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

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

CSEE Journal of Power and Energy Systems2024，Vol.10Issue(1)：P.66-75,10.

CSEE Journal of Power and Energy Systems2024，Vol.10Issue(1)：P.66-75,10.DOI:10.17775/CSEEJPES.2022.08970

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

Lingkang Zeng ¹Wei Yao ²Ze Hu ²Hang Shuai ³Zhouping Li ²Jinyu Wen ²Shijie Cheng²

作者信息

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

关键词

Deep reinforcement learning/generator tripping scheme/graph convolutional network/invalid action masking/knowledgefusion

分类

信息技术与安全科学

引用本文复制引用

Lingkang Zeng,Wei Yao,Ze Hu,Hang Shuai,Zhouping Li,Jinyu Wen,Shijie Cheng..Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework[J].CSEE Journal of Power and Energy Systems,2024,10(1):P.66-75,10.

基金项目

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

CSEE Journal of Power and Energy Systems

OACSTPCDEI

ISSN：2096-0042

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