TY  - JOUR
AU  - Rüttgers, Mario
AU  - Lee, Sangseung
AU  - Shin, Heesoo
TI  - Neural Networks for Improving wind Power Efficiency: A Review
JO  - Fluids
VL  - 7
SN  - 2311-5521
CY  - Belgrade
PB  - MDPI
M1  - FZJ-2022-05222
SP  - 12
PY  - 2022
AB  - 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 tomaximize 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 tomaximize 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 farmcontrol, 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 discussneural networks for improving the energy efficiency of wind farms. Last, we review neural networks to design turbine blades with superior aerodynamic performances.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000902649500001
DO  - DOI:10.3390/fluids7120367
UR  - https://juser.fz-juelich.de/record/911992
ER  -