Transient Voltage Control Based on Physics-Informed Reinforcement Learning

With the continuous expansion of power grid scale and the continuous implementation of Energy Internet construction, the long-distance and large-capacity electric energy exchange between regional power grids is increasingly frequent, which makes the stability problem to a wide range of attention. Therefore, this paper proposes a transient voltage control method based on physics information and reinforcement learning, which is called Physics-Informed Reinforcement Learning. This method combines the physical model and the data-driven model of power system, and takes the constraints in the physical model as the constraints of the data-driven model to accelerate the convergence rate of the model, so as to realize the rapid scheduling of transient voltage instability. Finally, an example of IEEE-9 bus system is given to verify the effectiveness and superiority of the proposed method.