用强化学习优化电网故障扩散的实时应对策略
Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
- 将影响图转化为马尔可夫决策过程，结合强化学习做实时决策
- 在IEEE 14/118节点系统上验证，主动断开线路可显著降低连锁故障风险
- 设计保守策略避免恶化系统状态，适合电力调度实时防护场景
尽管现代电力系统具有高可靠性，但随着可再生能源渗透率提升，连锁故障风险日益增加。实时缓解连锁故障需在不确定性下快速做出复杂操作决策。本文将影响图扩展为马尔可夫决策过程（MDP）模型，用于电力传输系统中的实时连锁故障缓解，考虑发电、负荷及初始故障的不确定性。该MDP包含“不作为”动作以支持保守决策，并采用强化学习求解。提出一种基于策略梯度的学习算法，初始策略对应未采取缓解措施的情况，且能处理无效动作。通过精心设计奖励函数，所学策略可在不恶化系统状态的前提下采取保守行动。该方法在IEEE 14节点和IEEE 118节点系统上验证，结果表明主动断开某些线路可有效降低连锁故障传播风险，且部分线路在多场景中持续表现为关键缓解节点。
原文摘要 · Abstract (English)
Despite high reliability, modern power systems with growing renewable penetration face an increasing risk of cascading outages. Real-time cascade mitigation requires fast, complex operational decisions under uncertainty. In this work, we extend the influence graph into a Markov decision process model (MDP) for real-time mitigation of cascading outages in power transmission systems, accounting for uncertainties in generation, load, and initial contingencies. The MDP includes a do-nothing action to allow for conservative decision-making and is solved using reinforcement learning. We present a policy gradient learning algorithm initialized with a policy corresponding to the unmitigated case and designed to handle invalid actions. The proposed learning method converges faster than the conventional algorithm. Through careful reward design, we learn a policy that takes conservative actions without deteriorating system conditions. The model is validated on the IEEE 14-bus and IEEE 118-bus systems. The results show that proactive line disconnections can effectively reduce cascading risk, and certain lines consistently emerge as critical in mitigating cascade propagation.
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