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| Hauptseite > Publikationsdatenbank > Neural Networks for Improving wind Power Efficiency: A Review > print |
| Hauptseite > Publikationsdatenbank > Neural Networks for Improving wind Power Efficiency: A Review > print |
| 001 | 911992 | ||
| 005 | 20230224084236.0 | ||
| 024 | 7 | _ | |a 10.3390/fluids7120367 |2 doi |
| 024 | 7 | _ | |a 2128/32863 |2 Handle |
| 024 | 7 | _ | |a WOS:000902649500001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-05222 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 530 |
| 100 | 1 | _ | |a Rüttgers, Mario |0 P:(DE-Juel1)177985 |b 0 |u fzj |
| 245 | _ | _ | |a Neural Networks for Improving wind Power Efficiency: A Review |
| 260 | _ | _ | |a Belgrade |c 2022 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1669704063_12116 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 700 | 1 | _ | |a Lee, Sangseung |0 P:(DE-HGF)0 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Shin, Heesoo |0 P:(DE-HGF)0 |b 2 |
| 770 | _ | _ | |a Wind and Wave Renewable Energy Systems, Volume II |
| 773 | _ | _ | |a 10.3390/fluids7120367 |0 PERI:(DE-600)2882362-X |p 12 |t Fluids |v 7 |y 2022 |x 2311-5521 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911992/files/Neural_Networks_for_Improving_Wind_Power_Efficiency_A_Review.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2022 |
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