Data-driven modeling, monitoring and mitigation of cascading outages in transmission and distribution systems

级联停电是一系列复杂的相互依赖的故障，它们逐渐削弱了向全国输送和分配电力的大型电网。这种连锁停电偶尔会发生，并可能导致大面积停电，多达数千万人受到影响。由于电力系统基础设施的老化、恶劣天气事件、可再生能源整合带来的变变性增加以及网络和物理攻击等新威胁，级联停电的风险是存在的，甚至可能会增加。鉴于停电对经济和社会的巨大影响，必须对级联停电的相互作用和传播进行建模和监测，并开发有效的缓解技术来降低其发生的风险。级联中断的原因多种多样，非常复杂。然而，电力公司收集的大量历史停电数据为级联停电的新分析提供了机会。该项目将使用这些数据来了解级联停机的传播和相互作用，推进对高风险操作条件的监控，并提供减少停机传播的工程原理。该研究有助于通过提高输配电电网对级联停电的弹性来提高关键基础设施系统的弹性，这是国家面临的重大挑战。该项目为研究生和本科生提供独特的多学科培训机会，将研究工作与教育相结合。该项目将开创基于数据的方法，从公用事业公司现有的大量数据中提取见解和可操作的信息。停机数据将使用大数据学习技术进行聚类，以揭示类似停机的模式。新模型将描述复杂停机交互的关键方面。例如，停电如何相互影响将被表示为电网组件之间的相互作用网络。数据将用于从历史数据中识别高风险级联条件的可观察相关性，以便仅在需要时应用级联事件的缓解方案。最后，新的模型和数据将用于制定有效监测和缓解的框架。由于大规模的输电停电对社会的灾难性影响是高风险的，而较小的配电停电是频繁的，因此研究小组将使用输配电电网的记录数据来分析停电依赖关系和级联。原型软件将用于处理数据，识别模型参数和关键指标，并执行基于风险和数据驱动的级联中断分析。该项目整合了数据分析、风险分析、复杂网络和电力系统工程的工程和科学方法，以减轻停电风险。

Cascading outages are complicated series of dependent failures that progressively weaken the large-scale electric power grid that transmits and distributes electric power to the nation. These cascading outages happen occasionally and can cause widespread blackouts, with up to tens of millions of people affected. The risk of cascading blackouts is present and could even increase because of the aging of power system infrastructure, severe weather events, increased variability due to the integration of renewable energy sources, and new threats such as cyber and physical attacks. Given the tremendous economic and societal impacts of blackouts, it is imperative to model and monitor the interactions and propagation of cascading outages, as well as develop effective mitigation techniques to reduce the risk of their occurrence. The causes of cascading outages are diverse and very complicated. However, the large amount of historical outage data collected by electric utilities provides an opportunity for new analysis of cascading outages. This project will use this data to understand the propagation and interactions of cascading outages, advance the monitoring of high-risk operating conditions, and provide engineering principles to reduce outage propagation. The research contributes to the grand national challenge of improving resilience of critical infrastructure systems by enhancing the resilience of power transmission and distribution grids against cascading blackouts. The project provides unique multi-disciplinary training opportunities for graduate and undergraduate students that combine research work and education.The project will pioneer data-based approaches for extracting insights and actionable information from the considerable data already available to utilities. The outage data will be clustered using big data learning techniques to reveal patterns of similar outages. New models will characterize key aspects of complicated outage interactions. For example, how outages affect one another will be expressed as a network of interactions between power grid components. Data will be used to identify observable correlates of high-risk cascading conditions from the historical data, so that mitigation schemes for cascading events can be applied only when needed. Finally, the new models and data will be used to develop a framework for effective monitoring and mitigation. Since large transmission blackouts are high risk due to their catastrophic impact on society, and the smaller distribution blackouts are frequent, the research team will analyze outage dependencies and cascading using recorded data from both the transmission and distribution power grids. Prototype software will be developed for processing data, identifying model parameters and key metrics, and performing risk-based and data-driven analysis of cascading outages. The project integrates engineering and science approaches from data analysis, risk analysis, complex networks, and power systems engineering to mitigate blackout risk.