Advances in the Assessment of Wind Turbine Operating Extreme Loads via More Efficient Calculation Approaches

2017 ◽  
Author(s):  
Peter Graf ◽  
Rick Damiani ◽  
Katherine Dykes ◽  
Jason M. Jonkman
Keyword(s):  
Wind Turbine ◽  
Extreme Loads ◽  
Efficient Calculation

Global Responses and Loads Analysis of a 750-kW Semi-Submersible Floating Offshore Wind Turbine Under Extreme Environmental Conditions

2019 ◽  
Author(s):  
Thanh Dam Pham ◽  
Junbae Kim ◽  
Byoungcheon Seo ◽  
Rupesh Kumar ◽  
Youngjae Yu ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Environmental Conditions ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
East Sea ◽  
Offshore Wind Turbine ◽  
Test Model ◽  
Scaled Model ◽  
Floating Offshore Wind Turbine ◽  
Extreme Loads

Abstract A pilot floating offshore wind turbine project of Korea was proposed for installing in the East Sea of Korea. The prototype is a semisubmersible platform supporting a 750-kW wind turbine. A scaled model was tested in the basin tank of the University of Ulsan at scale ratio 1:40. The 750-kW floating offshore wind turbine was modeled by using the NREL-FAST code. Numerical results were validated by comparing with those of the test model. This paper analyzes dynamic responses and loads of the wind turbine system under extreme environmental conditions. Extreme environmental conditions based on metocean data of East Sea Korea. Extreme responses and extreme loads are important data for designing the structure of the 750 kW semi-submersible floating offshore wind turbine.

Probabilistic Analysis of List Data for the Estimation of Extreme Design Loads for Wind Turbine Components

2003 ◽  
Author(s):  
M. D. Pandey ◽  
H. J. Sutherland
Keyword(s):  
Wind Turbine ◽  
Sampling Error ◽  
Field Measurements ◽  
Structural Data ◽  
Value Theory ◽  
Extreme Value ◽  
Sampling Errors ◽  
Extreme Loads ◽  
Mean Wind Speed ◽  
Design Loads

Robust estimation of wind turbine design loads for service lifetimes of 30 to 50 years that are based on field measurements of a few days is a challenging problem. Estimating the long-term load distribution involves the integration of conditional distributions of extreme loads over the mean wind speed and turbulence intensity distributions. However, the accuracy of the statistical extrapolation is fairly sensitive to both model and sampling errors. Using measured inflow and structural data from the LIST program, this paper presents a comparative assessment of extreme loads using three distributions: namely, the Gumbel, Weibull and Generalized Extreme Value distributions. The paper uses L-moments, in place of traditional product moments, to reduce the sampling error. The paper discusses the application of extreme value theory and highlights its practical limitations. The proposed technique has the potential of improving estimates of the design loads for wind turbines.

Probabilistic Analysis of LIST Data for the Estimation of Extreme Design Loads for Wind Turbine Components*†

2003 ◽  
Vol 125 (4) ◽  
pp. 531-540 ◽  
Author(s):  
M. D. Pandey ◽  
H. J. Sutherland
Keyword(s):  
Wind Turbine ◽  
Sampling Error ◽  
Test List ◽  
Structural Data ◽  
Value Theory ◽  
Extreme Value ◽  
Sampling Errors ◽  
Extreme Loads ◽  
Design Loads

The robust estimation of wind turbine design loads for service lifetimes of 30 to 50 years that are based on limited field measurements is a challenging problem. Estimating the long-term load distribution involves the integration of conditional distributions of extreme loads over the mean wind speed and turbulence intensity distributions. However, the accuracy of the statistical extrapolation can be sensitive to both model and sampling errors. Using measured inflow and structural data from the Long Term Inflow and Structural Test (LIST) program, this paper presents a comparative assessment of extreme loads using three distributions: namely, the Gumbel, Weibull and Generalized Extreme Value distributions. The paper uses L-moments, in place of traditional product moments, with the purpose of reducing the sampling error. The paper discusses the effects of modeling and sampling errors and highlights the practical limitations of extreme value theory.

Long-Term Extreme Load Prediction of Spar and Semisubmersible Floating Wind Turbines Using the Environmental Contour Method

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
D. Karmakar ◽  
Hasan Bagbanci ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Contour Method ◽  
Return Periods ◽  
Extreme Loads ◽  
Floating Wind Turbine ◽  
Extreme Load ◽  
Design Loads ◽  
Offshore Floating Wind Turbine

The prediction of extreme loads for the offshore floating wind turbine is analyzed based on the inverse reliability technique. The inverse reliability approach is in general used to establish the design levels associated with the specified probability of failure. The present study is performed using the environmental contour (EC) method to estimate the long-term joint probability distribution of extreme loads for different types of offshore floating wind turbines. The analysis is carried out in order to predict the out-of-plane bending moment (OoPBM) loads at the blade root and tower base moment (TBM) loads for a 5 MW offshore floating wind turbine of different floater configuration. The spar-type and semisubmersible type offshore floating wind turbines are considered for the analysis. The FAST code is used to simulate the wind conditions for various return periods and the design loads of various floating wind turbine configurations. The extreme and operation situation of the spar-type and semisubmersible type offshore floating wind turbine are analyzed using one-dimensional (1D) and two-dimensional (2D)-EC methods for different return periods. The study is useful to predict long-term design loads for offshore wind turbines without requiring excessive computational effort.

scholarly journals Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

2017 ◽  
Author(s):  
Peter Graf ◽  
Katherine Dykes ◽  
Rick Damiani ◽  
Jason Jonkman ◽  
Paul Veers
Keyword(s):  
Monte Carlo ◽  
Stochastic Optimization ◽  
Wind Turbine ◽  
Importance Sampling ◽  
Monte Carlo Methods ◽  
Environmental Conditions ◽  
Response Variability ◽  
Adaptive Procedure ◽  
Extreme Loads ◽  
Extreme Response

Abstract. Wind turbine extreme loads estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies, so there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. Here we introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability of turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.

scholarly journals An engineering model for 3D turbulent wind inflow based on a limited set of random variables

2017 ◽  
Author(s):  
Manuel Fluck ◽  
Curran Crawford
Keyword(s):  
Wind Turbine ◽  
Random Variables ◽  
Stochastic Approach ◽  
Turbine Rotor ◽  
Stochastic Simulations ◽  
Stochastic Methods ◽  
Extreme Loads ◽  
Commercial Code ◽  
Loads Analysis ◽  
Term Performance

Abstract. Emerging stochastic analysis methods are of potentially great benefit for wind turbine power output and loads analysis. Instead of requiring multiple (e.g. ten-minute) deterministic simulations, a stochastic approach can enable quick assessment of a turbine's long term performance (e.g. 20 year fatigue and extreme loads) from a single stochastic simulation. However, even though the wind inflow is often described as a stochastic process, the common spectral formulation requires a large number of random variables to be considered. This is a major issue for stochastic methods, which suffer from the curse of dimensionality leading to a steep performance drop with an increasing number of random variables contained in the governing equations. In this paper a novel engineering wind model is developed which reduces the number of random variables by 4–5 orders of magnitude compared to typical models while retaining proper spatial correlation of wind speed sample points across a wind turbine rotor. The new model can then be used as input to direct stochastic simulations models under development. A comparison of the new method to results from the commercial code TurbSim and a custom implementation of the standard spectral model shows that for a 3D wind field the most important properties (cross-correlation, covariance, auto- and cross-spectrum) are conserved adequately by the proposed method.

scholarly journals Assessment of wind turbine component loads under yaw-offset conditions

2018 ◽  
Vol 3 (1) ◽  
pp. 173-189 ◽  
Author(s):  
Rick Damiani ◽  
Scott Dana ◽  
Jennifer Annoni ◽  
Paul Fleming ◽  
Jason Roadman ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Power Plants ◽  
Clear Understanding ◽  
Analytical Treatment ◽  
Yaw Control ◽  
Extreme Loads ◽  
Turbine Component ◽  
Speed Regime ◽  
Aeroelastic Simulations ◽  
Utility Scale

Abstract. Renewed interest in yaw control for wind turbine and power plants for wake redirection and load mitigation demands a clear understanding of the effects of running with skewed inflow. In this paper, we investigate the physics of yawed operations, building up the complexity from a simplified analytical treatment to more complex aeroelastic simulations. Results in terms of damage equivalent loads (DELs) and extreme loads under misaligned conditions of operation are compared to data collected from an instrumented, utility-scale wind turbine. The analysis shows that multiple factors are responsible for the DELs of the various components and that airfoil aerodynamics, elastic characteristics of the rotor, and turbulence intensities are the primary drivers. Both fatigue and extreme loads are observed to have relatively complex trends with yaw offsets, which can change depending on the wind-speed regime. Good agreement is found between predicted and measured trends for both fatigue and ultimate loads.

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