Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually

GTS is composed of offline predetermination and real-time scenario match. However

it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination

this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First

the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then

linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially

KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover

the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.