Abstract
This paper proposes a reinforcement learning based SVC controller to improve the damping of power systems in the presence of load model parameters uncertainty. The proposed method is trained over a wide range of typical load parameters in order to adapt the gains of the SVC stabilizer. The simulation results show that the tuned gains of the SVC stabilizer using reinforcement learning can provide better damping than the conventional fixed-gains SVC stabilizer. To evaluate the usefulness of the proposed method, we compare the response of this method with PD controller. The simulation results show that our method has the better control performance than PD controller.
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© 2004 Springer-Verlag Berlin Heidelberg
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Rashidi, F., Rashidi, M. (2004). Design of a Robust and Adaptive Reinforcement Learning Based SVC Controller for Damping Enhancement in Power Systems. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_98
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DOI: https://doi.org/10.1007/978-3-540-30133-2_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
Online ISBN: 978-3-540-30133-2
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