| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | August 20, 2019 |
| Latest Amendment Date: | August 20, 2019 |
| Award Number: | 1932501 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Anthony Kuh
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | September 15, 2019 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $750,000.00 |
| Total Awarded Amount to Date: | $750,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
384 Rhodes Hall, Electrical and Ithaca, NY (ITH) NY US 14853-5401 |
| 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): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
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| 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
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.
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.
This research aims to harness the fruits of the data revolution, AI, and security research in overcoming technological barriers of high-fidelity, high-resolution, and secure monitoring and control of future grids. The research project has contributed to the theoretical foundations of cyber-physical systems and advanced the state-of-the-art methodologies and applications of cyber-physical energy systems.
The research project achieved its objective in three broad areas. On high-resolution sensing, communications, and control of large complex systems, the project developed a novel lossy compression system for wide-area monitoring of the transmission and distribution grids, achieving three to four orders of magnitude compression ratio while satisfying the required fidelity criterion. This research outcome has the potential to provide a communication and network substrate for the next-generation high-resolution continuous-point-on-wave monitoring system, capable of capturing a wide range of transient events currently invisible by the state-of-the-art synchrophasor technology.
On high-fidelity data analytics, the project developed machine-learning data-driven analytics using high-resolution measurements, offering state estimation in unobservable distribution systems, quickest detections of anomalies and emerging and novel trends, and generative probabilistic forecasting of demand, renewable generations, and electricity prices.
On secure computation, the project developed a secure computer architecture for digital substations and remote terminal units, leveraging advances in modern computer architecture designs with hardware security features. In particular, the developed computation architecture offers provably secure features such as timing and correct workflows even in cyber-attacks.
This research has made broader impacts in several directions. First, the developed technology contributes to the reliability, resilience, and security of the nation’s critical infrastructure. Second, the research project contributes to strengthening STEM research and education, enriching undergraduate and graduate curriculum development, and broadening the participation of underrepresented groups in power system research and development.
Last Modified: 12/28/2023
Modified by: Lang Tong
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