The University of South Florida was awarded a $299,717 project grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems to develop a graph signal processing framework for situational awareness in smart grids. The goal of this three-year project grant, awarded September 1, 2021 and set to be completed by August 31, 2024, is to improve situational awareness capabilities for smart grid operators through new signal processing techniques applied to...
The National Science Foundation (NSF) awarded a $484,965 project grant under the Engineering (CFDA 47.041) program to New York University (NYU) to develop transformative concepts and methodologies to enhance situational awareness of electric power distribution systems. The project aims to address challenges in integrating distributed renewable energy generation by enabling real-time tracking of distribution system operating states. Key objectives include learning-based continuous-time system...
The National Science Foundation (NSF) awarded a $250,000 Project Grant to Carnegie Mellon University (CMU) under the Engineering program (CFDA 47.041). The grant supports the development of a "Collaborative Research: Scalable Circuit Theoretic Framework for Large Grid Simulations and Optimizations" project. The project aims to create a generalized distributed framework for solving large-scale power grid problems that are both fast and robust, enabling transformative changes in future...
This $100,000 National Science Foundation Project Grant supports research at the University of Washington to develop a new data-driven power systems control framework with stability guarantees. Funded under the NSF Engineering program (CFDA 47.041), the research aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability assurances. Over the two-year period from March 2022 to February 2025, university...
The National Science Foundation (NSF) awarded a $199,940 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Georgia Research Foundation, Inc. (UGA Research Foundation) for the project titled "AMPS: Scalable Graph Models for Anomaly Detection in Large-Scale Smart Grids." The project aims to develop advanced, computationally efficient data-driven algorithms for real-time anomaly detection and diagnosis in smart power grids, which are...
This $260,000 National Science Foundation project grant will fund the development of data-enabled modeling, monitoring, and optimization algorithms targeting power system dynamics from 2022-2025. The University of Texas at Austin, through its parent organization the University of Texas System, will receive funding under the NSF Engineering program (CFDA 47.041) to correlate synchrophasor data and develop Gaussian process and stability-aware optimal power flow tools. Key outcomes will include...
The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Engineering program (CFDA 47.041) to the University of Vermont & State Agricultural College (UVM) to develop a generalized distributed framework for solving large-scale power grid problems. The project aims to advance the state-of-the-art in nonlinear programming, physics-inspired graph-partitioning, and combinatorial optimization to enable fast and robust simulations and optimizations of the future power...
This Project Grant from the National Science Foundation's $200,000 Engineering program (CFDA 47.041) will fund research at the University of California, San Diego to develop a new data-driven power systems control framework with stability guarantees. The three-year award beginning March 2022 aims to design reinforcement learning algorithms for inverter-based frequency and voltage control of power grids that provide formal stability through a novel approach bridging Lyapunov control theory and...
Lehigh University was awarded a $431,508 project grant from the National Science Foundation to develop data-driven algorithms for dynamic state-estimation of modern power systems. The three-year award, issued under the NSF's Engineering program (CFDA #47.041), aims to transform dynamic state-estimators through the use of machine learning and signal processing techniques applied to available measurements. This will generate new knowledge on monitoring modern power systems and improve smart grid...
This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems Project Grant awarded to Vanderbilt University, with a CFDA number of 47.041, provides $239,999 in funding from September 1, 2023 to August 31, 2026. The project aims to develop a new paradigm grounded in networked control system theory to represent graph data for effective graph machine learning. By modeling graphs as controlled networked dynamical systems, the project designs graph representations...