LAUSR

Static Var compensator (SVC) has often been used in the power system industries to regulate the bus voltage and reactive power. With an increasing concern of the power system transient stability, the SVC has also been used to dynamically regulate the power flow for oscillation dampings in recent years. A guided surrogate-gradient-based evolutionary strategy (GSES)-based SVC control approach is proposed in this article to damp power system interarea oscillations. The GSES algorithm is used to train a reinforcement learning agent to learn the best decisions that control the SVC, with an objective of quickly damping interarea oscillations. The GSES algorithm brings many benefits such as easier, more robust, and efficient training with less required hyperparameters. Because the GSES can engage with multiple individual workers during the training process, parallel computation techniques are implemented in this research to accelerate the computational speed. To test the performance of the proposed GSES-based SVC controller, we compared this novel method with a supplementary SVC damping controller on two different test systems. Results indicate that the proposed GSES-based SVC control approach can effectively damp power system interarea oscillations. Especially, in some case studies, the proposed approach successfully saved the test system from a transient-instability-caused breaking down.