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.

这项研究的目的是通过使用自适应动态编程神经控制机制来开发智能向量控制技术，智能风能提取和管理策略以及高级模拟和测试机制。 这将提高风能转化系统的效率和可靠性，并增强风能在2030年美国的20％风渗透视觉中的电力系统中的整合。知识分子优胜这项研究的智力优点包括：1）自适应动态编程矢量矢量控制技术，以克服智能媒介控制技术的缺乏，以提高智能效力技术，以提高智能范围的技术效率，2）2）2）2）2）2）在实践系统约束下，4）高级模拟和测试机制和5）智能可持续能源系统的课程，以增强学生在多学科领域的能力。BOADER会影响这项研究的更广泛影响。可持续能源系统的快速发展和增加的复杂性使得开发智能和支持网络的技术以进行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最佳和智能技术，并增加EPSCOR国家在高级科学研究中的参与。这项研究应对美国以下任务领域产生直接或间接的影响：进口能源的减少，减少与能源相关的排放，提高能源效率以及美国高级能源技术的技术领导者。