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 The Potential Wind Power Resource in Central-South of the Constanta County

2019 ◽  
Vol 15 (3) ◽  
pp. 1-12
Author(s):  
Emilian Boboc
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Extreme Values ◽  
Reanalysis Data ◽  
Value Theory ◽  
Physical Models ◽  
Structural Safety ◽  
Energy Yield ◽  
Wind Speeds ◽  
Mean Wind Speed

Abstract Usually, wind turbine generator’s structures or radio masts are located in wind exposed sites. The paper aims to investigate the wind conditions in the nearby area of Cobadin Commune, Constanta County, Romania at heights of 150-200m above the surface using global reanalysis data sets CFSR, ERA 5, ERA I and MERRA 2. Using the extreme value theory and the physical models of the datasets, the research focuses on the assessment of the maximum values that are expected for the wind speeds, but the wind statistics created can be used for a further wind or energy yield calculation. Without reaching the survival wind speed for wind turbine generators, with mean wind speed values higher than 7 m/s and considering the cut-in and cut-out wind speeds of 3 m/s, respectively 25 m/s, the site can be exploited in more than 90% of the time to generate electricity, thus, the paper is addressed to the investors in the energy of renewable sources. At the same time, the insights of the wind characteristics and the knowledge of the extreme values of the wind speed can be useful, not just for the designers, in the rational assessment of the structural safety of wind turbines, but also those evaluating the insured losses.

Mode-decomposing Analysis of the Extreme Load in Hybrid Electric Vehicles Using Extreme Value Theory

2016 ◽  
Vol 10 (1) ◽  
pp. 136-147
Author(s):  
Jian Zhou ◽  
Jixin Wang ◽  
Hongbin Chen
Keyword(s):  
Extreme Value Theory ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
New Thought ◽  
Extreme Loads ◽  
Extreme Load ◽  
Hybrid Electric

In a hybrid electric vehicle (HEV), the hybrid system, which is equipped with an engine and a motor, is a key component. However, given the multimode characteristics of HEV, the original extreme load of the engine or motor is not independent and the random variables cannot be directly fitted by the extreme value theory (EVT). Thus, this paper proposes a mode-decomposing application method (MDAM) using EVT. Based on the method, three typical distributions, including the Fréchet distribution, the Gumbel distribution, and the Weibull distribution, were combined as a unified expression, and it was adopted to fit the extreme loads within different modes of HEV. By comparing the fitting results, especially the shapes of the curves, the distributions of the load under different modes vary from each other, so the feasibility and necessity of MDAM in HEV are proved, and a new thought for fitting the extreme load in HEV is provided, which will contribute to improve the fitting accuracy.

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.

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.

Wind Turbine Drivetrain Test Bench Capability to Replicate Design Loads: Part I — Evaluation Methodology

2017 ◽  
Author(s):  
Philippe Giguère ◽  
John R. Wagner
Keyword(s):  
Wind Turbine ◽  
Test Bench ◽  
Task Force ◽  
Field Measurements ◽  
Evaluation Methodology ◽  
Systematic Method ◽  
Mechanical Loads ◽  
Testing Facility ◽  
Design Loads ◽  
Replicate Design

The utilization of a ground-based testing facility for full-size wind turbine drivetrains is growing. Several test benches have been developed to apply torque and non-torque loads. These mechanical loads can be the loads used to design the drivetrain components or loads obtained from field measurements. Irrespective of the reason for testing a drivetrain, the selected test bench should have the capability to impose the loads of interest. The design of these test benches and their capabilities vary, and the loads of interest vary between drivetrain designs. A systematic method to evaluate the capability of a test bench to impose the loads of interest has been developed. This method can be applied to any test bench and drivetrain design. Part I of this paper presents the methodology and recommendations for presenting and interpreting the results. The demonstration of the method is the focus of part II. Overall, this two-part paper aims to establish guidelines for consideration by the IEA task force 35 for ground based testing for wind turbines and their components.

Extreme Value Theory and Archimedean Copulas

2004 ◽  
Vol 2004 (3) ◽  
pp. 211-228 ◽  
Author(s):  
Mario V. Wüthrich
Keyword(s):  
Extreme Value Theory ◽  
Value Theory ◽  
Extreme Value ◽  
Archimedean Copulas

Extreme Value Theory and Dependence.

1985 ◽  
Author(s):  
M. R. Leadbetter
Keyword(s):  
Extreme Value Theory ◽  
Value Theory ◽  
Extreme Value
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