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首页> 外文期刊>IEEE transactions on industrial informatics >Wind Turbine Modeling With Data-Driven Methods and Radially Uniform Designs
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Wind Turbine Modeling With Data-Driven Methods and Radially Uniform Designs

机译：数据驱动方法和径向统一设计的风力涡轮机建模

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摘要

This paper proposes a radially uniform (RU) design to sample representative datasets from a large volume of wind turbine data to build accurate data-driven models. The sampling capability and computational complexity are theoretically analyzed. It is shown that the RU design is representative of the original dataset and has computational complexity that is of the same order as sorting algorithms. Five algorithms, the neural networks (NN), multivariate adaptive regression splines (MARS), support vector machines (SVM), nearest neighbors (kNN), and linear regression (LR) are applied to model the wind turbine power output, drive-train vibratory acceleration, and tower vibratory acceleration based on the training dataset and sampled datasets. Extensive computational experiments are conducted to demonstrate advantages of the RU sampler over the random and maximin samplers. Results show that RU sampler outperforms the random sampler for building all five types of models and is more effective than the maximin sampler for building nonlinear models.
机译：本文提出了一种径向统一（RU）设计，以从大量风力涡轮机数据中采样代表性数据集，以建立精确的数据驱动模型。从理论上分析了采样能力和计算复杂度。结果表明，RU设计代表了原始数据集，并且计算复杂度与排序算法相同。神经网络（NN），多元自适应回归样条（MARS），支持向量机（SVM），最近邻（kNN）和线性回归（LR）这五种算法被用于对风力发电机的功率输出，传动系统进行建模振动加速度和塔架振动加速度基于训练数据集和采样数据集。进行了大量的计算实验，以证明RU采样器相对于随机采样器和maximin采样器的优势。结果表明，RU采样器在构建所有五种类型的模型方面均优于随机采样器，并且在构建非线性模型方面比maximin采样器更有效。

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