CRII: CPS: Leveraging Convex Relaxation Techniques to Improve Power System Surveillance

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。本项目旨在通过改进聚合电力系统状态估计（PSSE）方法监测和评估电网健康状况的能力，增强电网的可靠性和鲁棒性。电网是一个网络物理系统，对现代日常生活至关重要，几乎随时随地都可以按需使用。网格可以说是全球最大的工程结构。该项目的目标是：(1)了解传统PSSE方法固有的漏洞，(2)提高PSSE算法对潜在破坏性条件的可靠性和鲁棒性。该项目扩展了最近开发的电力系统优化技术，以更好地了解整个电力系统的运行情况。该项目将与阿肯色州立大学的外展项目“P-20教育创新中心”合作，分享这项研究，并鼓励STEM领域的职业发展。本计画将基于凸松弛的技术扩展到大型资讯物理电力系统，以改善这些系统的监测、分析和可控性。该项目提出了一种有效的基于凸松弛的方法来解决PSSE问题。该项目利用半定规划松弛法和极值点法对大型交流（AC）电力系统进行分析，提供比传统PSSE更严格的流量分析界限。本项目还分析了在电力系统的某些运行区域之间进行转换的不可行性，这些区域可以在启动状态转换之前提供安全保障。这种方法可以识别特定类型的稀疏假数据，这些数据可能是由于传感器的性质或设计损坏而产生的，并且无法被当前电力系统的坏数据检测算法检测到。所提出的更紧松弛格式将广泛适用于大流系统模型中的各种非凸和非线性优化问题以及复杂的优化问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project aims to strengthen dependability and robustness of the electric power grid by improving the capability to aggregated power system state estimation (PSSE) methods to monitor and assess the health of a power grid. The electric power grid is a cyber-physical systems, essential for modern daily life that is always available on-demand nearly anywhere at any time. The grid is arguably the largest global engineered structure. The goals of this project are to (1) understand vulnerabilities intrinsic to traditional PSSE methods, and (2) improve the dependability and robustness of PSSE algorithms to potentially disruptive conditions. This project extends recently developed power system optimization techniques to enable better situation awareness of the operations of the overall power system. The project will work with the Arkansas State University’s outreach program “P-20 Educational Innovation Center” to share this research and encourage careers in STEM fields.This project extends convex relaxation-based techniques for large-scale cyber-physical power system in order to improve the monitoring, analysis, and controllability of these systems. The project presents an efficient convex relaxation-based approach to the PSSE problem. The project leverages semidefinite programming relaxation and extreme point approaches for large-scale alternating current (AC) power systems to provide tighter bounds on flow analyses than traditional PSSE. This project also presents an analysis showing the infeasibility of transitioning between certain operating regions in the power system that can be used to provide safety and security guarantees before initiating state transitions. This approach can identify specific types of sparse false data, that might arise from corrupted sensors by nature or design, and go undetected by current power systems' bad data detection algorithms. The proposed tighter relaxation schemes will be broadly applicable to a wide variety of nonconvex and nonlinear optimization problems in large flow-system models and in complex optimization problems.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.