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

CRII：CPS：利用凸松弛技术改进电力系统监控

• 批准号: 2308498
• 负责人: Mohammad Rasoul Narimani
• 金额: $ 17.5万
• 依托单位: The University Corporation, Northridge
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308498&HistoricalAwards=false
• 关键词: CRII; CPS; Leveraging; Convex; Relaxation

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.

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。该项目旨在通过提高聚合电力系统状态估计（PSSE）方法的能力来加强电网的可靠性和鲁棒性，以监测和评估电网的健康状况。 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提供。 电网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，使整个电力系统的操作更好的情况意识。 该项目将与阿肯色州州立大学的推广计划“P-20教育创新中心”合作，分享这一研究成果，并鼓励STEM领域的职业发展。该项目将基于凸松弛的技术扩展到大规模网络物理电力系统，以提高这些系统的监测，分析和可控性。该项目提出了一种有效的凸松弛为基础的方法PSSE问题。 该项目利用半定规划松弛和极值点方法的大型交流（AC）电力系统，提供更严格的边界流量分析比传统的PSSE。该项目还提出了一个分析，显示在电力系统中的某些操作区域之间的过渡，可以用来提供安全和安全保证，然后启动状态转换的不可行性。这种方法可以识别特定类型的稀疏错误数据，这些数据可能是由自然或设计损坏的传感器引起的，并且无法被当前电力系统的不良数据检测算法检测到。所提出的更严格的松弛计划将广泛适用于各种各样的非凸和非线性优化问题，在大型流动系统模型和复杂的优化problems.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。