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

点击翻译按钮获取中文摘要

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.