PFI:AIR - TT: Toward Commercialization: Development of Neural Network Control and Power Converter Prototype for Renewables and Smart Grid Integration

该PFI：AIR技术翻译项目的重点是翻译神经网络矢量控制技术，以满足可再生能源和智能电网控制和集成的需求。这项技术非常重要，因为它将提高电力质量，促进不间断的能源供应，增加消费者使用可再生能源和电动汽车的动力，并加速实现美国到2030年从可再生资源中获得20%电能的目标。该项目将产生一个使用神经网络矢量控制技术的并网功率转换器的原型。该技术具有以下独特的特点：快速响应时间，低超调和接近理想的控制性能。与该市场领域领先的传统标准矢量控制技术相比，这些特征提供了具有集成可再生能源的电力公用事业系统的效率、可靠性、稳定性和电能质量提高的优势。 该项目解决了以下技术差距，因为它从研究发现转化为商业应用：（1）证明用于满足电力系统需求的并网转换器的神经网络控制技术的概念，（2）展示使用神经网络控制将可再生能源集成到智能电网中的功能原型电力转换器板，（3）评估和基准测试神经网络控制的商业价值解决方案与传统技术，以及（4）制定商业化战略超越这个项目。此外，参与该项目的人员，PI，本科生和研究生，将通过技术和商业角度的活动获得创新翻译经验。学生团队将与PI一起沿着参加Crimson Startup Canvas，这是UA的客户发现计划，以了解通过该计划确定可持续商业模式的正式流程，增加吸引资金和投资的机会，创造新的就业机会并造福社会。该项目聘请南方公司服务参与顾问角色，为项目提供指导，并在从研究发现到商业现实的技术转化过程中引导商业化方面。

This PFI: AIR Technology Translation project focuses on translating a neural network vector control technology to fill the need for renewable and smart grid control and integration. The technology is important because it will improve the power quality and facilitate an uninterrupted energy supply, increase incentives for consumers to use energy from renewable resources and electric vehicles, and accelerate progress towards America's target of deriving 20% of its electrical energy from renewable resources by 2030. The project will result in a prototype of a grid-connected power converter using the neural network vector control technology. This technology has the following unique features: fast response time, low overshoot and close to ideal control performance. These features provide the advantages of improved efficiency, reliability, stability and power quality of an electric utility system with integrated renewables as compared to the leading competing conventional standard vector control technology in this market space. This project addresses the following technology gaps as it translates from research discovery toward commercial application: (1) proving the concept of the neural network control technology for grid-connected converters that meets the needs of electric power systems, (2) demonstrating a functional prototype power converter board that uses neural network control to integrate renewables into smart grids, (3) evaluating and benchmarking a commercially valuable solution of neural network control against conventional technology, and (4) developing a strategy for commercialization beyond this project. In addition, personnel involved in this project, PIs, undergraduates and graduates, will receive innovation translation experiences through activities from technical and business perspectives. The team of students, along with the PI, will participate in the Crimson Startup Canvas, UA's customer discovery program to understand the formal process through which to identify sustainable business models, increase the chance of attracting funding and investments, create new jobs and benefit society.The project engages Southern Company Services to participate in an advisor role, offer guidance for the project, and guide commercialization aspects in this technology translation effort from research discovery toward commercial reality.