system," IEEE Transactions on Power Apparatus and Systems, vol. 86, no. 5, pp. 517-531, 1967.
_, "Control of power systems via the multi-level concept," Ph.D. dissertation, Case Western Reserve University, Systems Research Center, 1968.
S. Henry, J. Pompée, J. Paul, M.Béna, and K. Bell, "New trends for the assessment of power system security under uncertainty," in Proceedings of Bulk Power System Dynamics and Control - VI, IREP04, 2004, pp. 679-684.
J. Huang, S. Harrison, G. Vanier, A. Valette, and L. Wehenkel, "Application of data mining techniques for settings of generator tripping and load shedding system in emergency control at hydro-québec," in Proceedings IEEE PES Winter Meeting, 2003, p. 6.
D. Bertsekas, Dynamic Programming and Optimal Control, 2nd ed. Belmont, MA: Athena Scientific, 2000, vol. I.
R. Marceau and J. Endreyeni, "Power system security assessment," Electra, November 1997.
L. Wehenkel, Automatic Learning Techniques in Power Systems. Kluwer Academic, 1998.
L. Kaelbling, M. Littman, and A. Moore, "Reinforcement Learning: a Survey," Journal of Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.
R. Sutton and A. Barto, Reinforcement Learning. An Introduction. MIT Press, 1998.
D. Ernst, P. Geurts, and L. Wehenkel, "Iteratively extending time horizon reinforcement learning," in Proc. of the 14th European Conference on Machine Learning, Dubrovnik, Croatia, September 2003.
_, "Tree-based batch mode reinforcement learning," Journal of achine Learning Research, vol. 6, pp. 503-556, April 2005.
D. Ormoneit and S. Sen, "Kernel-Based Reinforcement Learning," Machine Learning, vol. 49, no. 2-3, pp. 161-178, 2002.
P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. (to appear), pp. 1-39, 2006.
D. Ernst, "Near optimal closed-loop control. Application to electric power systems," Ph.D. dissertation, University of Liège, Belgium, March 2003.
D. Ernst, M. Glavic, and L. Wehenkel, "Power system stability control: reinforcement learning framework," IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 427-435, February 2004.
D. Ernst and L. Wehenkel, "FACTS devices controlled by means of reinforcement learning algorithms," in Proc. of PSCC'2002, Sevilla, Spain, June 2002.
M. Glavic, D. Ernst, and L. Wehenkel, "Combining a stability and a performance oriented control in power systems," IEEE Trans. on Power Systems, vol. 20, no. 1, 2005.
D. Ernst, M. Glavic, P. Geurts, and L. Wehenkel, "Approximate value iteration in the reinforcement learning context. Application to electrical power system control," International Journal of Emerging Electric Power Systems, vol. 3, pp. Issue 1, Article 1066, 2005, 30 pages.