用拓扑感知强化学习提升电网灾后自愈能力
Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks
- 将电网拓扑特征融入图强化学习模型，动态优化故障恢复策略
- 相比基线模型，供电量提升6%，电压越限减少6-8%
- 适合电力系统自动化、智能电网研究者参考
极端天气和网络攻击可能导致配电网络组件失效并中断运行，此时通常通过重构和负荷切除来增强韧性。本研究提出一种拓扑感知的图强化学习框架用于停电管理，将配电网络的高阶拓扑特征嵌入图强化学习模型中，实现重配置与负荷切除，以最大化供电量并维持运行稳定。在300种不同故障场景下的改进版IEEE 123节点馈线测试表明，引入拓扑数据分析工具持久同调（Persistence Homology, PH）后，累积奖励提高9-18%，功率输送量最多增加6%，电压越限次数减少6-8%。结果表明，将强化学习与拓扑数据分析结合，可有效实现配电网络的自我修复，支持快速、自适应且自动化的恢复。
原文摘要 · Abstract (English)
Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding are often adopted for resilience enhancement. This study introduces a topology-aware graph reinforcement learning (RL) framework for outage management that embeds higher-order topological features of the DN into a graph-based RL model, enabling reconfiguration and load shedding to maximize energy supply while maintaining operational stability. Results on the modified IEEE 123-bus feeder across 300 diverse outage scenarios demonstrate that incorporating the topological data analysis (TDA) tool, persistence homology (PH), yields 9-18% higher cumulative rewards, up to 6% increase in power delivery, and 6-8% fewer voltage violations compared to a baseline graph-RL model. These findings highlight the potential of integrating RL with TDA to enable self-healing in DNs, facilitating fast, adaptive, and automated restoration.
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