Award Abstract # 1824716
EAGER: Collaborative Research: Local Topological Properties of Power Flow Networks, and Their Role in Power System Functionality

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: UNIVERSITY OF TEXAS AT DALLAS
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 2018 = $124,999.00
FY 2020 = $29,106.00
History of Investigator:
  • Jie Zhang (Principal Investigator)
    jiezhang@utdallas.edu
  • Yulia Gel (Former Principal Investigator)
Recipient Sponsored Research Office: University of Texas at Dallas
800 WEST CAMPBELL RD.
RICHARDSON
TX  US  75080-3021
(972)883-2313
Sponsor Congressional District: 24
Primary Place of Performance: University of Texas at Dallas
800 W. Campbell Road, Richardson
Richardson
TX  US  75080-3021
Primary Place of Performance
Congressional District:
24
Unique Entity Identifier (UEI): EJCVPNN1WFS5
Parent UEI:
NSF Program(s): GOALI-Grnt Opp Acad Lia wIndus,
EPCN-Energy-Power-Ctrl-Netwrks
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 019Z, 092E, 1504, 7916
Program Element Code(s): 150400, 760700
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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(Showing: 1 - 10 of 20)
Abay, Nazmiye Ceren and Akcora, Cuneyt Gurcan and Gel, Yulia R and Kantarcioglu, Murat and Islambekov, Umar D. and Tian, Yahui and Thuraisingham, Bhavani "ChainNet: Learning on Blockchain Graphs with Topological Features" 2019 IEEE International Conference on Data Mining (ICDM) , 2019 https://doi.org/10.1109/ICDM.2019.00105 Citation Details
Akcora, Cuneyt and Gel, Yulia and Kantarcioglu, Murat and Lyubchich, Vyacheslav and Thuraisingam, Bhavani "GraphBoot: Quantifying Uncertainty in Node Feature Learning on Large Networks" IEEE Transactions on Knowledge and Data Engineering , 2019 10.1109/TKDE.2019.2925355 Citation Details
Akcora, Cuneyt G. and Bakdash, Jonathan Z. and Gel, Yulia R. and Kantarcioglu, Murat and Marusich, Laura R. and Thuraisingham, Bhavani "Attacklets: Modeling High Dimensionality in Real World Cyberattacks" Proceedings of the 2018 IEEE International Conference on Intelligence and Security Informatics (ISI) , 2018 10.1109/ISI.2018.8587399 Citation Details
Akcora, Cuneyt G. and Li, Yitao and Gel, Yulia R. and Kantarcioglu, Murat "BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain" Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence , 2020 https://doi.org/10.24963/ijcai.2020/612 Citation Details
Bush, B. "Topological machine learning methods for power system responses to contingencies" Proceedings of the Innovative Applications of Artificial Intelligence Conference , 2021 https://doi.org/ Citation Details
Chen, Y. and Coskunuzer, B. and Gel, Y.R. "Topological Relational Learning on Graphs" Advances in neural information processing systems , 2021 Citation Details
Chen, Y. and Gel, Y.R. and Poor, H.V. "BScNets: BlockSimplicialComplexNeuralNetworks" Proceedings of the AAAI Conference on Artificial Intelligence , 2022 Citation Details
Chen, Y. and Marchetti, Y. and Sizikova, E. and Gel, Y.R. "TCN: Pioneering Topological-based Convolutional Networks for Planetary Terrain Learning" Proceedings of the AAAI Conference on Artificial Intelligence , 2022 Citation Details
Chen, Yuzhou and Gel, Yulia and Lyubchich, Vyacheslav and Nezafati, Kusha "Snowboot: Bootstrap Methods for Network Inference" The R Journal , v.10 , 2019 10.32614/RJ-2018-056 Citation Details
Chen, Yuzhou and Gel, Yulia R. and Avrachenkov, Konstantin "LFGCN: Levitating over Graphs with Levy Flights" 2020 IEEE International Conference on Data Mining (ICDM) , 2020 https://doi.org/10.1109/ICDM50108.2020.00109 Citation Details
Chen, Yuzhou and Jiang, Tian and Heleno, Miguel and Moreira, Alexandre and Gel, Yulia R. "Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets" The 2022 IEEE International Conference on Big Data (IEEE BigData 2022) , 2022 Citation Details
(Showing: 1 - 10 of 20)

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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