Analysis of the Structural and Inflow Data From the List Turbine

2002 ◽  
Vol 124 (4) ◽  
pp. 432-445 ◽  
Author(s):  
Herbert J. Sutherland
Keyword(s):  
Test List ◽  
Vertical Component ◽  
Structural Response ◽  
Horizontal Wind ◽  
Reynolds Stresses ◽  
Load Spectrum ◽  
Fatigue Load ◽  
Horizontal Wind Speed ◽  
Fatigue Loads

The Long-term Inflow and Structural Test (LIST) program is collecting long-term, continuous inflow and structural response data to characterize the spectrum of loads on wind turbines. A heavily instrumented Micon 65/13M turbine with Phoenix 8m blades is being used as the test turbine for the first measurement campaign of this program. This turbine is located in Bushland, TX, a test site that exposes the turbine to a wind regime representative of a Great Plains commercial site. The turbine and inflow are being characterized with 60 measurements: 34 to characterize the inflow, 19 to characterize structural response, and seven to characterize the time-varying state of the turbine. In this paper, an analysis of the structural and inflow data is presented. Particular attention is paid to the determination of the various structural loads on the turbine, long-term fatigue spectra and the correlation of various inflow descriptors with fatigue loads. For the latter analysis, the inflow is described by various parameters, including the mean, standard deviation, skewness and kurtosis of the hub-height horizontal wind speed, turbulence intensity, turbulence length scales, Reynolds stresses, local friction velocity, Obukhov length, and the gradient Richardson number. The fatigue load spectrum corresponding to these parameters is characterized as an equivalent fatigue load. A regression analysis is then used to determine which parameters are correlated to the fatigue loads. The results illustrate that the vertical component of the inflow is the most important of the secondary inflow parameters with respect to fatigue loads. Long-term fatigue spectra illustrate that extrapolation of relatively short-term data to longer times is consistent for the data reported here.

Inflow and the Fatigue of the LIST Wind Turbine

2002 ◽  
Author(s):  
Herbert J. Sutherland
Keyword(s):  
Test List ◽  
Vertical Component ◽  
Structural Response ◽  
Structural Data ◽  
Reynolds Stresses ◽  
Load Spectrum ◽  
Fatigue Load ◽  
Obukhov Length ◽  
Fatigue Loads

The Long-term Inflow and Structural Test (LIST) program is collecting long-term, continuous inflow and structural response data to characterize the spectrum of loads on wind turbines. A heavily instrumented Micon 65/13M turbine with SERI 8m blades is being used as the primary test turbine for this test. This turbine is located in Bushland, TX, a test site that exposes the turbine to a wind regime representative of a Great Plains commercial site. The turbine and inflow are being characterized with 60 measurements: 34 to characterize the inflow, 19 to characterize structural response, and 7 to characterize the time-varying state of the turbine. In this paper, the inflow and structural data from this measurement campaign are analyzed to determine the correlation of various inflow descriptors with fatigue loads. The inflow is described by various parameters, including the mean, standard deviation, skewness and kurtosis of the wind speed, turbulence intensity, turbulence length scales, Reynolds stresses, local friction velocity, Obukhov length and the gradient Richardson number. The fatigue load spectrum corresponding to these parameters is characterized as an equivalent fatigue load. A regression analysis is then used to determine which parameters are correlated to the fatigue loads. The results illustrate that the vertical component of the inflow is the most important of the secondary inflow parameters on fatigue loads. Long-term fatigue spectra illustrate that extrapolation of relatively short-term data to longer times is consistent for the data reported here.

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

2004 ◽  
Vol 126 (4) ◽  
pp. 1069-1082 ◽  
Author(s):  
Korn Saranyasoontorn ◽  
Lance Manuel ◽  
Paul S. Veers
Keyword(s):  
Single Point ◽  
Test List ◽  
Structural Response ◽  
Field Measurements ◽  
Horizontal Wind ◽  
International Electrotechnical Commission ◽  
Horizontal Wind Speed ◽  
Low Frequencies ◽  
Inflow Turbulence ◽  
Vertical Turbulence

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 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 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 Ka´rma´n 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 Ka´rma´n model while the Mann model appears to overestimate this cross-coherence.

Inflow and Fatigue Response of the NWTC Advanced Research Turbine

2003 ◽  
Author(s):  
Herbert J. Sutherland ◽  
Neil D. Kelley ◽  
M. Maureen Hand
Keyword(s):  
Test List ◽  
Structural Response ◽  
Synchronous Generator ◽  
Measurement Unit ◽  
Response Data ◽  
Extreme Loads ◽  
Sonic Anemometers ◽  
Planar Array ◽  
Fatigue Loads

The Long-term Inflow and Structural Test (LIST) program is collecting long-term inflow and structural response data to characterize the spectrum of loads on wind turbines. In one of the measurement campaigns being conducted under this program, the 42-m diameter, 600-kW NWTC Advanced Research Turbine (ART) was monitored. The turbine is an upwind, two-bladed teetered-hub machine. It has full span pitch control and a synchronous generator. The inflow was monitored with a planar array of five high-resolution sonic anemometers and supporting meteorological instrumentation located 1.5 diameters upwind of the turbine. The structural response of the turbine was measured using strain gauge circuits and an inertial measurement unit (IMU). The former were used to monitor root bending moments and the low-speed shaft torque, while the latter was used to monitor the motion of the tower and the nacelle. Auxiliary gauges measured blade pitch, rotor teeter, nacelle yaw and generator power. A total of 3299 10-minute records were collected for analysis. From this set, 1044 records are used to examine the influence of various inflow parameters on fatigue loads. Long-term fatigue loads and extreme loads are also examined.

scholarly journals Statistical Analysis of Inflow and Structural Response Data From the LIST Program

2003 ◽  
Author(s):  
Luke D. Nelson ◽  
Lance Manuel ◽  
Herbert J. Sutherland ◽  
Paul S. Veers
Keyword(s):  
Wind Speed ◽  
Sequential Analysis ◽  
Test List ◽  
Structural Response ◽  
Vertical Shear ◽  
Horizontal Wind ◽  
Parameter Vector ◽  
Time Data ◽  
Response Data ◽  
Extreme Load

The Long-Term Inflow and Structural Test (LIST) program is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a test site near Bushland, Texas. Data from 491 ten-minute time data records are analyzed here to determine the dependency of fatigue and extreme loads on inflow parameters. Flap and edge bending moment ranges at a blade root are chosen as the structural response variable, z. Various parameters related to the inflow (including, for example, primary parameters, the mean and standard deviation of the hub-height horizontal wind speed, and secondary parameters, Reynolds stresses, vertical shear exponent, etc.) are each considered in an inflow parameter vector, x. Time series for the structural response, z, are processed in order to obtain a structural response parameter, y, where in separate statistical studies, y is taken to be either an equivalent fatigue load or an extreme load. This paper first describes a procedure by which the important “dependencies” of y on the various variables contained in the inflow parameter vector, x, may be determined considering all the available data. These dependencies of y on x are then recomputed using only the data with above-rated mean wind speeds (taken to be approximately 13 m/s). The procedure employed is similar to other previous studies, but we do not bin the data sets by wind speed since dependencies in one wind speed bin may be different from those in other bins. Also, our procedure, in sharp contrast to previous studies, examines each inflow parameter in the vector, x, in a sequential analysis, rather than by using multivariate regression.

Statistical Analysis of Wind Turbine Inflow and Structural Response Data from the LIST Program

2003 ◽  
Vol 125 (4) ◽  
pp. 541-550 ◽  
Author(s):  
Luke D. Nelson ◽  
Lance Manuel ◽  
Herbert J. Sutherland ◽  
Paul S. Veers
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Test List ◽  
Structural Response ◽  
Vertical Shear ◽  
Horizontal Wind ◽  
Parameter Vector ◽  
Response Data ◽  
Extreme Loads ◽  
Extreme Load

The Long-Term Inflow and Structural Test (LIST) program is gathering inflow and structural response data on a modified version of the Micon 65/13 wind turbine at a test site near Bushland, Texas. Data from 491 ten-minute time data records are analyzed here to determine the dependency of fatigue and extreme loads on inflow parameters. Flap and edge bending moment ranges at a blade root are chosen as the structural response variable, z. Various parameters related to the inflow (including, for example, primary parameters such as the mean and standard deviation of the hub-height horizontal wind speed, and secondary parameters such as Reynolds stresses, vertical shear exponent, etc.) are each considered in an inflow parameter vector, x. Time series for the structural response, z, are processed in order to obtain a structural response parameter, y, where in separate statistical studies, y is taken to be either an equivalent fatigue load or an extreme load. This study describes a procedure by which the important “dependencies” of y on the various variables contained in the inflow parameter vector, x, may be determined considering all the available data. These dependencies of y on x are then recomputed using only the data with above-rated mean wind speeds (taken to be approximately 13 m/s). The procedure employed is similar to other studies, but we do not bin the data sets by wind speed since dependencies in one wind speed bin may be different from those in other bins. Also, our procedure, in sharp contrast to previous studies, examines each inflow parameter in the vector, x, in a sequential analysis, rather than by using multivariate regression. Results from the present study suggest that the primary inflow parameters have a small amount of predictive power in establishing fatigue and extreme loads. In addition, large correlations that exist between several of the secondary parameters individually and each of the primary parameters make it difficult for the secondary parameters to provide any additional explanation of turbine response once the primary parameters have been accounted for.

scholarly journals Characteristics of Large-Scale Orographic Precipitation in a Linear Perspective

2011 ◽  
Vol 12 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Michael Kunz
Keyword(s):  
Time Scales ◽  
Characteristic Time ◽  
Large Scale ◽  
Linear Perspective ◽  
Horizontal Wind ◽  
Small Scale ◽  
Orographic Precipitation ◽  
Observation Data ◽  
Horizontal Wind Speed ◽  
Precipitation Patterns

Abstract Simulations of orographic precipitation over the low mountain ranges of southwestern Germany and eastern France with two different physics-based linear precipitation models are presented. Both models are based on 3D airflow dynamics from linear theory and consider advection of condensed water and leeside drying. Sensitivity studies for idealized conditions and a real case study show that the amount and spatial distribution of orographic precipitation is strongly controlled by characteristic time scales for cloud and hydrometeor advection and background precipitation due to large-scale lifting. These parameters are estimated by adjusting the model results on a 2.5-km grid to observed precipitation patterns for a sample of 40 representative orography-dominated stratiform events (24 h) during a calibration period (1971–80). In general, the best results in terms of lowest rmse and bias are obtained for characteristic time scales of 1600 s and background precipitation of 0.4 mm h−1. Model simulations of a sample of 84 events during an application period (1981–2000) with fixed parameters demonstrate that both models are able to reproduce quantitatively precipitation patterns obtained from observations and reanalyses from a numerical model [Consortium for Small-scale Modeling (COSMO)]. Combining model results with observation data shows that heavy precipitations over mountains are restricted to situations with strong atmospheric forcings in terms of synoptic-scale lifting, horizontal wind speed, and moisture content.

scholarly journals Doppler wind lidar activities at Cabauw

2021 ◽  
Author(s):  
Steven Knoop ◽  
Fred Bosveld ◽  
Marijn de Haij ◽  
Arnoud Apituley
Keyword(s):  
Wind Speed ◽  
Wind Profile ◽  
Continuous Wave ◽  
Wind Farms ◽  
Data Availability ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Low Clouds ◽  
Wind Measurements ◽  
Wind Lidar

<p>Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 µm and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.</p><p>We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.</p><p>Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.</p><p>[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, 2021</p><p>[2] https://ruisdael-observatory.nl/</p>

scholarly journals Comparison of Large Eddy Simulations of a convective boundary layer with wind LIDAR measurements

2012 ◽  
Vol 8 (1) ◽  
pp. 83-86 ◽  
Author(s):  
J. G. Pedersen ◽  
M. Kelly ◽  
S.-E. Gryning ◽  
R. Floors ◽  
E. Batchvarova ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Geostrophic Wind ◽  
Large Eddy Simulations ◽  
Horizontal Wind ◽  
Convective Boundary ◽  
Horizontal Wind Speed ◽  
Large Eddy ◽  
Lidar Measurements ◽  
Wind Lidar

Abstract. Vertical profiles of the horizontal wind speed and of the standard deviation of vertical wind speed from Large Eddy Simulations of a convective atmospheric boundary layer are compared to wind LIDAR measurements up to 1400 m. Fair agreement regarding both types of profiles is observed only when the simulated flow is driven by a both time- and height-dependent geostrophic wind and a time-dependent surface heat flux. This underlines the importance of mesoscale effects when the flow above the atmospheric surface layer is simulated with a computational fluid dynamics model.

scholarly journals Wind profile correction algorithm based on Atmospheric Boundary Layer stability profile

2021 ◽  
Author(s):  
Francisco Albuquerque Neto ◽  
Vinicius Almeida ◽  
Julia Carelli
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Profile ◽  
Horizontal Wind ◽  
Boundary Layer Stability ◽  
Correction Algorithm ◽  
Horizontal Wind Speed ◽  
Study Region ◽  
Profile Correction ◽  
Wind Profiles

<p>In recent years, the use of radar wind profilers (RWP) at airports has grown significantly with the aim of supporting decision makers to maintain the safety of aircraft landings and takeoffs.</p><p>The RWP provide vertical profiles of averaged horizontal wind speed and direction and vertical wind velocity for the entire Atmospheric Boundary Layer (ABL) and beyond. In addition, RWP with Radio-Acoustic Sounding System (RASS) are able to retrieve virtual temperature profiles in the ABL.</p><p>RWP data evaluation is usually based on the so-called Doppler Beam Swinging method (DBS) which assumes homogeneity and stationarity of the wind field. Often, transient eddies violate this homogeneity and stationarity requirement. Hence, incorrect wind profiles can relate to transient eddies and present a problem for the forecast of high-impact weather phenomena in airports. This work intends to provide a method for removing outliers in such profiles based on historical data and other variables related to the Atmospheric Boundary Layer stability profile in the study region.</p><p>For this study, a dataset of almost one year retrieved from a RWP LAP3000 with RASS Extension is used for a wind profile correction algorithm development.</p><p>The algorithm consists of the detection of outliers in the wind profiles based on the thermodynamic structure of the ABL and the generation of the corrected profiles.</p><p>Results show that the algorithm is capable of identifying and correcting unrealistic variations in speed caused by transient eddies. The method can be applied as a complement to the RWP data processing for better data reliability.</p><p> </p><p>Keywords: atmospheric boundary layer; stability profile; wind profile</p>

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