Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

James Roetzer, Xingjie Li and John Hall ()
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James Roetzer: Mechanical Engineering and Engineering Science, William States Lee College of Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Xingjie Li: Department of Mathematics and Statistics, College of Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
John Hall: Mechanical Engineering and Engineering Science, William States Lee College of Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Energies, 2024, vol. 17, issue 16, 1-20

Abstract: With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Keywords: wind energy; data-driven modeling; wind turbine performance; wake modeling; gaussian process regression; particle swarm optimization; fault detection; aerostructure twist; reduced computational expense (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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