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CPS:Medium:Collaborative Research:High-Fidelity High-Resolution and Secure Monitoring and Control of Future Grids: a synergy of AI, data science, and hardware security

CPS：中：协作研究：未来电网的高保真高分辨率和安全监控：人工智能、数据科学和硬件安全的协同作用

基本信息

批准号：
1932501
负责人：
Lang Tong
金额：
$ 75万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932501&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Fidelity

项目摘要

The increasing presence of renewable generations and distributed energy resources in transmission systems heightens the need for fast-timescale situational awareness for system reliability, resiliency, and both the operational and cyber security. Despite the invention of phasor measurement units that promised close-to-real-time monitoring of the system states, the limited deployment of phasor measurement units had hampered the ability of the system operator to uncover trends of instability, react to system contingencies, and detect malicious attacks on the power grid. This research develops new hardware and software solutions for high-fidelity, high-resolution, and secure monitoring and control of the future grid. By harnessing and exploiting the increasingly abundant and diverse data sources and through novel applications of machine learning and artificial intelligence, this research advances the state-of-the-art monitoring of cyber-physical systems in three fronts. First, this research develops machine learning approaches to high-resolution state estimation for power systems that are unobservable by existing phasor measurement units. Second, this research offers new solutions to detecting and mitigating data anomaly caused by malfunctions of sensors, communications systems, and cyber attacks by adversarial agents. Third, this research develops a new hardware architecture and prototypes for future digital substations that provide hardware-based security. This research has broader impacts on enhancing national security in critical infrastructures, promoting economic competitiveness through accelerated adoption of phasor measurement technology, and broadening participation of women and under-represented minority groups in science, technology, engineering, and mathematics.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.

输电系统中可再生能源和分布式能源的日益增多，提高了对快速态势感知的需求，以实现系统可靠性、弹性以及运营和网络安全。尽管相量测量单元的发明有望对系统状态进行接近实时的监控，但相量测量单元的有限部署阻碍了系统运营商发现不稳定趋势、对系统突发事件做出反应以及检测对电网的恶意攻击的能力。这项研究开发了新的硬件和软件解决方案，用于对未来电网进行高保真、高分辨率和安全的监测和控制。通过利用和利用日益丰富和多样化的数据源以及机器学习和人工智能的新颖应用，这项研究在三个方面推进了网络物理系统的最先进监控。首先，这项研究开发了机器学习方法，对现有相量测量单元无法观测的电力系统进行高分辨率状态估计。其次，这项研究提供了新的解决方案来检测和减轻由传感器、通信系统故障和敌对代理网络攻击引起的数据异常。第三，这项研究为未来的数字变电站开发了一种新的硬件架构和原型，提供基于硬件的安全性。这项研究对增强关键基础设施的国家安全、通过加速采用相量测量技术来提高经济竞争力以及扩大妇女和代表性不足的少数群体在科学、技术、工程和数学领域的参与具有更广泛的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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）

Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

DOI：
发表时间：
2020
期刊：
IEEE Transactions on Information Forensics and Security
影响因子：
6.8
作者：
Mulong Luo;A. Myers;G. Suh
通讯作者：
Mulong Luo;A. Myers;G. Suh

Universal Data Anomaly Detection via Inverse Generative Adversary Network

DOI：
10.1109/lsp.2020.2978462
发表时间：
2020-01
期刊：
IEEE Signal Processing Letters
影响因子：
3.9
作者：
Kursat Rasim Mestav;L. Tong
通讯作者：
Kursat Rasim Mestav;L. Tong

Learning the Unobservable: High-Resolution State Estimation via Deep Learning

DOI：
10.1109/allerton.2019.8919782
发表时间：
2019-09
期刊：
2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
影响因子：
0
作者：
Kursat Rasim Mestav;L. Tong
通讯作者：
Kursat Rasim Mestav;L. Tong

MGX: near-zero overhead memory protection for data-intensive accelerators

DOI：
10.1145/3470496.3527418
发表时间：
2020-04
期刊：
Proceedings of the 49th Annual International Symposium on Computer Architecture
影响因子：
0
作者：
Weizhe Hua;M. Umar;Zhiru Zhang;G. Suh
通讯作者：
Weizhe Hua;M. Umar;Zhiru Zhang;G. Suh

SoftVN: efficient memory protection via software-provided version numbers

DOI：
10.1145/3470496.3527378
发表时间：
2022-06
期刊：
Proceedings of the 49th Annual International Symposium on Computer Architecture
影响因子：
0
作者：
M. Umar;Weizhe Hua;Zhiru Zhang;G. Suh
通讯作者：
M. Umar;Weizhe Hua;Zhiru Zhang;G. Suh