reinforcement learning; power systems; transient stability
Abstract :
[en] 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 quasi-optimal control law designed by a brute force approach for this system.
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Bibliography
J.A. Boyan and M.L. Littman. Packet routing in dynamically changing networks: A reinforcement learning approach. In J.D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems, volume 6. Morgan Kaufmann, San Francisco CA, 1993.
T. Dy Liacco. Security functions in power system control centers. In IFAC Symp. on Power System Control, New Delhi, 1979.
D. Ernst, A. Bettiol, Y. Zhang, L. Wehenkel, and M. Pavella. Real time transient stability emergency control of the south-southeast brazilian system. SEPOPE, Salvador, Brazil, May 1998.
D. Ernst and C. Druet. Design of an optimal control law for emergency transient stability, 2000. Internal report.
D. Ernst and M. Pavella. Closed-loop transient stability emergency control. Pre-sented at the On-line Transient Stability Assessment and Control, panel session at the IEEE/PES Winter Meeting 2000, Singapore, 2000.
P. Kundur and G.K. Morison. Techniques for emergency control of power systems and their implementation. In Proceedings of the IFAC-CIGRE Symp. on Control of Power Plants and Power Systems, Beijing, pages 679-684, 1997.
R.S. Sutton and A.G. Barto. Reinforcement Learning, an introduction. The MIT Press, 1998.
G.J. Tesauro. Td-gammon, a self-teaching backgammon program, achieves master-level play. In Proceedings of the AAAI Fall Symp. on Games: Planning and Lear-ning, pages 19-23. AAAI Press Technical Report FS93-02, 1993.
S.B. Thrun. Eficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Shool of Computer Science, Carnegie-Mellon University, Pitts-burgh, Pennsylvania 15213-3890, January 1992.
A. Vallette, F. Lafrance, S. Lefebvre, and L. Radakovitz. ST600 programme de stabilité: manuel d’utilisation version 701. Hydro-Québec, Vice-présidence tech-nologie et IREQ, 1987.
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