Uncertainty quantification methods applied to wind energy systems

应用于风能系统的不确定性量化方法

• 批准号: RGPIN-2020-04511
• 负责人: Crawford, Curran
• 金额: $ 4.01万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740005
• 关键词: Uncertainty; quantification; methods; applied; wind

Wind energy is one of the most promising renewable energy sources given order of magnitude energy requirements and resource availability. Wind resources are widely distributed globally, while weather forecasting with demand response and storage technologies are enabling grid integration at-scale. Wind energy generators can be mass produced and installed incrementally. Technology progression in conventional `Danish concept' 3-bladed turbine design has enabled 10+ MW machines to be commercially viable. GW-scale arrays of bottom-mounted offshore wind turbines are now industrial practice. A floating offshore wind turbine array has just been commercially deployed for the first time, opening up additional wind resources globally in deeper waters. Nascent airborne wind energy systems (AWES) that fly kites/gliders on tethers to capture wind energy are also being developed, to pursue a step reduction in material requirements for wind energy converters. Large-scale AWES, if successful, will logically be deployed offshore. At the heart of all these systems is the wind itself, inherently turbulent and imposing stochastic loading on the structures and causing variable power output. Offshore, wave forcing is an additional stochastic forcing. The ability to quickly and accurately analyze stochastic loading is key to exploring new design concepts and improving performance. The proposed research program will build on the PI's research group's large body of work on a range of stochastic methods applied to energy systems, including fusing together different simulators (aerodynamics, structures) with heterogeneous levels of fidelity to affect robust optimization of the system parameters and configurations. The stochastic approaches to be developed will be capable of analyzing wind energy systems and directly computing uncertain measures of their performance and life time, rather than relying on simplified approximations of steady state performance. The proposed research will focus specifically on developing and applying stochastic methods for uncertainty quantification of floating conventional and AWES, to then enable design and optimization studies of alternative concepts. The first application is to wind farm design, requiring fast modeling of fatigue loading for turbine arrays based on local unsteady flows. The second application is inside a single machine design optimization loop. Wind turbine operation is never steady, hence optimization of a steady model is not optimal or efficient. The ability to accurately, efficiently and directly compute the stochastic response to unsteady wind and wave inputs opens up the possibility of using more efficient optimization methods to exploring and optimize a wide range of wind generation system designs.

考虑到能源需求和资源可用性的数量级，风能是最有前途的可再生能源之一。风能资源在全球范围内广泛分布，而天气预报与需求响应和存储技术正在实现大规模的电网整合。风能发电机可以批量生产并逐步安装。传统的“丹麦概念”3叶片涡轮机设计的技术进步使10+ MW的机器在商业上可行。千兆瓦级的海底风力涡轮机阵列现在已经成为工业实践。一种漂浮的海上风力涡轮机阵列刚刚首次商业化部署，为全球更深层的水域开辟了额外的风力资源。新兴的空中风能系统（awe）也正在开发中，它可以用绳子放风筝/滑翔机来捕捉风能，从而逐步减少对风能转换器的材料要求。如果成功，大规模的awe将被部署到海外。所有这些系统的核心都是风本身，固有的湍流和对结构施加随机载荷并导致可变功率输出。在近海，波浪强迫是一种额外的随机强迫。快速准确地分析随机载荷的能力是探索新设计概念和提高性能的关键。拟议的研究项目将建立在PI研究小组对一系列应用于能源系统的随机方法的大量工作的基础上，包括将不同的模拟器（空气动力学，结构）融合在一起，具有不同的保真度水平，以影响系统参数和配置的稳健优化。待开发的随机方法将能够分析风能系统，并直接计算其性能和寿命的不确定度量，而不是依赖于稳态性能的简化近似。拟议的研究将特别侧重于开发和应用浮动常规和awe的不确定性量化的随机方法，然后使替代概念的设计和优化研究成为可能。第一个应用是风电场设计，需要基于局部非定常流的涡轮阵列的快速疲劳载荷建模。第二个应用是在单个机器设计优化循环中。风力发电机组的运行从来不是稳定的，因此稳定模型的优化不是最优的或有效的。准确、高效、直接地计算非定常风浪输入随机响应的能力，为使用更有效的优化方法探索和优化各种风力发电系统设计提供了可能。