电力系统自动化2026，Vol.50Issue(10)：59-72,14.DOI:10.7500/AEPS20250625004
采用决策空间与策略模型动态迭代的线路过载紧急控制混合学习
Hybrid Learning for Line Overload Emergency Control with Dynamic Iteration of Decision Space and Strategy Model
摘要
Abstract
Renewable energy can rapidly regulate the power in the new power system,demonstrating the potential of participating in overload emergency control for lines.However,when it is adopted,generation methods for the emergency control strategy based on deep reinforcement learning face the challenges of excessively large decision space and high solution complexity.To address this issue,a hybrid learning method for emergency control with dynamic iteration of decision space and strategy model is proposed.First,a dual-network model comprising a control location network and a control value network is constructed,and an iterative learning framework for both networks is designed.Second,the control location network and its learning objectives are introduced,and a sensitivity-based sample generation method is designed to learn the control location network.Then,a deep reinforcement learning method for the control value network is proposed,and a segmented exploration strategy is designed for efficient learning of the control value network.Next,a dynamic iteration implementation process between the control value network and control location network is designed.Finally,the effectiveness of the proposed method is validated in the IEEE 39-bus system,IEEE 300-bus system,and a provincial power grid of China.关键词
紧急控制/新能源/深度强化学习/线路过载/混合学习/动态迭代/决策空间/样本生成Key words
emergency control/renewable energy/deep reinforcement learning/line overload/hybrid learning/dynamic iteration/decision space/sample generation引用本文复制引用
张寿志,陈戈,张俊勃,彭颖..采用决策空间与策略模型动态迭代的线路过载紧急控制混合学习[J].电力系统自动化,2026,50(10):59-72,14.基金项目
国家自然科学基金企业创新发展联合基金集成项目(U22B6007) （U22B6007）
国家自然科学基金资助项目(52277101) （52277101）
中央高校基本科研业务费专项资金资助项目(2024ZYGXZR109). This work is supported by National Natural Science Foundation of China(No.U22B6007,No.52277101)and Fundamental Research Funds for the Central Universities(No.2024ZYGXZR109). （2024ZYGXZR109）
