由效用线路停电数据形成的马尔可夫影响图以缓解大规模级联
A Markovian influence graph formed from utility line outage data to mitigate large cascades
AI总结：
本文提出一种基于效用线路停电数据的马尔可夫影响图，用于描述级联线路停电各代之间转移的概率，通过将多个线路停电作为马尔可夫链状态，扩展了传统影响图，并能预测级联规模分布，同时允许分析和测试缓解措施的效果。
摘要：
我们利用观测到的输电线路停电数据来构建一个马尔可夫影响图，该图描述了级联线路停电各代之间转移的概率，其中每个级联代由单一线路停电或多条线路停电组成。新的影响图定义了一个马尔可夫链，并通过将多个线路停电作为马尔可夫链状态，扩展了先前的影响图。扩展的影响图能够再现效用数据中的级联规模分布。特别是，它可以估计小、中和大级联的发生概率。影响图的关键优势在于允许分析和测试缓解措施的效果，这在观测数据中是不可用的。我们利用马尔可夫链的渐近性质，找到参与大级联最严重的线路，并展示对这些关键线路的升级如何降低大级联的概率。
英文摘要：
We use observed transmission line outage data to make a Markov influence graph that describes the probabilities of transitions between generations of cascading line outages, where each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.
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。
