Korn Saranyasoontorn

Design Loads for Wind Turbines Using the Environmental Contour Method

When interest is in establishing ultimate design loads for wind turbines such that a service life of, say, 20 years is assured, alternative procedures are available. One class of methods works by employing statistical loads extrapolation techniques following development first of 10-minute load maxima distributions (conditional on inflow parameters such as mean wind speed and turbulence intensity). The parametric conditional load distributions require extensive turbine response simulations over the entire inflow parameter range. We will refer to this first class of methods as the “parametric method.” An alternative method is based on traditional structural reliability concepts and isolates only a subset of interesting inflow parameter combinations that are easily first found by working backward from the target return period of interest. This so-called inverse reliability method can take on various forms depending on the number of variables that are modeled as random. An especially attractive form that separates inflow (environmental) variables from turbine load∕response variables and further neglects variability in the load variables given inflow is referred to as the environmental contour (EC) method. We shall show that the EC method requires considerably smaller amounts of computation than the parametric method. We compare accuracy and efficiency of the two methods in 1- and 20-year design out-of-plane blade bending loads at the root of two 1.5 MW turbines. Simulation models for these two turbines with contrasting features, in that one is stall-regulated and the other pitch-regulated, are used here. Refinements to the EC method that account for the effects of the neglected response variability are proposed to improve the turbine design load estimates.

A Comparison of Standard Coherence Models for Inflow Turbulence With Estimates from Field Measurements

The Long-term Inflow and Structural Test (LIST) program, managed by Sandia National Laboratories, Albuquerque, NM, is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a site near Bushland, Texas. With the objective of establishing correlations between structural response and inflow, previous studies have employed regression and other dependency analyses to attempt to relate loads to various inflow parameters. With these inflow parameters that may be thought of as single-point-in-space statistics that ignore the spatial nature of the inflow, no significant correlation was identified between load levels and any single inflow parameter or even any set of such parameters, beyond the mean and standard deviation of the hubheight horizontal wind speed. Accordingly, hence, we examine spatial statistics in the measured inflow of the LIST turbine by estimating the coherence for the three turbulence components (along-wind, across-wind, and vertical). We examine coherence spectra for both lateral and vertical separations and use the available ten-minute time series of the three components at several locations. The data obtained from spatial arrays on three main rowers located upwind from the test turbine as well as on two additional towers on either side of the main towers consist of 291 ten-minute records. Details regarding estimation of the coherence functions from limited data are discussed. Comparisons with standard coherence models available in the literature and provided in the International Electrotechnical Commission (IEC) guidelines are also discussed. It is found that the Davenport exponential coherence model may not be appropriate especially for modeling the coherence of the vertical turbulence component since it fails to account for reductions in coherence at low frequencies and over large separations. Results also show that the Mann uniform shear turbulence model predicts coherence spectra for all turbulence components and for different lateral separations better than the isotropic von Karman model. Finally, on studying the cross-coherence among pairs of turbulence components based on field data, it is found that the coherence observed between along-wind and vertical turbulence components is not predicted by the isotropic von Karman model while the Mann model appears to overestimate this cross-coherence.

Low-Dimensional Representations of Inflow Turbulence and Wind Turbine Response Using Proper Orthogonal Decomposition

A demonstration of the use of Proper Orthogonal Decomposition (POD) is presented for the identification of energetic modes that characterize the spatial random field describing the inflow turbulence experienced by a wind turbine. POD techniques are efficient because a limited number of such modes can often describe the preferred turbulence spatial patterns and they can be empirically developed using data from spatial arrays of sensed input/excitation. In this study, for demonstration purposes, rather than use field data, POD modes are derived by employing the covariance matrix estimated from simulations of the spatial inflow turbulence field based on standard spectral models. The efficiency of the method in deriving reduced-order representations of the along-wind turbulence field is investigated by studying the rate of convergence (to total energy in the turbulence field) that results from the use of different numbers of POD modes, and by comparing the frequency content of reconstructed fields derived from the modes. The National Wind Technology Center’s Advanced Research Turbine (ART) is employed in the examples presented, where both inflow turbulence and turbine response are studied with low-order representations based on a limited number of inflow POD modes. Results suggest that a small number of energetic modes can recover the low-frequency energy in the inflow turbulence field as well as in the turbine response measures studied. At higher frequencies, a larger number of modes are required to accurately describe the inflow turbulence. Blade turbine response variance and extremes, however, can be approximated by a comparably smaller number of modes due to diminished influence of higher frequencies.

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

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). @DOI: 10.1115/1.1796971#

ON ESTIMATION OF COHERENCE IN INFLOW TURBULENCE BASED ON FIELD MEASUREMENTS

The Long-Term Inflow and Structural Test (LIST) program, managed by Sandia National Laboratories, is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a site near Bushland, Texas. With the objective of establishing correlations between structural response and inflow, previous studies have employed regression and other dependency analyses to attempt to relate loads to various inflow parameters. With these inflow parameters that may thought of as single-point-in-space statistics that ignore the spatial nature of the inflow, no significant correlation was identified between load levels and any single inflow parameter or even any set of such parameters, beyond the mean and standard deviation of the hub-height horizontal wind speed. Accordingly, here, we examine spatial statistics in the measured inflow of the LIST turbine by estimating the coherence for the three turbulence components (along-wind, across-wind, and vertical). We examine coherence spectra for both lateral and vertical separations and use the available ten-minute time series of the three components at several locations. The data obtained from spatial arrays on three main towers located upwind from the test turbine as well as on two additional towers on either side of the main towers consist of 291 ten-minute records. Details regarding estimation of the coherence functions from limited data are discussed. Comparisons with standard coherence models available in the literature and provided in the IEC guidelines are also discussed.

On the Study of Uncertainty in Inflow Turbulence Model Parameters in Wind Turbine Applications

In stochastic simulation of in∞ow turbulence random flelds for wind turbine applications, one routinely employs standard spectral models specifled in terms of parameters that are usually provided as flxed values. Recent studies suggest that these parameters can exhibit signiflcant variability. As such, it is not unreasonable to expect that derived ∞ow flelds based on simulation with such spectral models can be in turn highly variable. Turbine load and performance variability could as well result. The aim here is to assess the extent of variability in derived in∞ow turbulence flelds that arises from accounting for the noted variability in spectral model parameters. Simulation of these parameters as random variables forms the basis of this study. A commercial-sized 1.5MW concept wind turbine is considered in the numerical studies. The variability in turbulence power spectral density functions at points on the rotor plane and in turbulence coherence functions for separations on the order of a rotor diameter is studied. Using time domain simulations, statistics of various wind turbine response measures are also studied where the focus is on the variability in gross statistics such as root-mean-square response and ten-minute extremes. While variability in in∞ow turbulence spectra can be great, the variability in turbine loads is generally considerably lower. One exception is for turbine yaw loads whose larger variability arises from its sensitivity to a coherence decay parameter that is itself highly variable. Finally, because reduced-order representations of turbulence random flelds using empirical orthogonal decomposition techniques can allow useful physical insights into spatial patterns of ∞ow, variability in the energy distribution and the shapes of such empirical eigenmodes is studied and a simplifled model is presented that retains key variable sources in a limited number of modes and that accurately preserves in∞ow turbulence fleld variability.

Low-Dimensional Representations of Wind Turbine Inflow Turbulence and Response using Proper Orthogonal Decomposition

A demonstration of the use of Proper Orthogonal Decomposition (POD) is presented for the identification of energetic modes that characterize the spatial random field describing the turbulence experienced by a wind turbine. POD techniques are efficient because a limited number of such modes can often describe the preferred turbulence spatial patterns and they can be empirically developed using data from spatial arrays of sensed input/excitation. In this study, for demonstration purposes, rather than use field data, POD modes are derived by employing the covariance matrix estimated from simulations of the spatial inflow turbulence field based on a Kaimal spectral model. The efficiency of the method in deriving reduced-order representations of the along-wind turbulence field is investigated by studying the rate of convergence (to total energy in the turbulence field) that results from the use of different numbers of POD modes, and by comparing the frequency content of reconstructed fields derived from the modes. The National Wind Technology Center’s Advanced Research Turbine (ART) is employed in the examples presented where both inflow turbulence and turbine response are studied with low-order representations based on a limited number of inflow POD modes. Results suggest that a small number of energetic modes can recover the low-frequency energy in the inflow turbulence field as well as in the turbine response measures studied. At higher frequencies, a larger number of modes is required to accurately describe the inflow turbulence. Blade turbine response variance and extremes, however, can be approximated by a comparably smaller number of modes due to diminished influence of higher frequencies.

A COMPARISON OF WIND TURBINE DESIGN LOADS IN DIFFERENT ENVIRONMENTS USING INVERSE RELIABILITY

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 (out-of-plane) bending loads at a blade root for a 600kW three-bladed 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 firstorder 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).