Wind Turbine Modeling with Data-Driven Methods and Radially Uniform Designs

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

23 Scopus Citations
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Original languageEnglish
Pages (from-to)1261-1269
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number3
Online published18 Feb 2016
Publication statusPublished - Jun 2016


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), k 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.

Research Area(s)

  • Data mining, data processing, neural networks (NN), wind energy, wind power prediction