2018
DOI: 10.1177/0954406218797972
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Deep neural network-based wind speed forecasting and fatigue analysis of a large composite wind turbine blade

Abstract: The purpose of this paper is to analyze the modern deep neural networks such as nonlinear autoregressive network with external inputs and a recurrent neural network called long short-term memory for wind speed forecast for long-term and use the prediction for fatigue analysis of a large 5 MW wind turbine blade made of composite materials. The use of machine learning algorithms of advanced neural network applied for engineering problems is increasing recently. The present paper therefore brings as important con… Show more

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Cited by 31 publications
(22 citation statements)
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References 52 publications
(44 reference statements)
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“…While the vortex convection speeds are themselves an interesting result, the example should demonstrate to readers the incredible power of using pre-trained neural networks for extracting features from data. Deep neural networks are becoming increasingly used within the wind industry for applications, e.g., for predicting rotor icing (Yuan et al, 2019), power-curve estimation (Kulkarni et al, 2019) or even for rotor-blade inspections (Shihavuddin et al, 2019). We hope to demonstrate with this paper that modern machine learning tools and infrastructure can provide a useful boost to research in unsteady aerodynamics, wind energy and other adjacent fields.…”
Section: Introductionmentioning
confidence: 88%
“…While the vortex convection speeds are themselves an interesting result, the example should demonstrate to readers the incredible power of using pre-trained neural networks for extracting features from data. Deep neural networks are becoming increasingly used within the wind industry for applications, e.g., for predicting rotor icing (Yuan et al, 2019), power-curve estimation (Kulkarni et al, 2019) or even for rotor-blade inspections (Shihavuddin et al, 2019). We hope to demonstrate with this paper that modern machine learning tools and infrastructure can provide a useful boost to research in unsteady aerodynamics, wind energy and other adjacent fields.…”
Section: Introductionmentioning
confidence: 88%
“…Modern deep neural networks like nonlinear autoregressive networks are used for forecasting wind speed and fatigue analysis. Hence, involving highfidelity modeling for the composite materials of the blade, an integrated methodology is developed to establish time-varying loads over blade cross-sections to calculate dynamic wind loads, using multiple environmental parameters and deep learning for wind speed forecasting methods and fatigue analysis for a WT blade using the stress life approach with load ratio based on cohesive zone modeling (Kulkarni et al, 2019).…”
Section: Dynamic Analysismentioning
confidence: 99%
“…While the vortex convection speeds are themselves an interesting result, the example should demonstrate to readers the incredible power of using pre-trained neural networks for extracting features from data. Deep neural networks are becoming increasingly used within the wind industry for applications e. g. for predicting rotor icing (Yuan et al, 2019), power-curve estimation (Kulkarni et al, 2019) or even for rotor-blade inspections (Shihavuddin et al, 2019).…”
Section: So How Are These Variations Treated?mentioning
confidence: 99%