Abstract
Conventional wide-area control strategies that are deployed to damp inter-area oscillations use fixed a priori schemes based on modal analysis of the power system. As these controllers cannot be tuned online, such schemes fail to perform well during severe dynamic system changes. In this paper, a wide-area control architecture designed based on reinforcement learning and optimal adaptive critic network is proposed, that learns and optimizes the system closed-loop performance. Also, a value priority scheme is designed using a derived Lyapunov energy function for prioritization of local and the proposed wide-area global controller which ensures coherent damping of local and inter-area oscillations. The method increases the reliability and allows for automatic tuning of stabilizing controllers especially in the presence of wide-area monitoring constraints. Simulation results on 8-bus 5-machine and 68-bus 16-machine IEEE test systems highlight the efficiency of the proposed method.
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