Project Grant DESC0018727

This Cooperative Agreement award from the U.S. Department of Energy's Office of Fossil Energy and Carbon Management (CFDA 81.089 - Fossil Energy Research and Development) provides $625,066 to develop and test a wireless artificial intelligence (AI)-powered multi-functional fiber optic sensor system for gasification-based systems. The award aims to create a sensor capable of simultaneously measuring high temperature, pressure, strain, hydrogen concentration, carbon monoxide concentration,...

This Cooperative Agreement award from the Department of Energy's Office of Cybersecurity, Energy Security, and Emergency Response (CESER) program (CFDA 81.008) provides $5,583,698.00 to the Electric Power Research Institute Inc. (EPRI) to develop an integrated, automated system to identify third-party and potential zero-day vulnerabilities, verify their exploitability dynamically, and create a feedback loop for rapid enhancement of static analysis capabilities. The objectives of this 3-year...

This $199,339 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the California State University San Marcos Corp (CSUSM) will develop novel AI and machine learning models for supervisory control of wind farm connections to the electric grid for stability monitoring. The project aims to create innovative AI/ML models that can directly analyze raw power data to enable accurate fault prediction and detection, which...

This Project Grant award from the National Science Foundation (NSF) Integrative Activities (CFDA 47.083) program provides $286,612 to the University of Nevada, Las Vegas (UNLV) to develop an explainable AI-supported performance monitoring system for distributed sustainable energy networks. The project aims to improve the reliability of sustainable energy systems, such as solar and wind power, by designing a framework to detect and classify anomalies using multi-modal learning and explainable...

This federal Cooperative Agreement award, valued at $704,588 and issued by the U.S. Department of Energy's Office of Electricity (CFDA 81.122 Electricity Research, Development and Analysis), aims to develop and demonstrate a graph-based sensor data analytics tool for grid stability monitoring and control. The goal of this 3-year project, awarded to North Dakota State University, is to leverage innovative graph-based techniques with multiple sensor data sets to enable grid operators to monitor...

This National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program grant award provides $508,455 to the University of Washington to support research that will leverage artificial intelligence technologies to enhance the resilience and efficiency of automated control systems in energy infrastructure. The project aims to develop an expert-guided, distributionally robust optimization framework that integrates reinforcement learning with mathematical optimization to improve...

This $400,000 Project Grant awarded by the National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems aims to revolutionize the design of learning-enabled, safety-critical systems, with a focus on power systems. The project, titled "COLLABORATIVE RESEARCH: SLES: SAFETY UNDER DISTRIBUTIONAL SHIFT IN LEARNING-ENABLED POWER SYSTEMS", will develop proactive, antifragile systems that can anticipate and adapt to changes, utilize multi-agent systems for...

This National Science Foundation project grant of $300,000 supports research at the University of Tulsa to develop a decentralized artificial intelligence framework for distribution system fault detection, identification, and power restoration. Under the NSF Engineering program (CFDA 47.041), the university will create a graph capsule network to recognize spatial and temporal patterns in distribution systems and identify fault types and locations. Researchers will also devise a novel...

This Project Grant award from the National Science Foundation (NSF) Integrative Activities (CFDA 47.083) program provides $300,000 in funding to Mississippi State University (MSU) to develop a reliable and transparent predictive condition monitoring system for distributed wind energy in rural areas using permissioned blockchain technology. The key objectives of this 2-year project are to: (1) create collaborative federated learning algorithms for condition monitoring to enable privacy-preserving...

The Department of Energy's Office of Energy Efficiency and Renewable Energy awarded a $5,500,231 Cooperative Agreement to Clemson University to design, develop, prototype, and validate a novel AI-enabled, photoacoustic imaging (PAI) based well-logging tool for comprehensive evaluation of the integrity of geothermal wells at high temperature and high pressure. The project aims to deliver a high-fidelity, complete inspection tool compatible with current conveyance methods that can provide a...