Multiple wake interactions in large wind farms

大型风电场的多重尾流相互作用

• 批准号: 1464383
• 负责人: Rebecca Barthelmie
• 金额: $ 13.49万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464383&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.

1067007 PI Barthelmie目前在美国部署的风力发电场通常包含数百台风力涡轮机，装机容量超过100兆瓦。这些大型阵列中的风力涡轮机尾流导致风电场总功率输出减少高达20%。 这些尾流（其包围风力涡轮机后面的风速降低和湍流增强的区域）还由于增加的疲劳载荷而降低了涡轮机寿命。私家侦探？先前的研究已经表明，当前的风力发电场模型低估了大型阵列中风力涡轮机尾流的大小。该项目的主要目标是（1）提高对风速、湍流和大气稳定性条件范围内的单、双和多尾流发展的物理理解和建模，以及（2）评估是否可以通过更好地量化和建模风力涡轮机尾流来显著降低功率预测的不确定性，并改进阵列配置。通过对风力涡轮机尾流之间以及整个风电场尾流与上覆大气之间的相互作用进行显式建模，可以大大降低预测大型风电场功率输出的不确定性。该研究将涉及决定风电场效率的因素的先进测量和建模，适用于目前正在部署的大型风力涡轮机和风电场。PI将侧重于风力涡轮机尾流引起的下游涡轮机上的功率损失和附加疲劳载荷的量化，包括三个部分：1）使用多普勒光探测和测距（激光雷达）对风力涡轮机尾流和相关大气和涡轮机参数进行高分辨率测量。PI将使用遥感系统对大型风力发电场进行测量，以量化大气状态和连续波，从而准确地量化风和自由湍流及其在一系列大气条件下单尾流、双尾流和多尾流情况下远高于叶尖高度（150 - 200 m）的剖面，并提供不同涡轮机负载条件下尾流行为的详细数据。2)大型运行风电场多尾流相互作用的数据分析和建模。 PI与一些风电场运营商合作，从五个大型陆上风电场获得数据集，这些数据集结合了规则和不规则阵列，可用于评估大型陆上风力涡轮机阵列的尾流行为。结合收集的数据，该分析将用于量化功能相关性，并开发包含涡轮机和大气参数的多尾流模型参数化。3)一种新的多尾流模型的发展。 PI将在扩展为单尾流开发的数值尾流模型的基础上开发一个新模型，并从分析模型中提取，以包括多尾流相互作用。这些活动旨在通过以下方式鼓励风电场建模领域的广泛参与和科学严谨性：1）扩大印第安纳州大学虚拟尾流实验室，为建模人员提供风电场案例研究数据和时间序列，用于模型开发和评估。虚拟尾流实验室是一个基于网络的工具，提供可用于量化风力涡轮机尾流和评估尾流模型的数据集。2)与国际团体合作开发尾流模型基准，为风电场模型评估提供更好的指标，并增加学术界和工业界对风电场最佳功率预测过程的参与。3)培训学生在风力发电气象学与工业合作，使用最先进的模型和风力发电场为基础的测量状态。