Design a Robust and Adaptive Reinforcement Learning Based SVC Controller for Damping Enhancement in Power Systems.
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.