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

doi:10.17775/CSEEJPES.2022.08970

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

Special Section Papers | 更新时间：2026-10-15

- 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
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第1期页码：66-75
- 作者机构：
  
  1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronics Engineering, Huazhong University of Science and Technology,Wuhan,China
  2. Dispatching and Control Center, Central China Branch of State Grid Corporation of China,Wuhan,China
  3. Department of Electrical Engineering and Computer Science, University of Tennessee,TN,Knoxville,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.08970
  中图分类号：
- 纸质出版：2024-01-01
- 稿件说明：
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Lingkang Zeng, Wei Yao, Ze Hu, 等. Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):66-75.

Lingkang Zeng, Wei Yao, Ze Hu, et al. Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 66-75.
Lingkang Zeng, Wei Yao, Ze Hu, 等. Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):66-75. DOI： 10.17775/CSEEJPES.2022.08970.

Lingkang Zeng, Wei Yao, Ze Hu, et al. Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 66-75. DOI： 10.17775/CSEEJPES.2022.08970.

摘要

Abstract

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.

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