用强化学习应对电网多阶段连锁故障，提升系统稳定性。
Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
- 将多阶段连锁故障问题建模为强化学习任务，设计仿真环境。
- 采用确定性策略梯度算法训练智能体，实现连续动作控制。
- 在IEEE 14/118节点系统上验证有效，适用于复杂电网防护场景。
电力系统中的连锁故障可能导致电网崩溃，严重干扰社会运行与经济活动。某些情况下会引发多阶段连锁故障，但现有缓解策略多为单阶段处理，忽视了多阶段的复杂性。本文将多阶段连锁故障问题建模为强化学习任务，并构建相应的仿真环境。利用确定性策略梯度算法训练强化学习智能体，实现连续动作控制。最后在IEEE 14-bus和IEEE 118-bus系统上验证了所提方法的有效性。
原文摘要 · Abstract (English)
Cascading failures in power grids can lead to grid collapse, causing severe disruptions to social operations and economic activities. In certain cases, multi-stage cascading failures can occur. However, existing cascading-failure-mitigation strategies are usually single-stage-based, overlooking the complexity of the multi-stage scenario. This paper treats the multi-stage cascading failure problem as a reinforcement learning task and develops a simulation environment. The reinforcement learning agent is then trained via the deterministic policy gradient algorithm to achieve continuous actions. Finally, the effectiveness of the proposed approach is validated on the IEEE 14-bus and IEEE 118-bus systems.
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