CAREER: Physics-informed Graph Learning for Anomaly Detection in Power Systems

职业：用于电力系统异常检测的物理信息图学习

• 批准号: 2338642
• 负责人: Tuyen Vu
• 金额: $ 50万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338642&HistoricalAwards=false
• 关键词: CAREER; Physics; informed; Graph; Learning

This NSF CAREER project aims to improve state estimation and anomaly detection problems for power system operators. The project will bring transformative changes in how operators detect the system states and abnormal events, including cyber-attacks. This will be achieved by using adaptive graph-based and physics-based methods. The intellectual merits of the project include 1) Development of spatial-temporal graph neural networks (ST-GNN) for state estimation and anomaly detection, 2) Integration of physics into the ST-GNN, 3) Correlation of IT/OT events, and 4) Development of state estimation and anomaly detection at scale. The broader impacts of the project include spurring the interests of future female engineers in sustainable energy and cyber-physical security through Clarkson's Horizons Program, supporting underrepresented undergraduates in research, enhancing collaboration between national labs and universities, and enhancing awareness of utilities through annual workshops for utility students. Timely identification and mitigation of emerging cyber-physical system (CPS) risks necessitate more sophisticated and resilient tools for state estimation and anomaly detection employed by grid operators. The primary objective of this CAREER project is to improve the state estimation and anomaly detection capabilities of power system operators through adaptive graph-based and physics-based methods. This objective will be realized through a meticulously crafted work plan aimed at (1) improving the precision and robustness of state estimation using physics-informed learning on graphs and (2) enhancing anomaly detection while achieving scalability through hybrid and distributed approaches. These goals will be pursued through four key research activities: 1) Developing spatial-temporal graph neural networks for enhanced state estimation, 2) Establishing a physics-informed framework to handle imbalanced data, 3) Integrating information technology (IT) data with operational technology (OT) data to bolster anomaly detection, and 4) Creating distributed methods for achieving scalable state estimation and anomaly detection. On the educational front, the aim is to broaden and strengthen the power engineering workforce by encouraging women to participate in the field, training the next generation of power engineers, and elevating the skills and awareness of current power engineers to effectively navigate emerging CPS threats in power system operations.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.

这个NSF职业项目旨在改善国家估计和电力系统运营商的异常检测问题。该项目将在运营商检测系统状态和包括网络攻击在内的异常事件的方式上带来变革性的变化。这将通过使用基于自适应的基于图形和物理学的方法来实现。该项目的智力优点包括1）用于状态估计和异常检测的时空图神经网络（ST-GNN）的开发，2）将物理学整合到ST-GNN中，3）IT/OT事件的相关性和4）状态估计的发展和状态估计的发展和大规模检测。该项目的更广泛的影响包括通过克拉克森的地平线计划激发未来女性工程师对可持续能源和网络物理安全的利益，支持在研究中代表性不足的本科生，增强了国家实验室与大学之间的协作，并通过年度工作室来增强公用事业的认识。 及时识别和缓解新兴网络物理系统（CPS）风险需要更复杂和弹性的工具，以进行网格操作员采用的状态估计和异常检测。该职业项目的主要目的是通过基于自适应的基于图形和物理学的方法来提高电力系统操作员的状态估计和异常检测能力。该目标将通过精心制作的工作计划来实现，旨在（1）使用物理学上的学习在图表上提高国家估计的精度和鲁棒性，以及（2）通过混合和分布式方法来实现可伸缩性，同时增强异常检测。这些目标将通过四个关键的研究活动来实现：1）开发空间 - 周期性图神经网络以增强状态估计，2）建立一个物理知识的框架来处理不平衡数据，3）将信息技术（IT）数据与操作技术（OT）数据集成在一起，以增强型号检测的方法，并创建分配量表估计的方法，以实现量表估计。在教育方面，目的是通过鼓励妇女参与该领域，培训下一代电力工程师，提高现任电力工程师的技能和意识来有效地在电力系统运营中导致CPS威胁的能力，这反映了NSF的法定任务和范围的范围，这是通过评估的范围，这表明了范围的范围。