Quantifying wind farm power losses due to wind turbine wakes

CBET-0828655Barthelmie大型风电场阵列中风力涡轮机的最佳间距在一定程度上取决于涡轮机相互作用，即在近距离时最大化的风力涡轮机尾迹。尾迹是单个涡轮机的顺风向风量，由于前一个涡轮机的能量提取，风速降低，湍流增强。在大型海上风电场，尾流造成的电力损失可能超过总潜在发电量的20%，并导致疲劳负荷增加，缩短涡轮机寿命。这项研究的目的是量化和提高对大型陆上风电场中风力机尾迹的发展、传播、组合和消散的预测能力。目前风电场预测工具中使用的尾迹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，这从根本上改变了上覆边界层的结构；(2)当前一代的风电场模型错误地指定了单个涡轮机的顺风和横向尾迹的组合。在小型风电场(三排或更小)中对模型的评估表明，模型能够捕获由于尾迹引起的功率损失，这支持与大型多排阵列有关的这两个假设。该项目将结合对观测风电场数据的统计分析和评估和开发三类模型(从解析到计算流体动力学代码)，以改进对尾流损失的预测。其目标是建立一个模型，准确地捕捉不同风速、湍流和大气稳定条件下的尾迹传播和相互作用，以更现实的方式模拟尾迹组合，并考虑边界层结构的变化。最终，这将产生一个模型，准确地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)。该项目的成果将有助于了解尾流如何通过大型风电场传播，并改变尾流对功率输出影响的建模方式。该项目将由风能研究领域的知名领导者R.J.Barthelmie教授领导，并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。双方都同意提供运营大型风力发电场的数据，并在模型应用和评估方面进行合作。这种合作将既加强项目，又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。

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.