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

• Published in: IEEE transactions on industrial informatics Vol. 12; no. 3; pp. 1261 - 1269
• Main Authors: Tan, Matthias, Zijun Zhang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithm design and analysis; Algorithms; Computation; Computational modeling; Data mining; Data models; data processing; Datasets; Design analysis; Mathematical models; Neural networks; Prediction algorithms; Regression; Samplers; Training; Training data; wind energy; wind power prediction; Wind turbines; neural networks (NN); wind power prediction; wind energy; Data mining; data processing
• DOI: 10.1109/TII.2016.2532321
• ISSN: 1551-3203

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.