| 引用本文: | 王冲,石大夯,万灿,陈霞,吴峰,鞠平.基于多智能体深度强化学习的随机事件驱动故障恢复策略[J].电力自动化设备,2025,45(3): |
| WANG Chong,SHI Dahang,WAN Can,CHEN Xia,WU Feng,JU Ping.Uncertain event-driven fault recovery strategy based on multi-agent deep reinforcement learning[J].Electric Power Automation Equipment,2025,45(3): |
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| 摘要: |
| 为了减少配电网故障引起的失负荷，提升配电网弹性，提出一种基于多智能体深度强化学习的随机事件驱动故障恢复策略：提出了在电力交通耦合网故障恢复中的随机事件驱动问题，将该问题描述为半马尔可夫随机决策过程问题；综合考虑系统故障恢复优化目标，构建基于半马尔可夫的随机事件驱动故障恢复模型；利用多智能体深度强化学习算法对所构建的随机事件驱动模型进行求解。在IEEE 33节点配电网与Sioux Falls市交通网形成的电力交通耦合系统中进行算例验证，结果表明所提模型和方法在电力交通耦合网故障恢复中有着较好的应用效果，可实时调控由随机事件(故障维修和交通行驶)导致的故障恢复变化。 |
| 关键词: 随机事件驱动 故障恢复 深度强化学习 电力交通耦合网 多智能体 |
| DOI：10.16081/j.epae.202501003 |
| 分类号:TM732 |
| 基金项目:国家自然科学基金资助项目(52277088) |
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| Uncertain event-driven fault recovery strategy based on multi-agent deep reinforcement learning |
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WANG Chong1, SHI Dahang1, WAN Can2, CHEN Xia3, WU Feng1, JU Ping1
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1.School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China;2.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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| Abstract: |
| In order to reduce the load loss caused by distribution network faults and improve the resilience of distribution network, an uncertain event-driven fault recovery strategy based on multi-agent deep reinforcement learning is proposed. The uncertain event-driven problem in the fault recovery power-traffic coupling network is presented, which is described as a semi-Markov random decision process problem. An uncertain event-driven fault recovery model based on semi-Markov is constructed by considering the optimization objective of system fault recovery comprehensively. Then, the multi-agent deep reinforcement learning algorithm is used to solve the uncertain event-driven model. A case study is carried out in the power-traffic coupling system formed by IEEE 33-bus distribution network and Sioux Falls traffic network. The results show that the proposed model and method have good application effects in the fault recovery of power-traffic coupling network, and can adjust the fault recovery changes caused by uncertain events(fault maintenance and traffic travel) in real time. |
| Key words: uncertain event-driven fault recovery deep reinforcement learning power-traffic coupling network multi-agent |
