权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Learning Power System Graph Signals for Cascade Resiliency

职业：学习电力系统图形信号以实现级联弹性

基本信息

批准号：
2238658
负责人：
Mia Naeini
金额：
$ 50.92万
依托单位：
University of South Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2028-02-29
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238658&HistoricalAwards=false
关键词：
CAREER Learning Power System Graph

项目摘要

This NSF CAREER project aims to improve the resiliency of smart transmission grids to cascading failures. Cascading failures in power grids are successive interdependent failures of components, which can lead to large blackouts with significant societal and economical impacts. The project will bring transformative change in supporting operation and mitigation functions during and before cascading failures through various graph-empowered predictive, descriptive and prescriptive analyses. These will be achieved by bridging the gap between graph-based and data-driven modeling and analyses of cascading failures in power grids. The intellectual merits of the project include developing a graph signal learning framework for analyzing cascading failures in power systems using graph signal processing (GSP) and graph-empowered machine learning techniques. The broader impacts of the project include an integrated education and workforce training component to foster interdisciplinary training in the area of energy data analytics through new course developments and mentoring and advising efforts with the help of industry and academic partners, as well as efforts to increase participation of underrepresented students in STEM disciplines.This project will develop new methodologies to enhance the reliability of power transmission grids to cascading failures by integrating the system’s structural and components interaction data along with temporal data, capturing dynamics of the states, in the form of graph signals. The proposed research will make new discoveries in the dynamics and properties of power systems’ graph signals during cascading failures through vertex domain, graph-frequency domain, and the joint vertex-frequency domain analyses and through modeling power system dynamics in a GSP framework using tools including graph filters. The analyses through the proposed framework will enable developing new techniques for detecting the proximity to cascade transition and identifying areas for protective control. Moreover, graph-empowered machine learning techniques, including graph neural networks, will be developed to learn the signatures and patterns of cascade stresses in various graph signal domains and to develop tools to support optimizing corrective and preventive decisions for improving cascade resiliency.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 CAREER项目旨在提高智能输电网对级联故障的弹性。电网中的级联故障是指各部件相继发生的相互依赖的故障，可能导致大面积停电，对社会和经济造成重大影响。该项目将通过各种图形支持的预测性、描述性和规范性分析，在级联故障期间和之前为支持操作和缓解功能带来革命性的变化。这些将通过弥合基于图和数据驱动的建模和电网级联故障分析之间的差距来实现。该项目的智力优势包括开发一个图信号学习框架，用于使用图信号处理（GSP）和图授权机器学习技术分析电力系统中的级联故障。该项目的更广泛影响包括综合教育和劳动力培训部分，通过新的课程开发，在行业和学术合作伙伴的帮助下指导和建议工作，促进能源数据分析领域的跨学科培训，以及努力增加代表性不足的学生参与STEM学科。该项目将开发新的方法，通过集成系统的结构和组件交互数据以及时间数据，以图形信号的形式捕获状态的动态，来提高输电网级联故障的可靠性。本研究将通过顶点域、图频域和联合顶点频域分析，以及使用包括图滤波器在内的工具在GSP框架中对电力系统动力学进行建模，对级联故障过程中电力系统图信号的动力学和特性进行新发现。通过提出的框架进行的分析将使开发新技术能够检测级联过渡的接近性并确定保护控制的区域。此外，将开发基于图的机器学习技术，包括图神经网络，以学习各种图信号域中级联应力的特征和模式，并开发工具来支持优化纠正和预防决策，以提高级联弹性。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。