PFI-RP: Development of Novel Inverter Technologies and Prototypes for Enhanced Power Generation from Renewable Energy Resources

PFI-RP：开发新型逆变器技术和原型以增强可再生能源发电

• 批准号: 2141067
• 负责人: Shuhui Li
• 金额: $ 55万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141067&HistoricalAwards=false
• 关键词: PFI; RP; Development; Novel; Inverter

The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project is to increase dependable power generation from renewable energy resources and to improve the reliability and stability of the nation’s electric power grid. Technical problems in connecting and controlling renewable energy resources have resulted in many large-scale failures and the interruption of energy production from these resources, jeopardizing the safe and reliable operation of the nation’s electric power system. The proposed technology seeks to overcome the limitations of existing systems, increase reliability for connecting the inverter-based resources to the electric power grid, and allow more renewable energy to supply American homes and businesses. Attaining these goals will increase customer adoption of electric or plug-in hybrid electric vehicles. The technology may increase the competitiveness of US companies in the global inverter, artificial intelligence (AI), and renewable energy markets. The commercial impacts of the project are augmented by an educational outreach plan that will engage student researchers in engineering design problems that integrate and address business-relevant constraints and customer needs.The proposed project aims to translate AI inverter control innovation into a commercial product, process, and/or service. There are two main gaps to overcome in the proposed project. One is a knowledge gap to extend the previous neural-network control technology to practical grid-following and grid-forming applications supporting the operation of inverter-based resources in grid-tied, islanded, and standalone conditions. The other gap is a technical barrier to move the proposed innovation out of the laboratory into real-world systems. Regarding the knowledge gap, research will equip the neural-network control innovation with modules that can support the inverter operation to meet various grid needs. Regarding the technical barrier, research will be performed in this project to enable the developed neural network inverter to satisfy the industry standards. The expected results include prototypes that can be applied to practical electric utility systems and AI-driven inverter technology that can meet strict reliability requirements of the electric power industry. The commercialization plan consists of a set of guided technology assessments and staged milestones with industry partners on commercial potential and business plan iteration.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.

该创新-研究伙伴关系（PFI-RP）项目的更广泛影响/商业潜力是增加可再生能源的可靠发电，并提高国家电网的可靠性和稳定性。在连接和控制可再生能源方面的技术问题导致了许多大规模的故障和这些资源的能源生产中断，危及国家电力系统的安全和可靠运行。该技术旨在克服现有系统的局限性，提高将逆变器资源连接到电网的可靠性，并允许更多的可再生能源供应美国家庭和企业。实现这些目标将增加电动或插电式混合动力电动汽车的客户采用。该技术可能会提高美国公司在全球逆变器，人工智能（AI）和可再生能源市场的竞争力。该项目的商业影响通过教育推广计划得到增强，该计划将使学生研究人员参与工程设计问题，整合并解决与业务相关的限制和客户需求。拟议项目旨在将AI逆变器控制创新转化为商业产品，流程和/或服务。在拟议的项目中有两个主要差距需要克服。一个是知识差距，以扩大以前的神经网络控制技术，以实际的网格以下和网格形成的应用，支持基于逆变器的资源在并网，孤岛和独立的条件下操作。另一个差距是将拟议的创新从实验室转移到现实世界系统的技术障碍。关于知识差距，研究将为神经网络控制创新配备能够支持逆变器操作以满足各种电网需求的模块。对于技术壁垒，本项目将进行研究，以使开发的神经网络逆变器满足行业标准。预期成果包括可应用于实际电力系统的原型和可满足电力行业严格可靠性要求的AI驱动逆变器技术。该商业化计划包括一系列指导性技术评估和与行业合作伙伴就商业潜力和商业计划迭代进行的阶段性里程碑。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Control and Operation Evaluation of Grid-Forming Inverters with L, LC, and LCL Filters

带 L、LC 和 LCL 滤波器的并网逆变器的控制和运行评估

• DOI: 10.1109/pesgm52003.2023.10253060
• 发表时间: 2023
• 期刊: 2023 IEEE Power & Energy Society General Meeting (PESGM
• 作者: Nurunnabi, Md;Li, Shuhui;Das, Himadry Shekhar
• 通讯作者: Das, Himadry Shekhar

Grid Interconnection Modeling of Inverter Based Resources (IBR) Plant for Transient Analysis

• DOI: 10.3390/en16073211
• 发表时间: 2023-04
• 期刊: Energies
• 影响因子: 3.2
• 作者: H. Das;Shuhui Li;Shahinur Rahman

Exploring Dynamic P-Q Capability and Abnormal Operations Associated with PMSG Wind Turbines

• DOI: 10.3390/en16104116
• 发表时间: 2023-05
• 期刊: Energies
• 影响因子: 3.2
• 作者: Shahinur Rahman;Shuhui Li;H. Das;Xingang Fu;H. Won;Yang-Ki Hong

Performance Evolution of Combined Grid-Forming and Grid-Following Inverters with Different Filtering Mechanisms

具有不同滤波机制的并网与并网组合逆变器的性能演变

• DOI: 10.23919/icpe2023-ecceasia54778.2023.10213884
• 发表时间: 2023
• 期刊: 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia
• 作者: Nurunnabi, Md;Li, Shuhui;Mondal, Hahnemann;Hong, Yang-Ki;Choi, Minyeong;Won, Hoyun
• 通讯作者: Won, Hoyun

A Comprehensive P-Q Capability Study for Grid Interconnection of Inverter Based Resources Plant

基于逆变器的资源电厂并网综合P-Q能力研究

• DOI: 10.1109/ias54024.2023.10406385
• 发表时间: 2023
• 期刊: 2023 IEEE Industry Applications Society Annual Meeting (IAS
• 作者: Rahman, Shahinur;Li, Shuhui;Das, Himadry Shekhar
• 通讯作者: Das, Himadry Shekhar