Collaborative Research: Wind Power - Neural Network Control, Multidisciplinary Integration, and Advanced Simulation

合作研究：风电-神经网络控制、多学科集成和高级仿真

• 批准号: 1102038
• 负责人: Shuhui Li
• 金额: $ 18万
• 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: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1102038&HistoricalAwards=false
• 关键词: Collaborative; Research; Wind; Power; Neural

The objective of this research is to develop intelligent vector control technology, intelligent wind power extraction and management strategy, and advanced simulation and testing mechanisms by using Adaptive Dynamic Programming neural control mechanisms. This will improve the efficiency and reliability of wind energy conversion systems and enhance wind power integration into the electric utility system for the 20% wind penetration vision of the United States in 2030. INTELLECTUAL MERITS The intellectual merits of this research include development of: 1) Adaptive Dynamic Programming vector control technology to overcome the deficiency of conventional vector control technology, 2) intelligent wind power extraction and management techniques to improve wind power production efficiency and reliability, 3) intelligent control integration strategy under practical system constraints, 4) advanced simulation and testing mechanisms, and 5) a curriculum in Intelligent Sustainable Energy Systems to enhance student capability in multidisciplinary fields.BROADER IMPACTSThe broader impacts of this research are significant. The rapid development and increased complexities of sustainable energy systems make it increasingly urgent to develop intelligent and cyber-enabled technologies for research and education of sustainable energy system field. This research will enhance optimal and intelligent technology for future smart and sustainable energy systems and increase the participation of an EPSCOR state in advanced scientific research. The research should have direct or indirect impacts to the following mission areas of the United States: reduction in imported energy, reduction of energy-related emissions, improvements in energy efficiency, and a technological lead for the United States in advanced energy techniques.

本研究的目的是利用自适应动态规划神经控制机制，开发智能矢量控制技术、智能风电提取和管理策略以及先进的仿真和测试机制。这将提高风能转换系统的效率和可靠性，并加强风力发电与电力公用事业系统的整合，以实现美国2030年风电渗透率达到20%的愿景。本研究的学术价值包括：1)自适应动态规划矢量控制技术，克服传统矢量控制技术的不足；2)智能风电提取和管理技术，提高风电生产效率和可靠性；3)实际系统约束下的智能控制集成策略；4)先进的仿真和测试机制；5)智能可持续能源系统课程，增强学生多学科能力。更广泛的影响本研究的更广泛的影响是显著的。随着可持续能源系统的快速发展和复杂性的增加，开发智能和网络技术用于可持续能源系统领域的研究和教育变得越来越迫切。这项研究将增强未来智能和可持续能源系统的优化和智能技术，并增加EPSCOR国家对先进科学研究的参与。这项研究应对美国的下列任务领域产生直接或间接影响：减少进口能源，减少与能源有关的排放，提高能源效率，并使美国在先进能源技术方面处于技术领先地位。