Wind farm control (WFC) algorithms rely on an estimate of the ambient wind speed, wind direction, and turbulence intensity in the determination of the optimal control setpoints. However, the measurements available in a commercial wind farm do not always carry sufficient information to estimate these atmospheric quantities. In this paper, a novel measure (“observability”) is introduced that quantifies how well the ambient conditions can be estimated with the measurements at hand through a model inversion approach. The usefulness of this measure is shown through several case studies. While the turbine power signals and the inter‐turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for WFC to sufficiently cover observability for any ambient condition. Further, generally, more wake interaction leads to a higher observability. Also, the mathematical framework presented in this article supports the straightforward notion that turbine power measurements provide no additional information compared with local wind speed measurements, implying that power measurements are superfluous. Irregular farm layouts result in a higher observability due to the increase in unique wake interaction. The findings in this paper may be used in WFC to predict which ambient quantities can (theoretically) be estimated. The authors envision that this will assist in the estimation of the ambient conditions in WFC algorithms and can lead to an improvement in the performance of WFC algorithms over the complete envelope of wind farm operation.

中文翻译：

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基于模型的风电场控制估计中的环境条件可观察性：以静态模型为重点

风电场控制（WFC）算法在确定最佳控制设定点时依赖于周围风速，风向和湍流强度的估计。但是，商业风电场中可用的测量值并不总是携带足够的信息来估计这些大气量。在本文中，引入了一种新颖的度量（“可观察性”），该度量量化了通过模型反演方法使用手头的度量可以估计周围环境的程度。通过一些案例研究表明了该措施的有效性。尽管涡轮机功率信号和涡轮机之间的相互作用会提供有关风向的信息，本文介绍的案例研究表明，强烈需要对WFC进行风向测量，以充分涵盖任何环境条件下的可观察性。此外，通常，更多的唤醒交互作用导致更高的可观察性。同样，本文介绍的数学框架也支持一个简单的概念，即涡轮机功率测量与本地风速测量相比不提供任何附加信息，这意味着功率测量是多余的。由于独特的唤醒交互作用的增加，不规则的场布局导致更高的可观察性。本文的发现可用于WFC中以预测可以（理论上）估计哪些环境量。