Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework  

作　　者：Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng 

机构地区：[1]State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronics Engineering,Huazhong University of Science and Technology,Wuhan 430074,China [2]Dispatching and Control Center,Central China Branch of State Grid Corporation of China,Wuhan 430077,China. [3]Department of Electrical Engineering and Computer Science,University of Tennessee,Knoxville,TN 37996,USA

出　　处：《CSEE Journal of Power and Energy Systems》2024年第1期66-75,共10页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported by National Natural Science Foundation of China(No.U22B20111,No.U1866602)。

摘　　要：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.

关 键 词：Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion 

分 类 号：TM31[电气工程—电机]