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Wind Turbine Modeling With Data-Driven Methods and Radially Uniform Designs | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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 sa...Show More

Abstract:

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.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 12, Issue: 3, June 2016)
Page(s): 1261 - 1269
Date of Publication: 19 February 2016

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