Regularized Learning Enabled Monitoring and Control for Wind Power Systems

风电系统的常规学习监控和控制

基本信息

批准号：
1362513
负责人：
Eunshin Byon
金额：
$ 32.5万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-01 至 2018-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362513&HistoricalAwards=false
关键词：
Regularized Learning Enabled Monitoring Control

项目摘要

The objective of this project is to develop new monitoring and control strategies for enhancing wind turbine reliability so that operations and maintenance costs of wind energy can be reduced. In wind power systems, the "wind input"-to-"turbine response" relationship is nonstationary, due to both internal (e.g., system's degradation) and external (e.g., surface contamination on blades) changes. This nonstationary dependency causes significant technological challenges in managing the health and performance of wind turbines. This project will develop a new regularized learning method to characterize the time-varying dependency among system variables so that changes in a turbine system can be tracked and predicted. Subsequently, a statistical monitoring method with adaptive control limits will be devised to signal the occurrence of anomalies. Based on the results from the regularized learning process, an adaptive control strategy will be developed to mitigate excessive and undesired mechanical stresses on turbine subsystems in an effort to prevent or slow the deterioration process. A new wireless structural health monitoring (SHM) system supporting real-time, embedded data processing will be advanced to track the behavior, performance and health of operational turbines. The outcomes of this research will facilitate the wind industry's smooth transition from using rudimentary diagnosis and control techniques to the use of sophisticated and integrative monitoring and control technologies. The new monitoring method will enable timely detection of anomalies while reducing the false alarms. Optimally determined control parameters will balance between power production and stress levels in an effort to extend a turbine?s service life. While using wind turbines as the primary application target, the methodology is applicable to other engineering systems subject to dynamic operating conditions including civil infrastructure systems. This project will contribute toward the preparation of a future workforce in the field of renewable energy and sustainability through an array of mechanisms including the integration of under-represented students in the STEM field into renewable energy research, opportunities for students to interact with national laboratories, and to be engaged with other domestic and international research groups.

该项目的目标是开发新的监测和控制策略，以提高风力涡轮机的可靠性，从而降低风能的运营和维护成本。在风力发电系统中，由于内部（例如系统退化）和外部（例如叶片表面污染）的变化，“风输入”与“涡轮机响应”关系是不稳定的。这种非平稳依赖性给管理风力涡轮机的健康和性能带来了重大的技术挑战。该项目将开发一种新的正则化学习方法来表征系统变量之间的时变依赖性，以便跟踪和预测涡轮机系统的变化。随后，将设计具有自适应控制极限的统计监测方法来发出异常发生的信号。根据规则化学习过程的结果，将开发自适应控制策略，以减轻涡轮机子系统上过度和不期望的机械应力，以防止或减缓恶化过程。一种支持实时嵌入式数据处理的新型无线结构健康监测（SHM）系统将得到改进，以跟踪运行涡轮机的行为、性能和健康状况。这项研究的成果将促进风电行业从使用基本的诊断和控制技术到使用复杂的综合监测和控制技术的平稳过渡。新的监控方法将能够及时发现异常情况，同时减少误报。最佳确定的控制参数将在发电量和应力水平之间取得平衡，以延长涡轮机的使用寿命。虽然以风力涡轮机作为主要应用目标，但该方法也适用于受动态运行条件影响的其他工程系统，包括民用基础设施系统。该项目将通过一系列机制，为可再生能源和可持续发展领域的未来劳动力做好准备，包括将 STEM 领域代表性不足的学生纳入可再生能源研究、为学生提供与国家实验室互动的机会、并与其他国内外研究小组合作。