| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | August 14, 2018 |
| Latest Amendment Date: | March 8, 2022 |
| Award Number: | 1807974 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Richard Nash
rnash@nsf.gov (703)292-5394 ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | August 15, 2018 |
| End Date: | July 31, 2023 (Estimated) |
| Total Intended Award Amount: | $397,273.00 |
| Total Awarded Amount to Date: | $397,273.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4202 E FOWLER AVE TAMPA FL US 33620-5800 (813)974-2897 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4202 E Fowler Avenue Tampa FL US 33620-5350 |
| 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): | EPCN-Energy-Power-Ctrl-Netwrks |
| 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
Abstract
Improving the reliability of the electric power grid is one of the critical challenges facing the nation. To meet this challenge, we believe that the Intelligent Sensors and Analyzers advanced technology proposed here can begin to transform our power grid and its affiliated microgrids to a Next-Generation-Smart-Grid. It could also enhance our power and energy sustainability and restorability in severely adverse weather conditions. It has the potential of starting a new revolution in creating intelligent Power Grid and Microgrids by extending todays microchip technology to integrated multi-sensing and information processing in a single compact device -- specialized for Power and Energy arena. Advanced sensor models integrated with Power Grid and Microgrids models will be developed for achieving the true potential of the intelligent sensors and analyzers (proposed herein) that would be deployable on a massive scale. The project will also promote teaching, training and learning in this key technology area. Specifically, the sensors, process-flow, and specialized microchip design activity of the project will be imported into the undergraduate senior-design projects. The project will also significantly benefit several graduate courses including a new course on Smart Power Grids, and existing courses on Micro-Electro-Mechanical-Systems II, and System on a Chip. The advances will be shared with the power and energy industry, government, and academia through a dedicated web site and, of course, through scientific publications.
As stated above, the goal of this project is to improve the reliability and restoration capability of wide area Power Grid as well as its affiliated Microgrids thereby reducing power outages and their cascade effects. Our solution is to embed novel intelligent sensors with distributed processing tools to provide real time monitoring and control of "Power Grid as well as its affiliated Microgrids" from transmission and distribution levels. The problems associated with the present lack of a real time intelligent infrastructure to monitor and control Power Grids -- on a massively distributed basis, have resulted in numerous blackouts and other serious inefficiencies. Our sensors/analyzers will provide accurate information to protective relays, enable intelligent islanding, a means by which the modeling of loads can be based upon accurate measured data rather than estimated data, for load shedding plans. The unique sensor architecture, fabrication, and signal processing issues will be addressed through an advanced Heterogeneous Sensor System on a Chip. As just stated, included will be Microgrids, in which the focus will be on islanding and restoration, sensors, estimation of key variables (frequency, phase and amplitude -- for harmonics as well when desired), defect and fault tolerance, thereby reliability improvement. Microgrids incorporate renewable energy sources, among others. System on a Chip based sensors can provide effective, real-time monitoring and control that is self-healing, for such sources. In short, the main objectives are the following: (1) Multi-Sensor (including Micro-Electro-Mechanical-Systems) design, fab, and test, (2) Intelligence: analog and digital sections design, fab, test (for "Power Grid and Microgrids"), (3) Power Grid and Microgrids objectives: Fault detection, islanding, active & reactive power estimation, theoretical models & simulations, (4) Final Heterogeneous System on a Chip design, fabrication, tests; Also, tests in our Smart Grid Lab.
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 goal of this project is to improve the reliability of power grids through novel intelligent sensors and system-on-a-chip (SoC)-based real-time monitoring and control devices. Our sensors and SoC will provide accurate information, enable real-time system condition estimation, and enable the digital twin technology to monitor and predict power grid stability at real time. Furthermore, real-time mitigation to enhance grid stability has also been developed. The project has two distinct outcomes in designing applications by use of smart sensors and real-time data stream and sensor fabrication.
First, a power plant digital twin has been developed by use of software-defined reconfigurable SoC. The state-of-the-art power system operation is limited to steady-state operation, e.g., generator dispatch, due to the low sampling rate of its data acquisition systems. To be able to predict dynamic stability upon a contingency, it is necessary to have a very different set of tools with the capability of fast and accurate sensing, real-time data streaming, and providing digital replica for dynamic stability evaluation. To this end, this project develops a digital twin for an inverter-based resource power plant that can acquire real-time measurements, calibrate a digital model's parameter based on real-time data streams, and predict stability based on such a model. The digital twin has been developed in NI cRIO-9063 FPGA/DSP chip and its functionalities of real-time monitoring and stability prediction have been fully tested.
The second distinct outcome is smart miniaturized sensors (MEMS), which can tell if there's a problem in the big power lines without needing people to report a power cut. Such sensors do not require contact to the electrical lines and hence require no current transformer or voltage transformer. So far, we've sketched out a bunch of different blueprints for our new sensor gadget. This trick might let us use the gadget in two ways: as a diving board for electricity (that's the two-port resonator) and as a detector for the magnetic 'wind' (the Lorentz force magnetometer). We're also working on making special models for different kinds of tests. Wrapping up, we've been like gadget inventors, tinkering with different designs for tiny devices that can feel the 'push' caused by magnetic 'winds' – these are our special tiny sensors. We made these sensors in a super clean room equipped with semiconductor foundry fabrication tools at the Nanotechnology Research and Education Center at USF. When we tested our tiny creations with some special equipment, they showed us they could do the job pretty well with performance on par with those of prior works. But we're not stopping here. We've got a bunch of designs and tests lined up to see just how good we can make these sensors. That's what we're doing, but with sensors for our smart power grids.
Last Modified: 11/25/2023
Modified by: Zhixin Miao
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