Multivariate Analysis and Prediction of Wind Turbine Response to Varying Wind Field Characteristics Based on Machine Learning
Multivariate Analysis and Prediction of Wind Turbine Response to Varying Wind Field Characteristics Based on Machine Learning
复制标题
基于机器学习的风力发电机对变化风场特征响应的多变量分析和预测
DOI：
10.1061/9780784413029.015
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
D. Hartmann
中科院分区：
文献类型：
--
作者：
Jinkyoo Park;K. Smarsly;K. Law;D. Hartmann
场址风场特性对风力机结构响应和寿命有重要影响。本文提出了一种用于分析和预测风力机结构对不同风场特性响应的机器学习方法。应用机器学习算法(i)更好地了解由于大气条件而引起的风场特征变化，以及（ii）对受波动风影响的风力涡轮机负载获得新的见解。以德国某500kw风力发电机组为参考系统，利用高斯混合模型，通过比较多个风场特征的联合概率分布函数，研究了风场的变化规律。在此基础上，基于高斯判别分析对具有代表性的风电机组昼、夜负荷进行了预测、比较和分析。
Site-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine structures to varying wind field characteristics. Machine learning algorithms are applied (i) to better understand changes of wind field characteristics due to atmospheric conditions and (ii) to gain new insights into the wind turbine loads being affected by fluctuating wind. Using Gaussian Mixture Models, the variations in wind fields are investigated by comparing the joint probability distribution functions of several wind field features, which are constructed from long-term monitoring data taken from a 500 kW wind turbine in Germany, which is used as a reference system. Furthermore, based on Gaussian Discriminative Analysis, representative daytime and nocturnal wind turbine loads are predicted, compared, and analyzed.