Reivew of active wake control for horizontal-axis wind turbines
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摘要: 对于大型风电场而言，由尾流干扰引起的产能损失最高可达30%～40%。主动尾流控制（AWC）技术通过上游风力机的偏航来诱导尾流侧向偏转，以减弱对后排风力机的尾流干扰、增加风电场的总产能，应用前景极其广阔。本文从无偏航风力机尾流模型、偏航风力机尾流模型、多风力机尾流叠加方法和风电场产能优化四个角度，综述了AWC技术在过去近20年的研究进展，并对该技术走向工程应用中所亟需解决的问题进行了展望。整体来看，该项技术已经基本成熟。研究人员从最初的理论、仿真和风洞实验研究，走向了实际风电场的效果验证。在理论预测层面，高斯尾流亏损模型、基于旋涡诱导横向速度的一次和二次尾流偏转模型、具备动量守恒特性的尾流叠加方法正在成为风电场产能预测的标配。实际工程应用层面，风力机间距、排数、湍流度、大气边界层热稳定性、风向和风速变化均会影响AWC所带来的产能收益；典型条件下，应用AWC技术后，全尾流干扰风向上的风电场产能可提高约5%～15%，各个风向平均后的年均收益约为1%～3%。Abstract: Large-scale wind farms with multiple rows of horizontal-axis wind turbines suffer from significant power losses (30%-40%) due to wake interactions. To deal with this situation, the yaw-based active wake control (AWC) has been proposed. The principle of AWC is to yaw the upstream wind turbines so that the wake can be deflected away from the turbine row, which hopefully will lead to a net gain of the total wind farm power production. In this paper, the progress of the AWC technique in the past decade is reviewed from four aspects: wake models of single non-yawed wind turbine, wake models of single yawed wind turbine, wake superposition methods for multiple wind turbines, and wind farm power optimization. Meanwhile, issues needed to be addressed before being applied to engineering are summarized. Based on these reviews, it is fair to conclude that the AWC technique is more or less mature now, in the sense that earlier laboratory results from analytical modeling, numerical simulations, and wind tunnel studies have been successfully applied to field tests of commercial wind farms, and significant improvement of the net power gain has been obtained. In terms of theoretical progress, EPFL Gaussian wake models, primary and secondary wake deflection models based on the vortex-induced cross-wind velocities, momentum-conserving wake superposition laws are increasingly becoming the standard in the wind farm power prediction. Regarding practical engineering, it has been found that a whole bunch of parameters such as turbine rows, streamwise turbine spacing, turbulence intensity, thermal instability of atmospheric boundary layer, wind speed, and direction variability can affect the magnitude of net power gain in the active wake control. According to recent field tests performed by National Renewable Energy Laboratory (NREL, US) and Stanford University, AWC is able to improve the total wind farm power production by 5%-15% if the wind direction is aligned with turbine rows, and when these net power gains are averaged over all wind directions, an increase of 1%-3% in the wind farm efficiency is expected.
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Key words:
- wind turbine /
- wake /
- yaw control /
- power optimisation /
- review
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图 5 用于风电场产能优化的主动偏航控制技术原理示意图
Figure 5. A sketch of the active yaw control technique for wind farm power optimization
表 1 非偏航风力机尾流扩张模型对比
Table 1. Comparisons of wake expansion models for non-yawed wind turbines
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表 3 尾流速度亏损叠加方法小结
Table 3. A list of wake deficit superposition methods
Method Authors (Year) Mathematical expression A Lissaman (1979)[64] $\bar U = {U_\infty } - \displaystyle\sum\limits_i {({U_\infty } - \bar u_{}^i)}$ B Katic et al. (1987)[65] $\bar U = {U_\infty } - \sqrt {\displaystyle\sum\limits_i { { {({U_\infty } - \bar u_{}^i)}^2} } }$ C Niayifar et al. (2016)[66] $\bar U = {U_\infty } - \displaystyle\sum\limits_i {(u_h^i - \bar u_{}^i)}$ D Voutsinas et al. (1990)[67] $\bar U = {U_\infty } - \sqrt {\displaystyle\sum\limits_i { { {(u_h^i - \bar u_{}^i)}^2} } }$ E Zong et al. (2020)[68] $\bar U = {U_\infty } - \displaystyle\sum\limits_i {\dfrac{ {u_c^i(x)} }{ { {U_c}(x)} }({U_\infty } - \bar u_{}^i)}$
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