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：
1824710
负责人：
Harold Vincent Poor
金额：
$ 12.5万
依托单位：
Princeton University
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 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824710&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无法预测的负载中断，并严重影响社会的几乎各个方面，从水，食品和燃料供应到运输控制，再到执法，再到医疗保健，金融和电信系统。因此，开发新的方法来改善我们对电力系统脆弱性背后的隐藏机制的理解，因此是保护经济稳定和人类生活并促进社会韧性在广泛方面的关键步骤。复杂的网络提供了电力系统的自然表示，其中将发电机和变电站指定为顶点和电线为边缘。通常，使用复杂网络分析电源系统的主要方法。第一种方法是基于网格网络的纯拓扑特性，第二种混合方法旨在结合电气工程概念，例如阻抗，最大功率等进入复杂的网络分析，这通常导致网格表示为加权有向图。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互补见解，并且两种方法都不能被视为一种普遍首选的方法。该项目旨在将拓扑数据分析的新颖概念引入电源系统的研究中，这些研究将利用复杂的网络工具和电气工程概念的优势。该项目将促进我们对电力流网格的理解，并在本地一级对关键基础架构功能，可靠性和鲁棒性进行更一般的了解。该项目旨在为更系统的，数据驱动和几何趋势增强功率推论开发新的程序，以开发出新的程序。流动网格，同时考虑动态高阶拓扑结构和电网网络的电气工程特性，并研究持续的同源性在扩大我们对广泛现实世界情景中电网背后的隐藏机制的理解时，可以延续我们对隐藏机制的理解。此外，该项目将检查拓扑数据分析的视野和局限该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响审查标准来评估值得支持。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DC Arc-Fault Detection Based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine

DOI：
10.1109/jsen.2020.3041737
发表时间：
2021-03
期刊：
IEEE Sensors Journal
影响因子：
4.3
作者：
Wenchao Miao;Qi Xu;K. Lam;P. Pong;Senior Member;H. Poor;Life Fellow
通讯作者：
Wenchao Miao;Qi Xu;K. Lam;P. Pong;Senior Member;H. Poor;Life Fellow

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

DOI：
10.1109/tsg.2019.2925405
发表时间：
2017-10
期刊：
IEEE Transactions on Smart Grid
影响因子：
9.6
作者：
Yue Zhao;Jianshu Chen;H. Poor
通讯作者：
Yue Zhao;Jianshu Chen;H. Poor

What network motifs tell us about resilience and reliability of complex networks

DOI：
10.1073/pnas.1819529116
发表时间：
2019-09-24
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Dey, Asim K.;Gel, Yulia R.;Poor, H. Vincent
通讯作者：
Poor, H. Vincent

Moving-Target Defense for Detecting Coordinated Cyber-Physical Attacks in Power Grids

DOI：
10.1109/smartgridcomm.2019.8909767
发表时间：
2019-08
期刊：
2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
影响因子：
0
作者：
Subhash Lakshminarayana;E. V. Belmega;H. Poor
通讯作者：
Subhash Lakshminarayana;E. V. Belmega;H. Poor

PMU Placement Optimization for Efficient State Estimation in Smart Grid