
NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
Recipient: |
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Initial Amendment Date: | August 13, 2018 |
Latest Amendment Date: | August 11, 2021 |
Award Number: | 1824716 |
Award Instrument: | Standard Grant |
Program Manager: |
Lawrence Goldberg
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
Start Date: | September 1, 2018 |
End Date: | August 31, 2022 (Estimated) |
Total Intended Award Amount: | $124,999.00 |
Total Awarded Amount to Date: | $154,105.00 |
Funds Obligated to Date: |
FY 2020 = $29,106.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
800 WEST CAMPBELL RD. RICHARDSON TX US 75080-3021 (972)883-2313 |
Sponsor Congressional District: |
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Primary Place of Performance: |
800 W. Campbell Road, Richardson Richardson TX US 75080-3021 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
GOALI-Grnt Opp Acad Lia wIndus, EPCN-Energy-Power-Ctrl-Netwrks |
Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
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-driven
and 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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The primary focus of this project was to develop a novel methodology that would capitalize on strengths of complex network (CN) analysis, algebraic topology, statistics and electrical engineering (EE) concepts, to enhance our understanding of power grids. The project has resulted in developing a number of new procedures for more systematic, data-driven and geometrically enhanced resilience quantification of power flow networks, while accounting both for dynamic higher order topological structure and for electrical engineering characteristics of power transmission and distribution grids. In addition, the project investigated the utility and limitations of persistent homology and network motifs in amplifying our understanding of hidden mechanisms behind power system functionality, robustness, reliability and stability in a broad range of real-world scenarios.
The project offered a number of unique opportinities for career development of new generations of electrical engineers, statisticians, and data scientists who work at the interface of power system analysis, mathemetical sciences, and machine learning, including joint internship projects with the national labs. The research performed by the mentees involved into the project has been recognized by various international awards. Finally, the special focus has been on broadening participation in STEMs and on advancing career development of future role models in STEM.
Last Modified: 11/30/2022
Modified by: Jie Zhang
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