Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning

CHEN Ge; ZHANG Junbo; PENG Ying; WANG Mingyang

doi:10.7500/AEPS20240808004

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Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning

Basic Research | 更新时间：2025-06-16

- Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning
- Automation of Electric Power Systems Vol. 49, Issue 6, Pages: 144-156(2025)
- 作者机构：
  
  华南理工大学电力学院，广东省广州市 510641
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52277101)
- DOI：10.7500/AEPS20240808004
  CLC：
- Received：08 August 2024，
  
  Revised：2024-11-27，
  
  Published Online：24 February 2025，
  
  Published：25 March 2025
- 稿件说明：
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陈戈,张俊勃,彭颖等.基于决策空间裁剪强化学习的连锁故障调切结合紧急控制[J].电力系统自动化,2025,49(6):144-156.

CHEN Ge,ZHANG Junbo,PENG Ying,et al.Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning[J].Automation of Electric Power Systems,2025,49(6):144-156.
陈戈,张俊勃,彭颖等.基于决策空间裁剪强化学习的连锁故障调切结合紧急控制[J].电力系统自动化,2025,49(6):144-156. DOI： 10.7500/AEPS20240808004.

CHEN Ge,ZHANG Junbo,PENG Ying,et al.Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning[J].Automation of Electric Power Systems,2025,49(6):144-156. DOI： 10.7500/AEPS20240808004.

摘要

新能源快速功率控制技术的发展使新能源功率调节具有参与过载主导型连锁故障紧急控制的潜力，而现有基于深度强化学习的连锁故障紧急控制方法未考虑调切结合的控制策略，且存在因决策空间较大而难以收敛的问题。为此，文中提出一种基于决策空间裁剪图深度强化学习的电网连锁故障调切结合紧急控制方法。首先，构建调切结合的映射策略模型，提出连锁故障紧急控制框架；其次，提出基于图卷积深度网络的决策空间裁剪模型及学习方法，根据灵敏度大小保留有控制贡献的控制地点以裁剪决策空间；然后，提出基于图深度强化学习的映射策略模型学习方法，在给定控制地点下实现对控制量的学习；最后，在IEEE 39节点和IEEE 300节点系统中验证所提方法的有效性和泛化性。

Abstract

The development of fast power control technologies for renewable energy makes the power adjustment of renewable energy has the potential to participate in the emergency control against overload-dominated cascading failures. However

the existing deep reinforcement learning based emergency control methods against cascading failures do not consider the control strategies combined adjustment and tripping

and there exists a problem of difficulty in converging with large decision spaces. Therefore

an emergency control method combined with adjustment and tripping against cascading failures in the power grid based on the decision space pruning graph deep reinforcement learning is proposed. First

a mapping strategy model combined adjustment and tripping is established

and an emergency control framework against cascading failures is proposed. Subsequently

a decision space pruning model and its learning method based on the graph convolutional deep network are proposed to prune the decision space by retaining the control locations with control contributions through sensitivity. Then

a learning method for the mapping policy model based on the graph deep reinforcement learning is proposed to learn control quantities under the given control locations. Finally

the effectiveness and generalization of the proposed method are verified in the IEEE 39-bus and IEEE 300-bus systems.

关键词

Keywords

references

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