Near-wake behaviour of a utility-scale wind turbine
Near-wake behaviour of a utility-scale wind turbine
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公用事业规模风力涡轮机的近尾流行为
DOI：
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发表时间：
2018
影响因子：
3.7
通讯作者：
Jiarong Hong
中科院分区：
文献类型：
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作者：
Teja Dasari;Yue Wu;Yun Liu;Jiarong Hong
超大尺度粒子图像测速（SLPIV）和相关的利用自然降雪的流动可视化技术已被证明是探测公用事业规模风力涡轮机周围湍流速度场和相干结构的有效工具（Hong等人）。Commun Nat。， 2014年第5卷，第4216条)。在这里，我们使用2014年至2016年在EOLOS现场站的2.5 MW涡轮机周围多次部署期间收集的数据进行了后续研究。这些数据包括在涡轮近尾迹115米（垂直）× 66米（流向）视场内的SLPIV测量数据，以及在各种涡轮运行条件下，与底部叶片尖端对应的海拔附近的叶顶涡行为的可视化。SLPIV测量提供了整个转子跨度的速度赤字和湍流动能评估。来自SLPIV的瞬时速度场表明存在间歇性尾迹收缩状态，这与通常与风力机尾迹相关的膨胀状态形成鲜明对比。这些收缩状态的特点是尾流中心部分的速度明显上升。引入尾流速度比$R_{w}$，定义为内尾流空间平均速度与外尾流空间平均速度之比，将瞬时近尾流分为膨胀状态（$R_{w}<1$）和收缩状态（$R_{w}>1$）。根据$R_{w}$准则，在SLPIV测量的30分钟时间内，尾迹收缩发生的时间为25%。通过对不同涡轮运行参数下尾迹状态时间序列的分布和采样分析，发现尾迹状态与桨距变化率之间存在一定的相关性。此外，叶片节距变化与涡轮上实测的塔架和叶片应变有很强的相关性，结果表明，涡轮塔架和叶片的弯曲确实会导致转子与涡轮尾迹的相互作用，导致尾迹收缩。叶顶涡行为的可视化表明存在一致的涡形成状态以及各种类型的扰动涡状态。在不同的条件采样限制下，通过许多涡轮机运行/响应参数（包括涡轮机功率和塔应变）以及这些量的波动，检查了对应于一致状态和扰动状态的直方图。本分析建立了不同涡轮工况下这些涡轮参数与叶顶涡行为之间明显的统计对应关系，并通过对这些涡轮参数和叶顶涡模式的时间序列样本的检验进一步证实了这一点。这项研究不仅提供了与最先进的数值模拟、实验室和现场测量进行比较的基准数据集，而且还有助于理解尾流特性和尾流的下游发展、涡轮机性能和调节，以及开发新的涡轮机或风电场控制策略。
Super-large-scale particle image velocimetry (SLPIV) and the associated flow visualization technique using natural snowfall have been shown to be effective tools to probe the turbulent velocity field and coherent structures around utility-scale wind turbines (Hong et al. Nat. Commun., vol. 5, 2014, article 4216). Here, we present a follow-up study using the data collected during multiple deployments from 2014 to 2016 around the 2.5 MW turbine at the EOLOS field station. These data include SLPIV measurements in the near wake of the turbine in a field of view of 115 m (vertical) $ imes$ 66 m (streamwise), and the visualization of tip vortex behaviour near the elevation corresponding to the bottom blade tip over a broad range of turbine operational conditions. The SLPIV measurements provide velocity deficit and turbulent kinetic energy assessments over the entire rotor span. The instantaneous velocity fields from SLPIV indicate the presence of intermittent wake contraction states which are in clear contrast with the expansion states typically associated with wind turbine wakes. These contraction states feature a pronounced upsurge of velocity in the central portion of the wake. The wake velocity ratio $R_{w}$ , defined as the ratio of the spatially averaged velocity of the inner wake to that of the outer wake, is introduced to categorize the instantaneous near wake into expansion ( $R_{w}<1$ ) and contraction states ( $R_{w}>1$ ). Based on the $R_{w}$ criterion, the wake contraction occurs 25 % of the time during a 30 min time duration of SLPIV measurements. The contraction states are found to be correlated with the rate of change of blade pitch by examining the distribution and samples of time sequences of wake states with different turbine operation parameters. Moreover, blade pitch change is shown to be strongly correlated to the tower and blade strains measured on the turbine, and the result suggests that the flexing of the turbine tower and the blades could indeed lead to the interaction of the rotor with the turbine wake, causing wake contraction. The visualization of tip vortex behaviour demonstrates the presence of a state of consistent vortex formation as well as various types of disturbed vortex states. The histograms corresponding to the consistent and disturbed states are examined over a number of turbine operation/response parameters, including turbine power and tower strain as well as the fluctuation of these quantities, with different conditional sampling restrictions. This analysis establishes a clear statistical correspondence between these turbine parameters and tip vortex behaviours under different turbine operation conditions, which is further substantiated by examining samples of time series of these turbine parameters and tip vortex patterns. This study not only offers benchmark datasets for comparison with the-state-of-the-art numerical simulation, laboratory and field measurements, but also sheds light on understanding wake characteristics and the downstream development of the wake, turbine performance and regulation, as well as developing novel turbine or wind farm control strategies.