The understanding of recovery processes in power distribution grids is limited by the lack of realistic outage data, especially large-scale blackout datasets. By analyzing data from three electrical companies across the United States, we find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity (defined as the peak number of outages during a blackout), but is independent of the number of customers affected. We present a cluster-based recovery framework to analytically characterize the dependence between outages, and interpret the dominant role blackout intensity plays in recovery. The recovery of blackouts is not random and has a universal pattern that is independent of the disruption cause, the post-disaster network structure, and the detailed repair strategy. Our study reveals that suppressing blackout intensity is a promising way to speed up restoration.

由于缺乏真实的停电数据，尤其是大规模停电数据集，对配电网恢复过程的理解受到限制。通过分析美国三个电力公司的数据，我们发现停电的恢复时间与附近停电的停机时间和停电强度（定义为停电期间停电的峰值次数）有关，但与停电的恢复时间无关。受影响的客户数量。我们提出了一个基于集群的恢复框架来分析表征停电之间的依赖性，并解释停电强度在恢复中所起的主导作用。停电的恢复不是随机的，而是具有与中断原因、灾后网络结构和详细修复策略无关的普遍模式。我们的研究表明，抑制停电强度是加速恢复的一种有前途的方法。