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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。最近，深度学习主要在图像处理和语言处理领域取得了成功。然而，在实时控制领域，深度学习才刚刚开始挑战比例-积分-导数控制器在工业应用中的主导地位，例如可再生能源集成的电力变流器的实时控制。许多亟待解决的问题，包括培训困难、嵌入式设备上的实现挑战，阻碍了嵌入式控制环境下的深度学习的开发和实施。为了克服这些困难，该项目旨在开发新型可扩展训练算法和新型深度神经网络控制器体系结构，以适应嵌入式控制设置的严格要求，该跨学科项目将开发可扩展和可靠的深度学习驱动的实时电力变流器嵌入式控制，以整合太阳能等可再生能源。具体地说，这个项目的目标是(A)开发可扩展的、并行的、快速的训练算法，用于使用高性能计算或云平台进行高采样频率和长持续时间的轨迹学习，这将显著地将训练时间从几天、甚至几周减少到几个小时，(B)开发可以在嵌入式设备中实现的新型深度神经网络结构，例如数字信号处理器/现场可编程门阵列，而不损害神经网络的普适性以及额外的计算能力和存储需求。该项目将建立和加强两家少数族裔服务机构之间的跨学科和机构间合作：德克萨斯A&AMP；M大学-金斯维尔和北卡罗来纳A&T州立大学。该项目将吸引、留住和教育更多的少数族裔，特别是西班牙裔、非裔美国人和女性学生参加博士项目。所开发的嵌入式控制的新训练算法和新架构可扩展到其他领域，如生物信息学、图像、机器人等。所开发的技术将导致深度学习驱动的电网集成可再生资源智能控制，并有助于解决美国电网中整合更多可再生能源的迫切需求。研究库(数据、代码、模拟等)该项目产生的数据将存放在德克萨斯农工大学金斯维尔分校和北卡罗来纳农工州立大学的数字储存库，并确保更广泛的计算机科学和可持续能源研究界至少在国家科学基金会规定的三年内长期访问。可通过两个校区的数字储存库通过项目网站(https://sites.google.com/view/dr-xingang-fu/home和https://sites.google.com/view/letuqingge/home)直接获取公共使用的数据文件。限制使用的数据文件是在删除了可能会严重损害数据分析潜力的潜在识别信息后分发的。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.