探索历史性数据中的连锁停电与天气现象
Exploring cascading outages and weather via processing historic data
AI总结：
通过分析历史停电数据，研究天气对连锁停电的影响，提出改进数据处理和模型以更好地理解和量化连锁效应的方法。
摘要：
我们描述了历史初始线路停电的总体统计特征，并探讨了其对制定备用清单和理解哪些初始停电可能引发进一步连锁反应的影响。我们利用历史停电数据，通过原因代码和NOAA风暴数据估计天气对连锁反应的影响。恶劣天气显著增加停电率，并与连锁效应相互作用，应在连锁模型和模拟中予以考虑。我们建议如何将天气影响纳入OPA连锁模拟并进行验证。有很好的前景通过改进历史停电数据的批量统计来提升对连锁效应的理解和量化。
英文摘要：
We describe some bulk statistics of historical initial line outages and the implications for forming contingency lists and understanding which initial outages are likely to lead to further cascading. We use historical outage data to estimate the effect of weather on cascading via cause codes and via NOAA storm data. Bad weather significantly increases outage rates and interacts with cascading effects, and should be accounted for in cascading models and simulations. We suggest how weather effects can be incorporated into the OPA cascading simulation and validated. There are very good prospects for improving data processing and models for the bulk statistics of historical outage data so that cascading can be better understood and quantified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
