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arXiv 2103.09082physics.flu-dynmath.OC

模型参数不确定性下的风电场偏航控制设定点优化

Wind farm yaw control set-point optimization under model parameter uncertainty

Michael F. Howland

AI总结:

本研究提出一种纳入模型参数不确定性的风电场偏航设定点优化方法,经开环数值实验与闭环大涡模拟验证,可提升尾流转向控制鲁棒性,低湍流特定布局下发电量提升显著。

摘要:

尾流转向(即风电场中特定涡轮机的有意偏航失准)已被证明是一种有望提升风电场总发电量的控制方法。现有算法利用稳态尾流模型优化偏航失准角设定点,采用确定性框架,或是考虑风向与偏航失准的波动性和不确定性的优化器。尾流模型依赖于平均流场中物理现象的参数化,例如尾流扩展率。尾流模型的参数具有不确定性,且会根据大气条件(包括湍流强度、稳定性、风切变、风转向及其他大气特征)在风电场中随时间变化。本研究开发了一种偏航设定点优化方法,除风况波动性和不确定性外,还纳入了模型参数不确定性。该优化方法在开环控制数值实验中进行了测试,实验采用公用事业级风电场运行数据:在低湍流强度下,考虑参数不确定性的设定点优化框架对特定风电机组布局的风电场发电量具有统计显著影响,但对所有研究的布局以及更高湍流强度下的结果并不显著。该设定点优化器还在对流大气边界层的大涡模拟中,针对模型风电场的闭环尾流转向控制进行了测试。纳入模型参数不确定性的偏航设定点优化,提升了闭环尾流转向控制对偏航控制器更新频率提高的鲁棒性。由于湍流对流大气边界层(ABL)中环境发电量波动性较高,风电场发电量的提升不具有统计显著性。

英文摘要:

Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize the yaw misalignment angle set-points using steady-state wake models and either deterministic frameworks, or optimizers which account for wind direction and yaw misalignment variability and uncertainty. Wake models rely on parameterizations of physical phenomena in the mean flow field, such as the wake spreading rate. The wake model parameters are uncertain and vary in time at a wind farm depending on the atmospheric conditions, including turbulence intensity, stability, shear, veer, and other atmospheric features. In this study, we develop a yaw set-point optimization approach which includes model parameter uncertainty, in addition to wind condition variability and uncertainty. The optimization is tested in open-loop control numerical experiments using utility-scale wind farm operational data for which the set-point optimization framework with parameter uncertainty has a statistically significant impact on the wind farm power production for certain wind turbine layouts at low turbulence intensity, but the results are not significant for all layouts considered nor at higher turbulence intensity. The set-point optimizer is also tested for closed-loop wake steering control of a model wind farm in large eddy simulations of a convective atmospheric boundary layer. The yaw set-point optimization with model parameter uncertainty improved the robustness of the closed-loop wake steering control to increases in the yaw controller update frequency. Increases in wind farm power production were not statistically significant due to the high ambient power variability in the turbulent, convective ABL.

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