Yaw-adjusted wind power curve modeling: A local regression approach
Yaw-adjusted wind power curve modeling: A local regression approach
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DOI：
10.1016/j.renene.2022.12.001
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发表时间：
2022-12-14
期刊：
影响因子：
8.7
通讯作者：
Ezzat, Ahmed Aziz
中科院分区：
文献类型：
--
作者：
Nasery, Praanjal;Ezzat, Ahmed Aziz
利用现场数据准确估计风力曲线对多个风电场的运营非常重要，包括生产力评估、输出功率估计、运营和维护等。现有的风电曲线估算方法主要依靠环境变量（如风速、风向、密度）作为输入来构建风电关系。本文试图将偏航失调作为功率曲线模型的附加输入，构建下文所称的“偏航调整风电曲线”。我们的分析表明，将偏航偏差整合到功率曲线中是非常重要的，这主要是由于环境变量（主要是风速）对涡轮机功率输出的压倒性影响，这掩盖了偏航误差对功率产生的次要影响。为此，我们提出了一种基于局部回归的方法，该方法以环境变量的有效邻域为条件重构了偏航功率关系。在法国两台陆上风力涡轮机的运行数据测试中，我们提出的方法在功率估计精度方面取得了显著的进步，相对于一组流行的基于统计和机器学习的功率曲线模型。
Accurate estimation of wind power curves using field data is instrumental to several wind farm operations including productivity assessment, power output estimation, operations and maintenance, among others. Existing methods for estimating wind power curves mainly rely on environmental variables (e.g., wind speed, direction, density) as inputs to construct the wind-to-power relationship. This paper attempts to integrate yaw misalignment as an additional input to power curve models, constructing what is referred to hereinafter as "yaw-adjusted wind power curves."Our analysis shows that integrating yaw misalignment into power curves is non-trivial, largely due to the overwhelming impact of environmental variables (mainly wind speed) on a turbine's power output, which obscures the secondary effect of yaw errors on power production. In response, we propose a local-regression-based method which reconstructs the yaw-to-power relationship conditional on an effective neighborhood of environmental variables. Tested on operational data from two onshore wind turbines in France, our proposed approach achieves significant improvements, in terms of power estimation accuracy, relative to a set of prevalent statistical-and machine-learning-based power curve models.