Statistical wind prediction and fatigue analysis for horizontal-axis wind turbine composite material blade under dynamic loads

Kulkarni, Pravin A; Hu, Weifei; Dhoble, Ashwinkumar S.; Padole, Pramod M.

doi:10.1177/1687814017724088

Cited by 14 publications

(11 citation statements)

References 48 publications

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“…Given that long-term wind speed measurements are often unavailable, statistical methods have been applied in wind prediction for fatigue reliability analysis of wind turbine blades. For example, Kulkarni et al [82] investigated three forecasting models (Markov chain, Kalman filter and autoregressive integrated moving average) and developed a long-term wind speed pattern used in the structural and fatigue analysis of a small horizontal-axis wind turbine composite blade. In addition to the mean wind speed, the fluctuations in the wind speed about the short-term mean naturally have a major impact on the blade fatigue reliability.…”

Section: Rotor Bladesmentioning

confidence: 99%

Structural Reliability Analysis of Wind Turbines: A Review

Jiang

Dong

et al. 2017

Energies

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Abstract:The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first-and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.

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Section: Rotor Bladesmentioning

confidence: 99%

Structural Reliability Analysis of Wind Turbines: A Review

Jiang

Dong

et al. 2017

Energies

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“…Kulkarni et al. 28 used CZM technique for fatigue analysis of adhesively bonded root joint of a composite material wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network-based wind speed forecasting and fatigue analysis of a large composite wind turbine blade

Kulkarni

Dhoble

Padole

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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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 connection between these latest machine learning methods and engineering analysis of complex wind turbine blades which are also the focus of researchers in renewable system design and analysis. First, a nonlinear autoregressive network with external inputs neural network model using Levenberg–Marquardt back propagation feed forward algorithm is developed with 5 years of environment parameters as input. Similarly, a long short-term memory based model is developed and compared. The chosen long short-term memory model is used for developing two-year wind speed forecast. This wind pattern is used to create time varying loads on blade sections and cross-verified with National Renewable Energy Laboratory tools. A high-fidelity CAD model of the NREL 5 MW blade is developed and the fatigue analysis of the blade is carried out using the stress life approach with load ratio based on cohesive zone modeling. The blade is found to have available life of about 23.6 years. Thus, an integrated methodology is developed involving high-fidelity modeling of the composite material blade, wind speed forecasting using multiple environmental parameters using latest deep learning methods for machine learning, dynamic wind load calculation, and fatigue analysis for National Renewable Energy Laboratory blade.

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“…To improve the ability of turbine blades to resist shock loading, to reduce the damage rate of turbine blades as much as possible, and to prolong the working time of the engine under the conditions where some turbine blades are damaged, tensile or shear tests under fault conditions are performed. [6][7][8] Few studies, however, have focused on the shock loading conditions caused by high-temperature and high-speed fragments.…”

Section: Introductionmentioning

confidence: 99%