CAREER: Deciphering Large-Scale Real Outage Data for Cascading Failure Analysis, Prevention, and Intervention

连锁故障是复杂工程系统中的一种常见现象，如电网、天然气系统、交通网络、互联网以及相互依存的关键基础设施系统。例如，2003年的美国-加拿大停电和2012年的印度停电等之前的重大连锁停电事故都造成了许多部件故障、重大经济损失和严重的社会影响。因此，极大地增强复杂系统的弹性，保持灯火通明，对于最大限度地减少服务中断，保持经济增长和繁荣至关重要。然而，现有的连锁故障研究在很大程度上依赖于仿真模型，这些模型要么过于笼统，无法提供可实施的预防/干预策略，要么太难进行基准测试或验证，无法得出可靠的结论。在这个职业项目中，现有方法的重大局限性将通过开发一个全新的实际停电数据驱动的研究框架来解决，以便更好地分析、预防和干预连锁故障。本项目开展的连锁故障分析、预防和干预研究，将大大降低灾难性停电的风险，提高电力系统的抗灾能力，为美国和世界各国带来巨大的经济效益和社会效益。提出了几项教育活动，包括在本科生和研究生一级开发课程，让本科生参与研究，让西班牙裔学生和女性学生参与该项目。对于K-12和社区教育，将建立基于级联停电场景的演示。这个职业项目的目标是为研究连锁故障和电力系统韧性开创一个新的数据驱动的研究方向，并为数据驱动的连锁故障分析、预防和干预开发系统的、变革性的理论基础和算法技术，解决现有方法的固有局限性，刷新对真实世界连锁故障的理解，并提供分析、预防和最终干预连锁故障的工具。网络科学、统计推理、数据科学和深度学习将与电力系统领域知识无缝结合，以获得解决这一非常具有挑战性的问题的独特解决方案。具体地说，将重点介绍以下三个相互关联的项目，以显著推进研究议程：1)通过有效和准确地估计故障交互作用，将公用事业停电数据转换为信息，为数据驱动的级联故障方法奠定坚实的基础，解决级联阶段传播模式演变的挑战、级联之间的高度异质性以及数据稀缺性。将开发深度学习方法来揭示交互的结构特征，并恢复丢失的组件交互。2)利用实际的停电数据，基于估计的故障交互作用，研究连锁故障传播的复杂和普遍的时空特性，将信息转化为知识，揭示连锁行为的时间演化、空间传播模式和时间演化和空间传播的临界性。3)将知识转化为可操作的智慧，推进极具挑战性的连锁故障预防与干预研究。级联故障预防应基于确定的关键部件，使用预期停机次数来开发。将使用故障交互作用网络和原始系统拓扑来识别关键部件的结构或操作特征。此外，利用从实际停电数据中学习到的信息/知识和电力系统领域知识，通过一种深度、安全的强化学习方法，建立和解决了一种新的连锁故障干预问题。通过解释学习到的最优策略的高层抽象，将提供有关级联故障干预的有用见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Cascading failure is a common phenomenon in complex engineered systems, such as electric power grids, natural gas systems, transportation networks, Internet, and interdependent critical infrastructure systems. For example, previous major cascading blackouts such as the U.S.-Canadian blackout in 2003 and the Indian blackout in 2012 have caused many component failures, significant economic losses, and severe social impacts. Therefore, greatly enhancing the complex system resilience and keeping the lights on are thus very critical for minimizing the service disruptions and maintaining the economic growth and prosperity. However, the existing cascading failure study heavily relies on simulation models, which are either too general to be able to provide implementable prevention/intervention strategies or too difficult to benchmark or validate for drawing reliable conclusions. In this CAREER project, the significant limitations of the existing approaches will be addressed by developing a fresh-new real outage data driven research framework in order to better analyze, prevent, and intervene cascading failures. The research on cascading failure analysis, prevention, and intervention to be conducted in this project will help greatly reduce the risk of catastrophic blackouts and enhance the resilience of power systems, bringing tremendous economic and social benefits to U.S. and other countries around the world. Several educational activities including curriculum development at undergraduate and graduate level, engaging undergraduate students in research, and involving Hispanic and women students in the project are proposed. For K-12 and community education, a demonstration based on a cascading blackout scenario will be built. Academic community will be engaged with webinars, seminars, and panel sessions at conferences.The goal of this CAREER project is to initiate a new data-driven research direction for studying cascading failure and power system resilience and develop systematic, transformative theoretical foundations and algorithmic techniques for data-driven cascading failure analysis, prevention, and intervention, addressing the inherent limitations of existing approaches, refreshing the understanding of real-world cascading failure, and providing tools to analyze, prevent, and finally intervene cascading failures. Network science, statistical inference, data science, and deep learning will be seamlessly integrated with power system domain knowledge in order to obtain a unique solution to the very challenging problem. Specifically, the following three inter-related projects will be highlighted to significantly advance the research agenda: 1) A solid foundation of the data-driven cascading failure approach will be built through an efficient and accurate estimation of failure interactions to convert utility outage data to information, addressing the challenges of propagation pattern evolution over cascading stages, high heterogeneity among cascades, and data scarcity. Deep learning approaches will be developed to reveal the structural features of the interactions and recover the missing component interactions. 2) Utilizing real utility outage data, the complex and universal temporal-spatial properties of cascading failure propagation will be investigated based on the estimated failure interactions to convert information to knowledge, revealing the temporal evolution of cascading behaviors, spatial propagation patterns, and criticality in both temporal evolution and spatial propagation. 3) The very challenging cascading failure prevention and intervention study will be advanced by converting knowledge to actionable wisdom. Cascading failure prevention is to be developed based on the identified critical components using the expected number of outages. Structural or operational signatures of critical components will be identified using both the failure interaction network and the original system topology. Further, a novel cascading failure intervention problem will be formulated and solved by a deep, safe reinforcement learning approach that utilizes both the learned information/knowledge from real outage data and the power system domain knowledge. By interpreting the high-level abstraction of the learned optimal policies, useful insights about cascading failure intervention will be provided.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.