Collaborative Research: Empowering Next Generation Offshore Wind Farms Through Systematic Characterization of Floating Wind Turbine Array Dynamics

合作研究：通过浮式风力涡轮机阵列动力学的系统表征来增强下一代海上风电场的能力

• 批准号: 2034160
• 负责人: Raul Cal
• 金额: $ 38.8万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034160&HistoricalAwards=false
• 关键词: Collaborative; Research; Empowering; Next; Generation

Higher and more consistent wind speeds, along with the abundance of available area, make offshore wind a promising pathway to increasing the percentage of US electricity supplied by renewable energy sources, such as utility scale wind farms. The depth of the water in many locations, e.g., off the west coast of the US, and the ability to place installations further from shore, where they are less controversial, make floating platforms an appealing choice for future installations. While onshore wind farms are well established, far less is known about how interactions between the ocean environment (e.g., waves) and turbine motions and their wakes affect power output efficiency and turbine wear in floating wind farms. This grant will support research that employs laboratory experiments and high-fidelity computer simulations that will enable more robust and realistic predictions of wind plant power output. The data to be generated in this project can be leveraged to make design and control changes to increase efficiency and resilience of floating wind farms. These advances will help pave the way to more installed wind power that increases the use of clean, renewable technologies, thereby decreasing the carbon footprint of our energy system. The construction and operation of large-scale power plants will also help create and sustain a vibrant new US wind energy economy. The project will train graduate students in the broad interdisciplinary tools of wind energy science. Broader community outreach will be achieved through curriculum developed for public entities (e.g., museums) and summer programs for K-12 students.This project will develop state-of-the art laboratory experiments and numerical simulation tools to measure, analyze and characterize the coupled wind, wave, wake and platform dynamics affecting the power output and local turbine properties in floating wind farms. The new knowledge and data developed through these studies will be exploited to create a suite of dynamical systems modeling, estimation and analysis tools that will provide better predictions of critical wind farm properties such as power output and turbine loading. The data will be interrogated using dynamical systems tools such as proper orthogonal decomposition and dynamic (Koopman) mode decomposition that enable us to infer important system properties for further analysis, model validation and to develop estimation techniques as the building blocks for future real-time estimation and control algorithms. The outcomes of this work include: (1) New system characterization approaches that couple laboratory and simulation tools to systematically explore how the coupled wind, wave, turbine wake and platform dynamics affect the wind farm properties over a broad range of interacting spatial and temporal scales; (2) A suite of physics-informed models that describe the dynamics of the interactions most relevant to operation, design and control of floating wind farms; and (3) Estimation approaches that exploit the data and modeling tools, while taking into account the sensing and actuation approaches that can be implemented within real-time control paradigms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

更高和更一致的风速以及大量可用区域，使海上风成为增加可再生能源（例如公用事业规模风电场）提供的美国电力百分比的有希望的途径。在许多地方（例如，在美国西海岸，水的深度）以及将安装距离越来越小的岸边，在岸上越来越小的争议，使浮动平台成为未来安装的吸引人的选择。虽然陆上风电场已经建立了良好，但人们对海洋环境（例如波浪）和涡轮机动的相互作用及其唤醒如何影响浮动风电场中的功率输出效率和涡轮磨损。该赠款将支持采用实验室实验和高保真计算机模拟的研究，这些研究将对风电厂功率输出进行更健壮和现实的预测。可以利用该项目生成的数据来进行设计和控制更改，以提高浮动风电场的效率和弹性。这些进步将有助于为更多安装的风能铺平道路，从而增加了清洁，可再生技术的使用，从而减少了我们能源系统的碳足迹。大型发电厂的建设和运营还将有助于创造和维持充满活力的新美国风能经济。该项目将在风能科学的广泛跨学科工具中培训研究生。将通过为公共实体（例如，博物馆）和K-12学生开发的课程来实现更广泛的社区外展活动。该项目将开发最先进的实验室实验和数值模拟工具，以测量，分析和表征影响风，波浪，波浪，波浪，唤醒和平台动力学，影响电力输出和当地涡轮式风能场地。通过这些研究开发的新知识和数据将被利用，以创建一系列动态系统建模，估算和分析工具，以更好地预测关键风电场性能，例如功率输出和涡轮机加载。将使用动态系统工具（例如正确的正交分解和动态（Koopman）模式分解）来询问数据，这使我们能够推断重要的系统属性以进行进一步分析，模型验证并开发估计技术，以作为未来实时估计和控制算法的构件。这项工作的结果包括：（1）新的系统表征方法，将实验室和模拟工具进行系统探索耦合的风，波浪，涡轮唤醒和平台动力学如何影响风场在各种相互作用的空间和时间尺度上如何影响风电场的性能； （2）一套具有物理信息的模型，描述了与浮动风电场的操作，设计和控制最相关的相互作用的动力学； （3）利用数据和建模工具的估计方法，同时考虑到可以在实时控制范式中实施的感应和驱动方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。