电力系统自动化2025，Vol.49Issue(6)：144-156,13.DOI:10.7500/AEPS20240808004
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
Emergency Control Combined with Adjustment and Tripping Against Cascading Failures Based on Decision Space Pruning Reinforcement Learning
摘要
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
新型电力系统/新能源/连锁故障/图深度强化学习/紧急控制/决策空间裁剪Key words
new power system/renewable energy/cascading failure/graph deep reinforcement learning/emergency control/decision space pruning引用本文复制引用
陈戈,张俊勃,彭颖,王明扬..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):144-156,13.基金项目
国家自然科学基金资助项目(52277101) （52277101）
国家自然科学基金企业创新发展联合基金项目(U22B6007) （U22B6007）
广州市应用基础研究计划项目(2024A04J9892). This work is supported by National Natural Science Foundation of China(No.52277101,No.U22B6007)and Science and Technology Projects in Guangzhou(No.2024A04J9892). （2024A04J9892）
