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

AMPS: Collaborative Research: Analysis of Local Power Grid Properties: From Network Motifs to Tensors

AMPS：协作研究：本地电网特性分析：从网络主题到张量

基本信息

批准号：
1736417
负责人：
Harold Vincent Poor
金额：
$ 11.5万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736417&HistoricalAwards=false
关键词：
AMPS Collaborative Research Analysis Local

项目摘要

Power grids are a critical infrastructure upon which society relies for effective operation of many other pursuits, including commerce, healthcare, transportation, etc. The list is quite long, as electricity use pervades almost every aspect of human activity. Thus, methods for improving the resilience and security of the energy infrastructure are of deep interest to society and have substantial potential impact on its proper functioning across a very broad front. The past decade has seen an increasing interest in the application of tools developed in the interdisciplinary field of complex network analysis to improve our understanding of power system behavior. Indeed, a power grid can be naturally described as a graph where nodes represent, e.g., transformers, substations or generators, and edges represent electrical connections. Methods of complex network analysis have provided new insights into the fundamental and intrinsic characteristics of power system vulnerability and the effectiveness of associated risk mitigation strategies. However, knowledge on the role of local network structures in functionality of power systems yet remains limited. The project develops a new methodology to enhance our understanding on a functional role of various local network features in unveiling hidden mechanisms behind vulnerability of real power systems and their dynamic response to malfunctions. This project contemplates a new approach to analysis of the local topological properties of power grids via network motifs, which are smaller recurrent patterns occurring in network structure. This methodology provides a basis for understanding linkages between higher-order network topologies and functionality of power grids. The proposed formulation presents a broad platform for the development both of new analytical methods for characterizing power system stability, vulnerability and resilience, and of algorithms for detecting faults, attacks and other anomalies in power grids. Furthermore, the project provides opportunities for strengthening our understanding of the role of motifs more generally in the study of network behavior that may be applied more broadly in a variety of cyber-physical settings.

电网是社会许多其他活动有效运行所依赖的关键基础设施，包括商业、医疗保健、交通运输等。清单相当长，因为电力消耗几乎渗透到人类活动的方方面面。因此，改善能源基础设施的复原力和安全性的方法对社会极为重要，并对其在非常广泛的战线上的正常运作具有重大的潜在影响。在过去的十年里，在复杂网络分析的跨学科领域中开发的工具的应用越来越引起人们的兴趣，以提高我们对电力系统行为的理解。事实上，电网可以自然地描述为一个图，其中节点表示例如变压器、变电站或发电机，而边表示电气连接。复杂网络分析方法对电力系统脆弱性的基本和内在特征以及相关风险缓解策略的有效性提供了新的见解。然而，关于本地网络结构在电力系统功能中的作用的知识仍然有限。该项目开发了一种新的方法，以加强我们对各种本地网络特征在揭示实际电力系统脆弱性背后的隐藏机制及其对故障的动态响应方面的功能作用的理解。该项目设想了一种通过网络主题分析电网局部拓扑特性的新方法，网络主题是发生在网络结构中的较小的循环模式。这种方法为理解高阶网络拓扑和电网功能之间的联系提供了基础。提出的公式为发展新的电力系统稳定性、脆弱性和恢复力的分析方法，以及检测电网中的故障、攻击和其他异常的算法提供了广阔的平台。此外，该项目提供了机会，以加强我们对主题在网络行为研究中的作用的更一般理解，这些网络行为可能在各种网络物理环境中得到更广泛的应用。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Motif-based analysis of power grid robustness under attacks

基于基序的攻击下电网鲁棒性分析

DOI：
10.1109/globalsip.2017.8309114
发表时间：
2017
期刊：
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing
影响因子：
0
作者：
Dey, Asim Kumer;Gel, Yulia R.;Poor, H. Vincent
通讯作者：
Poor, H. Vincent

Stealth Attacks on the Smart Grid

DOI：
10.1109/tsg.2019.2935353
发表时间：
2018-08
期刊：
IEEE Transactions on Smart Grid
影响因子：
9.6
作者：
Ke Sun;I. Esnaola;S. Perlaza;H. V. Poor
通讯作者：
Ke Sun;I. Esnaola;S. Perlaza;H. V. Poor

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

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

PMU Placement Optimization for Efficient State Estimation in Smart Grid