Building the Foundation for Smart Wind Farms through First-Order Controls Opportunities based on Real-Time Observations of Complex Flows

通过基于复杂流实时观测的一阶控制机会为智能风电场奠定基础

• 批准号: 1336935
• 负责人: Brian Hirth
• 金额: $ 38.41万
• 依托单位: Texas Tech University
• 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: 2013-09-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1336935&HistoricalAwards=false
• 关键词: Building; Foundation; Smart; Wind; Farms

PI: Hirth, BrianProposal Number: 1336935Institution: Texas Tech UniversityTitle: Building the Foundation for Smart Wind Farms through First-Order Controls Opportunities based on Real-Time Observations of Complex FlowsReducing the cost of wind energy requires optimization of new and existing wind farms, which is inherently dependent on understanding the complex flows within and surrounding them, and using this knowledge to develop more advanced control systems. Complex flows within wind farms result from turbine wakes, heterogeneous local terrain and roughness, and the natural variability in atmospheric boundary layer flow. Existing numerical simulations used to plan wind farm installations fail to accurately represent these complex flows, and current wind turbines are optimized for autonomous operation with minimal recognition of their surroundings or interaction with neighboring turbines. Observations of the complex flows within and surrounding wind farms are exceedingly limited owing to significant instrumentation costs and inherent spatial coverage shortcomings of current technologies. This project will provide innovative data collection technologies, strategies, and analysis techniques to document the complex flow fields across several operational wind farms. Through industrial partnerships, this information will be integrated with operational turbine data and controls to construct transformative methods for developing the first generation of "smart" wind farms.This project leverages recently-developed Doppler radar technologies and innovative methodologies to provide an unprecedented assessment of the complex flows within and surrounding wind farms. Generated wind field products will be coupled with operational turbine and available meteorological data to provide flow field validation, comparison of radar derived and actual power output, and investigation of individual turbine and array performance. Supplemental access to first-order control opportunities (e.g. yawing a turbine slightly out of the wind to deflect the resulting wake away from a downstream turbine) within a research-scale wind farm and several full-scale operational wind farms will enhance the development of intelligent control systems.The use of specialized Doppler research radars to characterize wind farm flow fields represents a paradigm shift for measurement campaigns within the wind energy community. The high spatial and temporal resolutions and larger observational footprints allow for the identification of shortcomings within the current numerical simulations and modeling efforts. Integration of the operational turbine data provides an opportunity to investigate performance optimization spanning individual turbines to large turbine arrays. First-order controls opportunities provide a foundation to develop ?smart? wind farms through the creation of proactive, networked control systems capable of maximizing the total power output from an entire turbine array. Such intelligence, even in a basic form, does not currently exist. Implementation of these data collection and analyses methodologies in wind farm planning, design, deployment, and operation offer the potential to revolutionize wind energy world-wide.Beyond the integration into available undergraduate and graduate coursework, the technological advancements embodied within this interdisciplinary research project will also be reduced into several course modules targeting K-12 student groups. These modules, along with access to the radar technology, will be delivered to local student groups by the graduate students and faculty associated with the project, including underrepresented groups, to introduce the next-generation of scientists and engineers to the basics of wind energy, remote sensing, atmospheric science, controls, and aerodynamics. The unique and close collaboration with industry partners throughout this project will foster methodologies, products, and procedures that will have an immediate impact on existing wind farm operation and new wind farm design and layout. This project carries the potential to transform current wind energy practices to ultimately increase wind farm power output leading to a reduction in the cost of energy.

项目负责人：Hirth， brian提案编号：1336935机构：德克萨斯理工大学标题：通过一阶控制建立智能风电场的基础基于复杂流量的实时观测机会降低风能成本需要优化新的和现有的风电场，这本质上依赖于了解它们内部和周围的复杂流量，并利用这些知识开发更先进的控制系统。风力发电场内的复杂流动是由涡轮机尾迹、不均匀的局部地形和粗糙度以及大气边界层流动的自然变异性造成的。现有的用于规划风电场装置的数值模拟不能准确地代表这些复杂的流动，而且目前的风力涡轮机是为自主运行而优化的，对周围环境的识别或与邻近涡轮机的相互作用最小。由于高昂的仪器成本和当前技术固有的空间覆盖缺陷，对风电场内部和周围复杂流动的观测非常有限。该项目将提供创新的数据收集技术、策略和分析技术，以记录多个运行风电场的复杂流场。通过工业合作，这些信息将与运行中的涡轮机数据和控制相结合，为开发第一代“智能”风力发电场构建变革性方法。该项目利用最新开发的多普勒雷达技术和创新方法，对风电场内部和周围的复杂气流进行前所未有的评估。生成的风场产品将与运行中的涡轮机和可用的气象数据相结合，以提供流场验证、雷达推导和实际功率输出的比较，以及单个涡轮机和阵列性能的调查。在一个研究规模的风力发电场和几个全面运行的风力发电场中，对一阶控制机会的补充访问（例如，将涡轮机稍微偏航以使尾流偏离下游涡轮机）将加强智能控制系统的发展。使用专门的多普勒研究雷达来表征风电场流场，代表了风能社区测量活动的范式转变。高空间和时间分辨率以及更大的观测足迹使我们能够发现当前数值模拟和建模工作中的缺陷。运行涡轮机数据的集成为研究从单个涡轮机到大型涡轮机阵列的性能优化提供了机会。一阶控制机会为开发“智能”提供了基础。风力发电场通过创建主动的、网络化的控制系统，能够最大限度地提高整个涡轮机阵列的总输出功率。这样的智慧，即使是最基本的形式，目前也不存在。在风电场规划、设计、部署和运营中实施这些数据收集和分析方法，为全球风能革命提供了潜力。除了整合到现有的本科和研究生课程中，这个跨学科研究项目中体现的技术进步也将被缩减为针对K-12学生群体的几个课程模块。这些模块以及雷达技术将由与该项目相关的研究生和教师（包括代表性不足的群体）交付给当地的学生团体，向下一代科学家和工程师介绍风能、遥感、大气科学、控制和空气动力学的基础知识。在整个项目中，与行业伙伴的独特而密切的合作将促进方法、产品和程序的发展，这些方法、产品和程序将对现有风电场的运营和新风电场的设计和布局产生直接影响。该项目有可能改变目前的风能做法，最终增加风力发电场的发电量，从而降低能源成本。