Utility Outage Data Driven Interaction Networks for Cascading Failure Analysis and Mitigation
Utility Outage Data Driven Interaction Networks for Cascading Failure Analysis and Mitigation
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DOI：
10.1109/tpwrs.2020.3015380
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发表时间：
2021-03
影响因子：
6.6
通讯作者：
Junjian Qi
中科院分区：
文献类型：
--
作者：
Junjian Qi
本文分析了来自公用事业的真实停机数据，以便更好地理解级联停机传播。为了准确地估计组件中断之间的相互作用，引入了两种机制：代间相互作用的演化和连续代之间的记忆。通过求解一组精心制定的线性方程，考虑复杂组件相互作用引起的循环，基于估计的交互网络计算度量，即一个组件中断后的预期中断次数。用数学方法证明了线性方程的唯一正解的存在性。对于中断传播至关重要的组件将根据所开发的度量进一步确定。此外，根据实际停机数据估计的交互网络揭示了停机传播特性。此外，利用估计的相互作用，从一个高度概率生成依赖的相互作用模型模拟的级联很好地捕获了原始中断数据的属性，包括线路中断次数的分布、分支过程的后代平均值、组件度量的分布和已识别的关键组件。通过降低已识别的关键组件失效的概率，可以大大降低级联故障风险。
In this paper, real outage data from utilities are analyzed to gain better understanding of cascading outage propagation. In order to accurately estimate the interactions between component outages, two mechanisms are introduced: the evolution of interactions over generations and the memory between consecutive generations. A metric, the expected number of outages following one component outage, is calculated based on the estimated interaction networks by solving a set of carefully formulated linear equations, considering loops due to the complex component interactions. Existence of unique positive solution for the linear equations is mathematically proved. Components that are critical for outage propagation are further identified based on the developed metric. Besides, the outage propagation properties are revealed by the interaction networks estimated from real outage data. Further, the cascades simulated from a highly probabilistic generation-dependent interaction model using the estimated interactions well capture the properties of the original outage data in terms of the distribution of the number of line outages, the offspring mean of the branching process, the distribution of the component metrics, and the identified critical components. Cascading failure risks are greatly mitigated by reducing the probability that the identified critical components fail.