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EAGER: Collaborative Research: Local Topological Properties of Power Flow Networks, and Their Role in Power System Functionality

EAGER：协作研究：潮流网络的局部拓扑特性及其在电力系统功能中的作用

基本信息

批准号：
1824716
负责人：
Jie Zhang
金额：
$ 12.5万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824716&HistoricalAwards=false
关键词：
EAGER Collaborative Research Local Topological

项目摘要

Failures of power system infrastructure can result in unpredicted load interruptions and severe implications for proper functioning of virtually every aspect of our society, from water, food and fuel supply to transportation control to law enforcement to healthcare, finance, and telecommunication systems. Developing new methods for improving our understanding of hidden mechanisms behind power system vulnerability is, hence, a critical step towards protecting economic stability and human life and facilitating societal resilience on a broad front. Complex networks offer a natural representation of power systems, where generators and substations are specified as vertices and electric lines are sketched as edges. There are generally two main approaches to the analysis of power systems using complex networks. The first approach is based on purely topological properties of a grid network, and the second hybrid approach aims to incorporate electrical engineering concepts, e.g. impedance, maximum power, etc., into the complex network analysis, which typically results in a representation of a grid as a weighted directed graph. Both approaches provide important complementary insights into hidden mechanisms behind functionality of power systems, and neither approach can be viewed as a universally preferred method. This project aims to introduce novel concepts of topological data analysis into studies of power systems that will capitalize on strengths of both complex network tools and electrical engineering concepts. The project will facilitate our understanding of power-flow grids and, more generally, of critical infrastructure functionality, reliability, and robustness, at a local level.This project aims to develop novel procedures for more systematic, data-drivenand geometrically enhanced inference for power flow grids, while accounting both for dynamic higher order topological structure and for electrical engineering characteristics of a grid network, and to study the utility of persistent homologies in amplifying our understanding of hidden mechanisms behind power grid resilience in a broad range of real-world scenarios. Furthermore, the project will examine horizons and limitations of topological data analysis for modeling reliability of power-flow grids and more generally for characterizing and monitoring resilience of critical energy infrastructures to a wide range of risks, including cyber-attacks, natural hazards and random failures.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.

电力系统基础设施的故障可能导致不可预测的负荷中断，并严重影响我们社会几乎每个方面的正常运作，从水、食品和燃料供应到运输控制、执法、医疗保健、金融和电信系统。因此，开发新方法来提高我们对电力系统脆弱性背后隐藏机制的理解，是在广泛领域保护经济稳定和人类生活以及促进社会恢复能力的关键一步。复杂的网络提供了电力系统的自然表示，其中发电机和变电站被指定为顶点，电线被勾画为边缘。通常有两种主要的方法来分析使用复杂网络的电力系统。第一种方法是基于网格网络的纯粹拓扑特性，第二种混合方法旨在将电气工程概念，例如阻抗，最大功率等，纳入复杂的网络分析中，这通常会导致将网格表示为加权有向图。这两种方法都为电力系统功能背后的隐藏机制提供了重要的补充见解，并且这两种方法都不能被视为普遍首选的方法。该项目旨在将拓扑数据分析的新概念引入电力系统的研究，这将充分利用复杂网络工具和电气工程概念的优势。该项目将促进我们对电网的理解，更广泛地说，是对地方一级关键基础设施功能、可靠性和稳健性的理解。该项目旨在开发新的程序，用于更系统，数据驱动和几何增强的潮流电网推断，同时考虑动态高阶拓扑结构和电网的电气工程特征，并研究持久同构的效用，以扩大我们对电网弹性背后隐藏机制的理解在广泛的现实世界场景中。此外，该项目将研究拓扑数据分析的范围和局限性，以建立潮流电网的可靠性模型，更广泛地描述和监测关键能源基础设施对各种风险的恢复能力，包括网络攻击、自然灾害和随机故障。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。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

LFGCN: Levitating over Graphs with Levy Flights

DOI：
10.1109/icdm50108.2020.00109
发表时间：
2020-09
期刊：
2020 IEEE International Conference on Data Mining (ICDM)
影响因子：
0
作者：
Yuzhou Chen;Y. Gel;Konstantin Avrachenkov
通讯作者：
Yuzhou Chen;Y. Gel;Konstantin Avrachenkov

Harnessing the power of topological data analysis to detect change points

DOI：
10.1002/env.2612
发表时间：
2019-12-19
期刊：
ENVIRONMETRICS
影响因子：
1.7
作者：
Islambekov, Umar;Yuvaraj, Monisha;Gel, Yulia R.
通讯作者：
Gel, Yulia R.

On the role of local blockchain network features in cryptocurrency price formation

DOI：
10.1002/cjs.11547
发表时间：
2020-03-18
期刊：
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
影响因子：
0.6
作者：
Dey, Asim K.;Akcora, Cuneyt G.;Kantarcioglu, Murat
通讯作者：
Kantarcioglu, Murat

Assessing the Resilience of the Texas Power Grid Network

评估德克萨斯州电网的弹性

DOI：
10.1109/dsw.2019.8755787
发表时间：
2019
期刊：
the IEEE Data Science Workshop (DSW
影响因子：
0
作者：
Ofori-Boateng, Dorcas;Dey, Asim Kumer;Gel, Yulia R.;Li, Binghui;Zhang, Jie;Poor, H. Vincent
通讯作者：
Poor, H. Vincent

Topological clustering of multilayer networks