Multiple wake interactions in large wind farms

大型风电场的多重尾流相互作用

• 批准号: 1067007
• 负责人: Rebecca Barthelmie
• 金额: $ 30万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1067007&HistoricalAwards=false
• 关键词: Multiple; wake; interactions; large; wind

1067007 PI BarthelmieCurrent generation wind farms being deployed in the US often contain hundreds of wind turbines with installed capacities in excess of 100 MW. Wind turbine wakes in these large arrays are responsible for reduction of total wind-farm power output by up to 20%. These wakes, which encompass the region of decreased wind speeds and enhanced turbulence behind wind turbines, also reduce turbine lifetimes due to increased fatigue loading. The PIs? previous research has shown that current generation wind-farm models underestimate the magnitude of wind-turbine wakes in large arrays. The main objectives of this project are (1) to improve the physical understanding and modeling of the development of single, double and multiple wakes in a range of wind speed, turbulence, and atmospheric stability conditions, and (2) to assess whether uncertainty in power prediction can be significantly reduced, and array configuration improved, by better quantification and modeling of wind-turbine wakes. The uncertainty in predicting power output from large wind farms can be substantially reduced by explicit modeling of the interaction between wind-turbine wakes, and between whole wind-farm wakes and the overlying atmosphere. The research will involve advanced measurement and modeling of the factors that dictate wind-farm efficiency, appropriate to the large scales of wind turbines and wind farms currently being deployed. The PIs will focus on the quantification of power losses and additional fatigue loading on downstream turbines due to wind- turbine wakes and comprises three parts: 1) Highly resolved measurements of wind-turbine wakes and associated atmospheric and turbine parameters using Doppler light detection and ranging (lidar). The PIs will conduct measurements in large wind farms using a remote sensing systems to quantify the atmospheric state and continuous wave to accurately quantity both wind and freestream turbulence and their profiles well above tip-heights (150 -200 m) in single, double, and multiple wake situations under a range of atmospheric situations and to provide detailed data on wake behavior under different turbine loading conditions. 2) Data analysis and modeling for multiple wake interaction in large operational wind farms. The PIs have partnered with a number of wind-farm operators to obtain data sets from five large onshore wind farms with a combination of regular and irregular arrays that can be used to evaluate wake behavior in large onshore wind turbine arrays. In conjunction with data collected, this analysis will be used to quantify functional dependencies, and develop model parameterizations of multiple wakes incorporating turbine and atmospheric parameters. 3) Development of a new multiple-wake model. The PIs will develop a new model based on an extension of the numerical wake model developed for single wakes and drawing from the analytical models to include multiple wake interactions.The PIs? activities are designed to encourage broad participation and scientific rigor in the field of wind-farm modeling by: 1) Expanding the Indiana University virtual wake laboratory to supply wind-farm case study data and time series for modelers to use in model development and evaluation. The virtual wake laboratory is a web-based tool, which supplies data sets that may be used to quantify wind-turbine wakes and to evaluate wake models. 2) Development of wake-model benchmarking in collaboration with international groups to provide better metrics for wind-farm model evaluation and to increase involvement from academia and industry in the process of providing optimal power prediction from wind farms. 3) Train students in wind-power meteorology in collaboration with industry using state of the art models and wind-farm based measurements.

在美国部署的1067007 PI Barthelmiecurrent Estraent Estraent Generation Wind Farms通常包含数百个风力涡轮机，其安装能力超过100兆瓦。这些大阵列中的风力涡轮机醒来，负责将总风能电源输出降低多达20％。 这些唤醒包括降低风速和风力涡轮机后面湍流的区域，还降低了涡轮机的寿命。 pis？先前的研究表明，当前一代的风农模型低估了大型阵列中风涡轮激素唤醒的幅度。该项目的主要目的是（1）在一系列风速，湍流和大气稳定性条件下提高对单，双重和多重唤醒的发展的物理理解和建模，以及（2）评估功率预测的不确定性是否可以显着降低，并通过更好地降低阵列配置，通过更好地量化风盘式风盘式饮用风盘。通过明确建模风涡轮唤醒之间的相互作用以及整个风网唤醒和上覆的大气之间的相互作用，可以大大降低大型风电场功率输出的不确定性。这项研究将涉及对决定风向效率的因素的高级测量和建模，这适用于目前正在部署的大型风力涡轮机和风电场。 PI将集中于量化功率损耗和由于风力涡轮机唤醒引起的下游涡轮机上的额外疲劳负载，并包括三个部分：1）使用多普勒光检测和范围（LIDAR），风涡激唤醒的高度分辨率测量以及相关的大气和涡轮参数。 PI将使用遥控系统在大型风电场进行测量，以量化大气状态和连续波，以准确数量的风和自由式湍流及其轮廓远高于小费高（150 -200 m）在单个，双重和多重唤醒情况下在大气情况下进行多个唤醒状态，并在不同的大气环境下提供详细的数据，并在不同的涡轮上加载条件下提供了详细的数据。 2）大型操作风电场中多次唤醒相互作用的数据分析和建模。 PIS与许多风农运营商合作，从五个大型陆上风电场获得数据集，这些风场与常规和不规则阵列的组合结合在一起，可用于评估大型陆上风力涡轮机阵列中的唤醒行为。结合收集的数据，该分析将用于量化功能依赖性，并开发包含涡轮和大气参数的多个唤醒的模型参数化。 3）开发新的多次效力模型。 PI将基于为单一唤醒开发的数值唤醒模型的扩展，并从分析模型中绘制以包含多个尾流相互作用。活动旨在鼓励广泛的参与和科学严格的风向模型领域，作者：1）扩大印第安纳大学虚拟尾流实验室，以提供风网案例研究数据和时间序列，以供建模者用于模型开发和评估。虚拟尾流实验室是一种基于Web的工具，该工具提供可用于量化风力涡轮唤醒和评估唤醒模型的数据集。 2）与国际团体合作开发唤醒模型的基准测试，以提供更好的指标，以用于风向模型评估，并在提供风电场提供最佳权力预测的过程中增加学术界和行业的参与。 3）使用最先进的模型和基于风鱼类的测量结果培训与行业合作的风能气象学培训学生。