Extracting Resilience Metrics From Distribution Utility Data Using Outage and Restore Process Statistics
Extracting Resilience Metrics From Distribution Utility Data Using Outage and Restore Process Statistics
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DOI：
10.1109/tpwrs.2021.3074898
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发表时间：
2021-11-01
影响因子：
6.6
通讯作者：
Wang, Zhaoyu
中科院分区：
文献类型：
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作者：
Carrington, Nichelle'Le K.;Dobson, Ian;Wang, Zhaoyu
恢复力曲线跟踪配电网事故期间停电的累积和恢复情况。我们证明了由效用数据生成的弹性曲线总是可以分解为中断过程和恢复过程，并且这些过程通常在时间上是重叠的。我们使用实际效用数据中的许多事件来描述这些过程的统计数据，并根据这些统计数据推导出恢复持续时间、未服务的客户小时数以及停机和恢复率等弹性指标的公式。这些公式将这些指标的平均值表示为事件中停机次数的函数。我们还给出了恢复持续时间的可变性的公式，该公式允许我们以95%的置信度预测最大恢复持续时间。总而言之，我们给出了一种简单而通用的方法来将弹性曲线分解为停机和恢复过程，然后展示如何使用这些过程从标准配电系统数据中提取弹性度量。
Resilience curves track the accumulation and restoration of outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.