Predicting Design Wind Turbine Loads from Limited Data: Comparing Random Process and Random Peak Models

2001 ◽  
Vol 123 (4) ◽  
pp. 364-371 ◽  
Author(s):  
LeRoy M. Fitzwater ◽  
Steven R. Winterstein
Keyword(s):  
Wind Turbine ◽  
Random Process ◽  
Process Model ◽  
Probability Model ◽  
Probability Models ◽  
Extreme Loads ◽  
Design Loads ◽  
Bending Loads ◽  
Turbine Design ◽  
Peak Model

This paper considers two distinct topics that arise in reliability-based wind turbine design. First, it illustrates how general probability models can be used to predict long-term design loads from a set of limited-duration, short-term load histories. Second, it considers in detail the precise choice of probability model to be adopted, for both flap and edge bending loads in both parked and operating turbine conditions. In particular, a 3-moment random peak model and a 3- or 4-moment random process model are applied and compared. For a parked turbine, all models are found to be virtually unbiased and to notably reduce uncertainty in estimating extreme loads (e.g., by roughly 50%). For an operating turbine, however, only the random peak model is found to retain these beneficial features. This suggests the advantage of the random peak model, which appears to capture the rotating blade behavior sufficiently well to accurately predict extremes.

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.

Simulation of Offshore Wind Turbine Response for Extreme Limit States

2007 ◽  
Author(s):  
P. Agarwal ◽  
L. Manuel
Keyword(s):  
Wind Turbine ◽  
Environmental Conditions ◽  
Response Variability ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Threshold Method ◽  
Conditional Response ◽  
Extreme Loads ◽  
Design Loads

When interest is in estimating long-term design loads for an offshore wind turbine using simulation, statistical extrapolation is the method of choice. While the method itself is rather well-established, simulation effort can be intractable if uncertainty in predicted extreme loads and efficiency in the selected extrapolation procedure are not specifically addressed. Our aim in this study is to address these questions in predicting blade and tower extreme loads based on stochastic response simulations of a 5 MW offshore turbine. We illustrate the use of the peak-over-threshold method to predict long-term extreme loads. To derive these long-term loads, we employ an efficient inverse reliability approach which is shown to predict reasonably accurate long-term loads when compared to the more expensive direct integration of conditional load distributions for different environmental (wind and wave) conditions. Fundamental to the inverse reliability approach is the issue of whether turbine response variability conditional on environmental conditions is modeled in detail or whether only gross conditional statistics of this conditional response are included. We derive design loads for both these cases, and demonstrate that careful inclusion of response variability not only greatly influences long-term design load predictions but it also identifies different design environmental conditions that bring about these long-term loads compared to when response variability is only approximately modeled. As we shall see, for this turbine, a major source of response variability for both the blade and tower arises from blade pitch control actions due to which a large number of simulations is required to obtain stable distribution tails for the turbine loads studied.

scholarly journals Validation and accommodation of vortex wake codes for wind turbine design load calculations

2020 ◽  
Author(s):  
Koen Boorsma ◽  
Florian Wenz ◽  
Koert Lindenburg ◽  
Mansoor Aman ◽  
Menno Kloosterman
Keyword(s):  
Wind Turbine ◽  
Current Trend ◽  
Fatigue Loading ◽  
Computational Effort ◽  
Vortex Wake ◽  
Fatigue Load ◽  
Rotor Aerodynamics ◽  
Design Loads ◽  
Turbine Design ◽  
Blade Element

Abstract. The computational effort for wind turbine design loads calculations is more extreme than it is for other applications (e.g. aerospace) which necessitates the use of efficient but low-fidelity models. Traditionally the Blade Element Momentum (BEM) method is used to resolve the rotor aerodynamics loads for this purpose, as this method is fast and robust. With the current trend of increasing rotor size, and consequently large and flexible blades, a need has risen for a more accurate prediction of rotor aerodynamics. Previous work has demonstrated large improvement potential in terms of fatigue load predictions using vortex wake models together with a manageable penalty in computational effort. The present publication has contributed towards making vortex wake models ready for application to certification load calculations. The observed reduction in flapwise blade root moment fatigue loading using vortex wake models instead of the Blade Element Momentum method from previous publications has been verified using a numerical wind tunnel, i.e. Computational Fluid Dynamics (CFD) simulations. A validation effort against a long term field measurement campaign featuring 2.5 MW turbines has also confirmed the improved prediction of unsteady load characteristics by vortex wake models against BEM based models in terms of fatigue loading. New light has been shed on the cause for the observed differences and several model improvements have been developed, both to reduce the computational effort of vortex wake simulations and to make BEM models more accurate. Scoping analyses for an entire fatigue load set have revealed the overall fatigue reduction may be up to 5 % for the AVATAR 10 MW rotor using a vortex wake rather than a BEM based code.

Statistical Extrapolation Methods for Estimating Wind Turbine Extreme Loads

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Patrick Ragan ◽  
Lance Manuel
Keyword(s):  
Wind Turbine ◽  
Process Model ◽  
Field Data ◽  
International Electrotechnical Commission ◽  
Threshold Method ◽  
Extreme Loads ◽  
Peak Over Threshold ◽  
Peak Over Threshold Method ◽  
Superior Approach ◽  
Utility Scale

With the introduction of the third edition of the International Electrotechnical Commission (IEC) Standard 61400-1, designers of wind turbines are now explicitly required, in one of the prescribed load cases, to use statistical extrapolation techniques to determine nominal design loads. In this study, we use field data from a utility-scale 1.5MW turbine sited in Lamar, Colorado to compare the performance of several alternative techniques for statistical extrapolation of rotor and tower loads—these include the method of global maxima, the peak-over-threshold method, and a four-moment process model approach. Using each of these three options, 50-year return loads are estimated for the selected wind turbine. We conclude that the peak-over-threshold method is the superior approach, and we examine important details intrinsic to this method, including selection of the level of the threshold to be employed, the parametric distribution used in fitting, and the assumption of statistical independence between successive peaks. While we are primarily interested in the prediction of extreme loads, we are also interested in assessing the uncertainty in our predictions as a function of the amount of data used. Towards this end, we first obtain estimates of extreme loads associated with target reliability levels by making use of all of the data available, and then we obtain similar estimates using only subsets of the data. From these separate estimates, conclusions are made regarding what constitutes a sufficient amount of data upon which to base a statistical extrapolation. While this study makes use of field data in addressing statistical load extrapolation issues, the findings should also be useful in simulation-based attempts at deriving wind turbine design load levels where similar questions regarding extrapolation techniques, distribution choices, and amount of data needed are just as relevant.

A Comparison of Wind Turbine Design Loads in Different Environments Using Inverse Reliability Techniques

2004 ◽  
Vol 126 (4) ◽  
pp. 1060-1068 ◽  
Author(s):  
Korn Saranyasoontorn ◽  
Lance Manuel
Keyword(s):  
Wind Turbine ◽  
Limit State ◽  
Response Variability ◽  
Horizontal Axis Wind Turbine ◽  
Wind Environment ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method ◽  
Design Loads ◽  
Bending Loads

The influence of turbulence conditions on the design loads for wind turbines is investigated by using inverse reliability techniques. Alternative modeling assumptions for randomness in the gross wind environment and in the extreme response given wind conditions to establish nominal design loads are studied. Accuracy in design load predictions based on use of the inverse first-order reliability method (that assumes a linearized limit state surface) is also investigated. An example is presented where three alternative nominal load definitions are used to estimate extreme flapwise bending loads at a blade root for a 600 kW three-bladed, stall-regulated horizontal-axis wind turbine located at onshore and offshore sites that were assumed to experience the same mean wind speed but different turbulence intensities. It is found that second-order (curvature-type) corrections to the linearized limit state function assumption inherent in the inverse first-order reliability approach are insignificant. Thus, we suggest that the inverse first-order reliability method is an efficient and accurate technique of predicting extreme loads. Design loads derived from a full random characterization of wind conditions as well as short-term maximum response (given wind conditions) may be approximated reasonably well by simpler models that include only the randomness in the wind environment but account for response variability by employing appropriately derived “higher-than-median” fractiles of the extreme bending loads conditional on specified inflow parameters. In the various results discussed, it is found that the higher relative turbulence at the onshore site leads to larger blade bending design loads there than at the offshore site. Also, for both onshore and offshore environments accounting for response variability is found to be slightly more important at longer return periods (i.e., safer designs).

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