CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms

职业：用于风电场高可靠性监测和控制的概率工具

• 批准号: 1254129
• 负责人: Peter Seiler
• 金额: $ 40万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254129&HistoricalAwards=false
• 关键词: CAREER; Probabilistic; Tools; Reliability; Monitoring

The research objective of this Faculty Early Career Development (CAREER) award is to create probabilistic tools for the design of high reliability systems with application to wind energy. The research focuses specifically on two innovative methods to reduce the costs of wind energy. First, probabilistic techniques will be developed to detect faulty turbine components in a fast and accurate manner. This will enable preventative maintenance or continued operation after selected component faults. Second, tools from stochastic optimal control theory will be applied to design damage-aware control strategies for the wind farm. Currently each turbine within a wind farm is operated to maximize its own captured power while ensuring that structural loads remain within design limits. Stochastic optimal control will be used to perform this trade-off by coordinating all turbines in the wind farm. This will enable a better trade-off between the revenue from power production and the true financial costs that arise due to structural loads. The research will lead to a significant reduction in operating costs for wind turbines thus improving the economic viability of wind energy. In addition, the research will be applicable to high reliability systems in other domains including aerospace, automotive, and medical devices. The research also addresses the need to develop a highly skilled workforce to design and maintain reliable wind turbines. The plan centers on a unique collaboration with a local community college, Mesabi Range, which offers a two-year technical program in wind energy maintenance. The technicians trained at Mesabi Range will benefit from a deeper understanding of the control and monitoring systems they encounter in the field. Students and researchers at the University of Minnesota will benefit from understanding the issues faced by wind turbine technicians. Finally, the research findings will be integrated into the university undergraduate and graduate curriculum.

该教师早期职业发展（CAREER）奖的研究目标是创建概率工具，用于设计应用于风能的高可靠性系统。该研究特别关注两种降低风能成本的创新方法。首先，将开发概率技术，以快速和准确的方式检测故障涡轮机部件。 这将使预防性维护或在选定的部件故障后继续运行成为可能。 其次，从随机最优控制理论的工具将被应用到设计的风电场的损伤感知控制策略。 目前，风力发电场内的每个涡轮机被操作以最大化其自身捕获的功率，同时确保结构负载保持在设计极限内。 随机最优控制将用于通过协调风电场中的所有涡轮机来执行此权衡。这将使电力生产的收入和由于结构负荷而产生的真实财务成本之间实现更好的权衡。该研究将导致风力涡轮机的运营成本大幅降低，从而提高风能的经济可行性。 此外，该研究将适用于其他领域的高可靠性系统，包括航空航天，汽车和医疗设备。 该研究还解决了发展高技能劳动力的需求，以设计和维护可靠的风力涡轮机。该计划的核心是与当地社区学院Mesabi Range的独特合作，该学院提供为期两年的风能维护技术课程。在Mesabi Range接受培训的技术人员将受益于对他们在现场遇到的控制和监测系统的更深入了解。明尼苏达大学的学生和研究人员将受益于了解风力涡轮机技术人员所面临的问题。 最后，研究结果将被整合到大学本科和研究生课程中。