Angle Instability and Oscillations Control using SVC: A Deep Reinforcement Learning Enhanced Local Controller



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Submitted Dec 19, 2022
Published Feb 26, 2023
10.24018/ejece.2023.7.1.490

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An advanced control scheme to deal with transient angle instability and low-frequency angle oscillations in power systems is proposed in this paper. The control combines an existing controller (derived from the concept of Lyapunov energy functions) and deep Q-network (a deep reinforcement learning algorithm) to control a static VAr compensator. This control is modified in this paper in a way to directly control thyristor controller reactor part of the compensator, offering an easier implementation. The aim is to improve performances of existing control through the use of deep Q-network while retaining its basic system stabilization characteristics. Advantages of the proposed control scheme are illustrated by implementation of a model of four-machine test power system (this system is considered as the benchmark when studying the phenomena dealt with in this paper) in MATLAB/Simulink environment and using TensorFlow toolkit for deep Q-network usage.
Keywords: Deep Reinforcement Learning Lyapunov Energy-Based Control Power System Control Power System Stability

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