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Dynamic wake modulation induced by utility-scale wind turbine operation

机译:公用事业规模的风机运行引起的动态尾流调制

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Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly changing, the rate of wake recovery can be significantly influenced by dynamic wake modulation, which has not yet been explored. Here we present the first investigation of dynamic wake modulation in the near wake of an operational utility-scale wind turbine, and quantify its relationship with changing conditions. This experimental investigation is enabled using novel super-large-scale flow visualization with natural snowfall, providing unprecedented spatiotemporal resolution to resolve instantaneous changes of the wake envelope in the field. These measurements reveal the significant influence of dynamic wake modulation, which causes an increase in flux across the wake boundary of 11% on average, on wake recovery, providing insights into necessary modifications to traditional wake and farm models. Further, our study uncovers the direct connection between dynamic wake modulation and operational parameters readily available to the turbine controller such as yaw error, blade pitch, and tip speed ratio. These connections pave the way for more precise wake prediction and control algorithms under field conditions for wind farm optimization.
机译:了解风力涡轮机尾流的混合和恢复对于提高风电场中顺风涡轮机的发电和结构稳定性至关重要。在现场,进水流量和涡轮机运行不断变化,动态唤醒调制会显着影响唤醒恢复的速度,目前尚未进行探讨。在这里,我们介绍了在实用的公用事业规模的风力涡轮机的近尾流中进行动态尾流调制的首次研究,并量化了其与变化条件的关系。此实验研究使用具有自然降雪的新型超大规模流动可视化技术实现,从而提供了空前的时空分辨率,以解决现场尾迹包络线的瞬时变化。这些测量结果揭示了动态尾流调制对尾流恢复的重大影响,动态尾流调制对尾流恢复的影响平均为11%。此外,我们的研究揭示了动态尾流调制与涡轮控制器容易获得的运行参数(例如偏航误差,叶片螺距和叶尖速比)之间的直接联系。这些连接为风电场优化领域的现场条件下更精确的尾流预测和控制算法铺平了道路。

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