Power System Transient Stability Emergency Control Based on Hybrid Distributed Deep Reinforcement Learning

The large-scale integration of renewable energy into the grid under the "dual carbon" goal enhances the time-varying characteristics of power system operation, and puts forward new requirements for online emergency control strategies. To maintain the transient stability of the power system after being subjected to large disturbances, this paper proposes an online emergency control strategy based on hybrid distributed deep reinforcement learning. First, a Markov decision process is adopted to describe the mathematical model of transient stability emergency control. Next, to address the issues of the curse of dimensionality and accuracy dropping caused by the discretization of hybrid action space in conventional deep reinforcement learning algorithms, a discrete-continuous hybrid policy architecture is proposed, and the proximal policy optimization algorithm is introduced as the policy optimizer, achieving direct handling of hybrid action space in emergency control problem. Then, to address the drawbacks of conventional deep reinforcement learning methods, such as long training time and insufficient robustness, a distributed parallel training architecture is introduced, and an invalid action mask combining prior physical knowledge is developed, further improving the training efficiency and robustness. Finally, the test results in the IEEE 39-bus system confirm the effectiveness and superiority of the proposed method in transient stability emergency control.