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关注这篇论文的作者
Giwhyun Lee
Yu Ding
Marc G. Genton
Le Xie

考虑多元环境因素的内陆与海上风电功率曲线估计

研究问题
风机输出受风速以外的多种环境因素影响时,如何估计条件功率曲线及输出分布,并刻画非线性与交互作用?
方法与思路
作者提出加性多元核方法,纳入风向、空气密度等环境因素,在给定环境条件下提供功率输出的点估计与密度估计。
材料陈述的贡献
摘要称方法能够捕捉非线性和部分高阶交互作用,并以四台内陆风机及两台海上风机的运行数据展示改善。
阅读关注点
应核对加性结构如何容纳高阶交互、带宽选择、条件密度构造,以及运行数据中的比较标准和验证设计。
阅读边界
摘要未给出改善幅度、比较模型或外部验证结果,不能外推至其他风场;索引摘要未注明版本,不能推断刊本变化。

原文摘要

In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.

笔记与分组
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