首页|Intelligent Predetermination of Generator Tripping Scheme:Knowledge Fusion-based Deep Reinforcement Learning Framework
Intelligent Predetermination of Generator Tripping Scheme:Knowledge Fusion-based Deep Reinforcement Learning Framework
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Generator tripping scheme(GTS)is the most com-monly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermi-nation 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 knowl-edge 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.
Deep reinforcement learninggenerator tripping schemegraph convolutional networkinvalid action maskingknowledge fusion
State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology,Wuhan 430074,China
Dispatching and Control Center,Central China Branch of State Grid Corporation of China,Wuhan 430077,China
Department of Electrical Engineering and Computer Science,University of Tennessee,Knoxville,TN 37996,USA
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
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