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

输配电系统级联停电的数据驱动建模、监控和缓解

• 批准号: 1609080
• 负责人: Zhaoyu Wang
• 金额: $ 34.79万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609080&HistoricalAwards=false
• 关键词: Data; driven; modeling; monitoring; mitigation

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.

连锁停电是一系列复杂的相关故障，这些故障逐渐削弱了向国家传输和分配电力的大规模电网。这些级联停电偶尔发生，可能导致大范围停电，影响多达数千万人。由于电力系统基础设施老化、恶劣天气事件、可再生能源整合导致的可变性增加以及网络和物理攻击等新威胁，连锁停电的风险存在，甚至可能增加。考虑到停电的巨大经济和社会影响，必须对级联停电的相互作用和传播进行建模和监控，并开发有效的缓解技术以降低其发生的风险。连锁停电事故的原因多种多样，非常复杂。然而，电力公司收集的大量历史停电数据为连锁停电的新分析提供了机会。该项目将使用这些数据来了解连锁停电的传播和相互作用，推进对高风险运行条件的监控，并提供减少停电传播的工程原则。该研究有助于提高关键基础设施系统的恢复能力，通过提高输电和配电网对连锁停电的恢复能力，来应对国家面临的重大挑战。该项目为研究生和本科生提供了独特的多学科培训机会，将联合收割机研究工作和教育相结合。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。停电数据将使用大数据学习技术进行聚类，以揭示类似停电的模式。新的模型将描述复杂的停电相互作用的关键方面。例如，停电如何相互影响将被表示为电网组件之间的交互网络。数据将用于从历史数据中确定高风险级联条件的可观察相关性，以便仅在需要时适用级联事件的缓解计划。最后，新的模型和数据将用于制定有效监测和缓解的框架。由于大规模输电停电对社会造成灾难性影响，风险很高，而较小的配电停电也很频繁，研究小组将使用输电和配电网的记录数据分析停电依赖性和级联。将开发原型软件，用于处理数据、确定模型参数和关键指标，并对连锁停电进行基于风险和数据驱动的分析。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。

项目成果

Extracting Resilience Metrics From Distribution Utility Data Using Outage and Restore Process Statistics

• DOI: 10.1109/tpwrs.2021.3074898
• 发表时间: 2021-11-01
• 期刊: IEEE TRANSACTIONS ON POWER SYSTEMS
• 影响因子: 6.6
• 作者: Carrington, Nichelle'Le K.;Dobson, Ian;Wang, Zhaoyu
• 通讯作者: Wang, Zhaoyu

Extracting Resilience Statistics from Utility Data in Distribution Grids

从配电网的公用事业数据中提取弹性统计数据

• DOI: 10.1109/pesgm41954.2020.9281596
• 发表时间: 2020
• 期刊: IEEE Power and Energy Society General Meeting
• 作者: Carrington, Nichelle'Le K.;Ma, Shanshan;Dobson, Ian;Wang, Zhaoyu
• 通讯作者: Wang, Zhaoyu

Bayesian Estimates of Transmission Line Outage Rates That Consider Line Dependencies

• DOI: 10.1109/tpwrs.2020.3012840
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Kai Zhou;J. Cruise;C. Dent;I. Dobson;L. Wehenkel;Zhaoyu Wang;Amy L. Wilson

IEEE Transactions on Power Systems

IEEE 电力系统汇刊

• 发表时间: 2018
• 期刊: IEEE transactions on power systems
• 影响因子: 6.6
• 作者: Kancherla, Sameera;Dobson, Ian
• 通讯作者: Dobson, Ian