Journal Article

FZJ-2022-05222

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Neural Networks for Improving wind Power Efficiency: A Review

Rüttgers, M.FZJ*RWTH* ; Lee, S. (Corresponding author)Extern* ; Shin, H.Extern*

2022
MDPI Belgrade

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Please use a persistent id in citations: http://hdl.handle.net/2128/32863  doi:10.3390/fluids7120367

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 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2022

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Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DEAL Springer ; DOAJ Seal ; Emerging Sources Citation Index ; Essential Science Indicators ; Fees ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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