Neural Networks for Improving Wind Power Efficiency: A Review

Fluids, vol. 7, pp. 367

Abstract

The demand for wind energy harvesting has grown significantly to mitigate the global challenges of climate change, energy security, and zero carbon emissions. Various methods to maximize wind power efficiency have been proposed. Notably, neural networks have shown large potential in improving wind power efficiency. In this paper, we provide a review of attempts to maximize wind power efficiency using neural networks. A total of three neural-network-based strategies are covered: (i) neural-network-based turbine control, (ii) neural-network-based wind farm control, and (iii) neural-network-based wind turbine blade design. In the first topic, we introduce neural networks that control the yaw of wind turbines based on wind prediction. Second, we discuss neural networks for improving the energy efficiency of wind farms. Last, we review neural networks to design turbine blades with superior aerodynamic performances.

Authors 3

1. Heesoo Shin

   Inha University

   Affiliation as printed

   Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of Korea

2. Mario Rüttgers Aachen Institute of Aerodynamics Chair of Fluid Mechanics

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany

   Jülich Aachen Research Alliance-Center for Simulation and Data Science, 52074 Aachen, Germany

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

3. Sang-Seung Lee corresponding

   Inha University

   Affiliation as printed

   Applied AI Center for Thermal and Fluid Research, Inha University, Incheon 22212, Republic of Korea

   Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of Korea