Exploiting Physical and Dynamical Structures for Real-time Inference in Electric Power Systems

利用物理和动态结构进行电力系统实时推理

• 批准号: 2246658
• 负责人: Lalitha Sankar
• 金额: $ 36万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246658&HistoricalAwards=false
• 关键词: Exploiting; Physical; Dynamical; Structures; Real

This NSF project aims to improve the capabilities of modern electric power systems. Recent years have seen a dramatic increase in solar and wind power throughout the grid, including inside the distribution system. This increasing penetration of distributed energy resources (DERs), which will only accelerate over time, has tremendous benefits, but also brings challenges, as DERs are substantially different from traditional large-scale generators. This project brings transformative solutions to these challenges to improve the situational awareness of the DERs throughout the system, by leveraging advanced high-fidelity sensors as well as modern data science. The intellectual merits of the project include methods to extract useful information from even a small number of high-fidelity sensors; this information can be used to make rapid control decisions to improve the stability of the overall system without relying on traditional generators. The broader impacts of the project include mentoring of graduate students and postdocs, in addition to undergraduate researchers, including and especially underrepresented minorities, via the Arizona State University (ASU) Summer Undergraduate Research Initiative (SURI).This project studies three main problem areas: (i) estimation of the topology of distribution networks that connect the grid edge to the bulk network; (ii) faster detection and localization of forced oscillations, which are typically caused by disturbances or devices failures and can pose significant threats to power system operations; and (iii) learning the parameters of DER dynamics, including inertia and damping, so as to maintain refined dynamic models which can be used by system operators to assess and ensure system stability. The project addresses these challenges via the unifying framework of structure: the graphical structure of grid topology, sparsity in the location of an oscillation source, and structure in dynamic models. The project exploits these structural elements to develop new algorithms to extract situational awareness from high-fidelity meters, especially Phasor Measurement Units (PMUs). Project outcomes include theoretical results as well as numerical experiments on the performance of these algorithms in realistic settings of power systems.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.

NSF的这个项目旨在提高现代电力系统的能力。近年来，整个电网中的太阳能和风能急剧增加，包括配电系统内部。随着时间的推移，分布式能源（DER）的渗透率越来越高，带来了巨大的好处，但也带来了挑战，因为DER与传统的大型发电机有很大的不同。该项目为这些挑战带来了变革性的解决方案，通过利用先进的高保真传感器和现代数据科学，提高整个系统的DER的态势感知能力。该项目的智力优势包括从少量高保真传感器中提取有用信息的方法;这些信息可用于快速控制决策，以提高整个系统的稳定性，而无需依赖传统发电机。该项目的更广泛影响包括指导研究生和博士后，以及本科生研究人员，包括特别是代表性不足的少数民族，通过亚利桑那州州立大学（ASU）夏季本科生研究计划（SURI）。（ii）更快地检测和定位受迫振荡，受迫振荡通常由干扰或设备故障引起，并且可以对电力系统操作构成重大威胁;以及（iii）学习DER动态的参数，包括惯性和阻尼，以便保持系统操作员可以使用的精细动态模型，以评估和确保系统稳定性。该项目通过统一的结构框架来解决这些挑战：网格拓扑的图形结构，振荡源位置的稀疏性以及动态模型中的结构。该项目利用这些结构元素来开发新的算法，以从高保真仪表，特别是相量测量单元（PMU）中提取态势感知。项目成果包括理论结果以及数值实验的性能，这些算法在现实环境中的电力system.This奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的智力价值和更广泛的影响审查标准。