Effects of Icing on Wind Turbine Fatigue Loads

This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades.

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Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads

Renewable Energy ◽

10.1016/j.renene.2016.01.010 ◽

2016 ◽

Vol 90 ◽

pp. 352-361 ◽

Cited By ~ 18

Author(s):

Henrik Stensgaard Toft ◽

Lasse Svenningsen ◽

John Dalsgaard Sørensen ◽

Wolfgang Moser ◽

Morten Lybech Thøgersen

Keyword(s):

Wind Turbine ◽

Climate Parameters ◽

Wind Climate ◽

Fatigue Loads

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Analysis of Fatigue Loads of Wind Turbine Blades Subject to Cold Weather Conditions Using a Finite Element Model

49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition ◽

10.2514/6.2011-262 ◽

2011 ◽

Cited By ~ 1

Author(s):

Patricio Lillo ◽

Curran Crawford

Keyword(s):

Finite Element ◽

Finite Element Model ◽

Wind Turbine ◽

Turbine Blades ◽

Weather Conditions ◽

Element Model ◽

Cold Weather ◽

Wind Turbine Blades ◽

Fatigue Loads

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1834 Influence of wind factors for fatigue loads of the wind turbine system

The proceedings of the JSME annual meeting ◽

10.1299/jsmemecjo.2008.2.0_187 ◽

2008 ◽

Vol 2008.2 (0) ◽

pp. 187-188

Author(s):

Atsutoshi MUTO ◽

Norio KUBO ◽

Haruki MIKAMI ◽

Toshiharu KARAUSHI

Keyword(s):

Wind Turbine ◽

Wind Turbine System ◽

Fatigue Loads

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Dependence of offshore wind turbine fatigue loads on atmospheric stratification

Journal of Physics Conference Series ◽

10.1088/1742-6596/524/1/012165 ◽

2014 ◽

Vol 524 ◽

pp. 012165 ◽

Cited By ~ 5

Author(s):

K S Hansen ◽

G C Larsen ◽

S Ott

Keyword(s):

Wind Turbine ◽

Offshore Wind ◽

Offshore Wind Turbine ◽

Atmospheric Stratification ◽

Fatigue Loads

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From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases

Wind Energy Science ◽

10.5194/wes-3-767-2018 ◽

2018 ◽

Vol 3 (2) ◽

pp. 767-790 ◽

Cited By ~ 22

Author(s):

Nikolay Dimitrov ◽

Mark C. Kelly ◽

Andrea Vignaroli ◽

Jacob Berg

Keyword(s):

Wind Turbine ◽

Nearest Neighbor ◽

Polynomial Chaos ◽

Surrogate Models ◽

Polynomial Chaos Expansion ◽

High Fidelity ◽

Chaos Expansion ◽

Site Specific ◽

Sensitivity Indices ◽

Fatigue Loads

Abstract. We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads using simplified load mapping functions (surrogate models), trained by means of a database with high-fidelity load simulations. The performance of five surrogate models is assessed by comparing site-specific lifetime fatigue load predictions at 10 sites using an aeroelastic model of the DTU 10 MW reference wind turbine. The surrogate methods are polynomial chaos expansion, quadratic response surface, universal Kriging, importance sampling, and nearest-neighbor interpolation. Practical bounds for the database and calibration are defined via nine environmental variables, and their relative effects on the fatigue loads are evaluated by means of Sobol sensitivity indices. Of the surrogate-model methods, polynomial chaos expansion provides an accurate and robust performance in prediction of the different site-specific loads. Although the Kriging approach showed slightly better accuracy, it also demanded more computational resources.

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Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads

Wind Energy Science ◽

10.5194/wes-4-479-2019 ◽

2019 ◽

Vol 4 (3) ◽

pp. 479-513 ◽

Cited By ~ 2

Author(s):

Amy N. Robertson ◽

Kelsey Shaler ◽

Latha Sethuraman ◽

Jason Jonkman

Keyword(s):

Sensitivity Analysis ◽

Wind Turbine ◽

Lift Coefficient ◽

International Electrotechnical Commission ◽

Individual Parameter ◽

Aerodynamic Properties ◽

Fatigue Loads ◽

Sensitive Parameters ◽

The Impact ◽

Inflow Conditions

Abstract. Proper wind turbine design relies on the ability to accurately predict ultimate and fatigue loads of turbines. The load analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An elementary effects sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1) wind-inflow conditions and (2) turbine structural and aerodynamic properties, both cases using the National Renewable Energy Laboratory 5 MW baseline wind turbine. The Veers model was used to generate synthetic International Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is on individual parameter sensitivity, though interactions between parameters are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u-direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This information can be used to help establish uncertainty bars around the predictions of engineering models during validation efforts, and provide insight to probabilistic design methods and site-suitability analyses.

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T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Energies ◽

10.3390/en13061306 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1306 ◽

Cited By ~ 1

Author(s):

Christos Galinos ◽

Jonas Kazda ◽

Wai Hou Lio ◽

Gregor Giebel

Keyword(s):

Wind Turbine ◽

Fatigue Damage ◽

Surrogate Model ◽

Wind Farm ◽

Computationally Efficient ◽

Detailed Model ◽

Set Point ◽

Power Set ◽

Fatigue Loads ◽

Aeroelastic Simulations

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

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Comparison of the Blade Element Momentum Theory with Computational Fluid Dynamics for Wind Turbine Simulations in Turbulent Inflow

Applied Sciences ◽

10.3390/app8122513 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2513 ◽

Cited By ~ 7

Author(s):

Sebastian Ehrich ◽

Carl Schwarz ◽

Hamid Rahimi ◽

Bernhard Stoevesandt ◽

Joachim Peinke

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Standard Deviation ◽

Wind Turbine ◽

Cfd Simulation ◽

Wind Fields ◽

Turbulent Inflow ◽

Power Spectral ◽

Fatigue Loads ◽

Blade Element

In this work three different numerical methods are used to simulate a multi-megawatt class class wind turbine under turbulent inflow conditions. These methods are a blade resolved Computational Fluid Dynamics (CFD) simulation, an actuator line based CFD simulation and a Blade Element Momentum (BEM) approach with wind fields extracted from an empty CFD domain. For all three methods sectional and integral forces are investigated in terms of mean, standard deviation, power spectral density and fatigue loads. It is shown that the average axial and tangential forces are very similar in the mid span, but differ a lot near the root and tip, which is connected with smaller values for thrust and torque. The standard deviations in the sectional forces due to the turbulent wind fields are much higher almost everywhere for BEM than for the other two methods which leads to higher standard deviations in integral forces. The difference in the power spectral densities of sectional forces of all three methods depends highly on the radial position. However, the integral densities are in good agreement in the low frequency range for all methods. It is shown that the differences in the standard deviation between BEM and the CFD methods mainly stem from this part of the spectrum. Strong deviations are observed from 1.5 Hz onward. The fatigue loads of torque for the CFD based methods differ by only 0.4%, but BEM leads to a difference of up to 16%. For the thrust the BEM simulation results deviate by even 29% and the actuator line by 7% from the blade resolved case. An indication for a linear relation between standard deviation and fatigue loads for sectional as well as integral quantities is found.

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