Quantifying wind farm power losses due to wind turbine wakes

量化风力涡轮机尾流造成的风电场功率损失

• 批准号: 0828655
• 负责人: Rebecca Barthelmie
• 金额: --
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0828655&HistoricalAwards=false
• 关键词: Quantifying; wind; farm; power; losses

CBET-0828655BarthelmieOptimal spacing of wind turbines in large wind farm arrays depends in part on turbine interactions, i.e. wind turbine wakes that are maximized at close spacing. Wakes are the volume downwind of individual turbines where wind speed is reduced and turbulence is enhanced due to energy extraction at the preceding turbine. In large offshore wind farms, power losses due to wakes can exceed 20% of total potential power production and lead to an increase in fatigue loading reducing turbine lifetimes. The objective of the research is to quantify and improve predictive capability for the development, propagation, combination and dissipation of wind turbine wakes in large onshore wind farms. Wake models used in current wind farm prediction tools under-predict power losses in large wind farms. There are two potential explanations; (1) large wind farms create additional turbulence which fundamentally alters the structure of the overlying boundary-layer, (2) combining wakes from individual turbines both downwind and laterally is mis-specified by the current generation of wind farm models. Evaluation of the models in small wind farms (three rows or smaller) indicates that models are able to capture power losses due to wakes which lends support to these two hypotheses that pertain to large multi-row arrays. This project will combine statistical analysis of observed wind farm data with evaluation and development of three classes of models (from the analytic to computational fluid dynamics codes) to improve predictions of wake losses. The goal is to produce a model that accurately captures wake propagation and interactions in different wind speed, turbulence and atmospheric stability conditions, to model wake combination in a more realistic way and to account for changes in the structure of the boundary-layer. Ultimately, this will produce a model which accurately quantifies wake losses for existing wind farm layouts and will allow assessment of wind farm layouts (turbine spacing) which are optimized for wake losses. The outcome from this project will contribute unique insight into how wakes propagate through large wind farms, and transform the ways in which wake impacts on power output are modeled.This project will be led by Professor R.J. Barthelmie, a renowned leader in wind energy research, and will be conducted in collaboration with scientists at the National Renewable Energy Laboratory and Airtricity/E.ON, a world leading renewable energy company. Both have agreed to provide data from operating large wind farms and to collaboration on model application and evaluation. This collaboration will both enhance the project and increase symbioses and knowledge transfer. The project will also enhance educational and training opportunities at Indiana University.

CBET-0828655Barthelmie大型风电场阵列中风力涡轮机的最佳间距部分取决于涡轮机的相互作用，即在近距离时风力涡轮机尾流最大化。尾流是单个涡轮机的顺风体积，由于前涡轮机的能量提取，风速降低并且湍流增强。在大型海上风电场中，尾流引起的功率损失可能超过总潜在发电量的 20%，并导致疲劳载荷增加，从而缩短涡轮机的使用寿命。该研究的目的是量化和提高大型陆上风电场风力涡轮机尾流的发展、传播、组合和消散的预测能力。 当前风电场预测工具中使用的尾流模型低估了大型风电场的功率损失。有两种可能的解释： (1) 大型风电场产生额外的湍流，从根本上改变了上覆边界层的结构，(2) 当前一代风电场模型错误地指定了顺风和横向的单个涡轮机的尾流。对小型风电场（三排或更小）中模型的评估表明，模型能够捕获由于尾流造成的功率损失，这为与大型多排阵列有关的这两个假设提供了支持。该项目将把观测到的风电场数据的统计分析与三类模型（从解析到计算流体动力学代码）的评估和开发结合起来，以改进尾流损失的预测。目标是建立一个模型，准确捕捉不同风速、湍流和大气稳定性条件下的尾流传播和相互作用，以更真实的方式对尾流组合进行建模，并考虑边界层结构的变化。最终，这将产生一个模型，该模型可以准确量化现有风电场布局的尾流损失，并允许评估针对尾流损失进行优化的风电场布局（涡轮机间距）。 该项目的成果将为尾流如何通过大型风电场传播提供独特的见解，并改变尾流对电力输出影响的建模方式。该项目将由 R.J. Barthelmie 是风能研究领域的著名领导者，该项目将与国家可再生能源实验室和世界领先的可再生能源公司 Airtricity/E.ON 的科学家合作进行。双方均同意提供运营大型风电场的数据，并就模型应用和评估进行合作。这种合作将增强该项目并增加共生和知识转移。该项目还将增加印第安纳大学的教育和培训机会。