Collaborative Research: CISE-MSI: RCBP-RF: CPS: Develop Scalable and Reliable Deep Learning-driven Embedded Control Applied in Renewable Energy Integration

合作研究：CISE-MSI：RCBP-RF：CPS：开发可扩展且可靠的深度学习驱动的嵌入式控制应用于可再生能源集成

• 批准号: 2131175
• 负责人: Letu Qingge
• 金额: $ 13.43万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131175&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; RCBP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Recently deep learning has succeeded mainly in image processing, language processing fields. However, in the real-time control field, deep learning has just started to challenge the dominant role of proportional-integral-derivative controllers in industrial applications, e.g., real-time control of power converters for renewable energy integration. Many urgent problems including training difficulty, the implementation challenges on embedded devices, are curbing deep learning from the development and implementation in embedded control settings. To overcome these difficulties, this project aims to develop novel scalable training algorithms and novel deep neural network controller architectures to fit the strict requirement of embedded control settings.The interdisciplinary project will develop scalable and reliable deep learning-driven embedded control of power converters in real-time for integrating renewable energy such as solar power. Specifically, this project aims (a) to develop scalable, parallel, fast training algorithms for high sampling frequency, and long-time duration trajectory learning using an high performance computing or cloud platform that will significantly reduce training time from several days, even weeks to several hours, (b) to develop novel deep neural network architectures that can be implemented in embedded devices, e.g., Digital Signal Processors / Field-programmable Gate Arrays without compromising the neural network generalizability and extra computing power and storage requirements.The project will build and enhance interdisciplinary and inter-institution collaborations between two Minority Serving Institutions: Texas A&M University-Kingsville and North Carolina A&T State University. The project will attract, retain, and educate more minorities particularly Hispanic, African-American, and female students to attend Ph.D. programs. The developed new training algorithm and new architectures for embedded control can be extended to other fields, e.g., bioinformatics, image, robotics, etc. The developed technologies will result in deep learning-driven intelligent control for grid integration of renewable resources and help solve the urgent need to integrate more renewable energy into the power grid in the United States. The research repository (data, code, simulations, etc.) generated from the project will be deposited with the digital repository at Texas A&M University-Kingsville and North Carolina A&T State University and ensure that the broader computer science and sustainable energy research community have long-term access for a minimum of three years prescribed by the National Science Foundation. Public-use data files can be accessed directly through the project websites ( https://sites.google.com/view/dr-xingang-fu/home and https://sites.google.com/view/letuqingge/home ) via the digital repository on both campuses. Restricted-use data files are distributed after removing potentially identifying information that would significantly impair the analytic potential of the data.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.