ASCENT: Ultra-Compact PV Microinverters with Integrated Active Power Decoupling

This NSF project aims to investigate a new family of energy conversion systems for residential and commercial solar photovoltaic systems. The project will bring transformative changes to the size and weight of solar energy conversions systems. This will be achieved by innovative circuit topologies, unique control, novel sensors, and optimized electro-thermal co-design. The intellectual merits of the project include fundamental research for scientific understanding of proposed innovative solar micro-inverters with substantially higher power densities and specific powers. The broader impacts of the project include high quality integration of research and education in power electronics to meet the emerging workforce and educational needs of the U.S. energy industry by educating young and talented students in strategic fields of renewable energy conversion systems. The team plans to recruit and train a broad group of talented students for this research project. The results will be disseminated through professional conferences and reputable journals to enhance scientific and educational achievements. The project will also facilitate widespread adoption of solar energy systems. This project aims at introducing transformative solar micro-inverters enabled by novel single-stage, dual-active-bridge structures, and integrated active power decoupling control enhanced by unique high bandwidth sensors and innovative machine learning. Theoretical advancements in ultra-compact and highly efficient wide bandgap Gallium Nitride power electronic interfaces will be achieved. Innovative high bandwidth isolated current sensors capable of handling high slew-rate common mode voltage in a switch-node will be introduced. The work will result in novel machine learning, neural network control, and modulation techniques for wide bandgap-based converters. This project involves multidisciplinary research in power electronics, sensing circuits, thermal management, electro-thermal co-design, machine learning, neural network control, reliability, and design for manufacturing. The results are envisioned to lead to major breakthroughs in control, modeling, sensing, design, and reliability of power electronic interfaces for solar energy conversion systems. 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.