Influence of Atmospheric Stability on Wind Turbine Power Performance Curves

2006 ◽  
Vol 128 (4) ◽  
pp. 531-538 ◽  
Author(s):  
Jonathon Sumner ◽  
Christian Masson
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Similarity Theory ◽  
Atmospheric Stability ◽  
Performance Testing ◽  
System Of Nonlinear Equations ◽  
Power Performance ◽  
Turbine Efficiency ◽  
The Impact ◽  
Power Curves

The impact of atmospheric stability on vertical wind profiles is reviewed and the implications for power performance testing and site evaluation are investigated. Velocity, temperature, and turbulence intensity profiles are generated using the model presented by Sumner and Masson. This technique couples Monin-Obukhov similarity theory with an algebraic turbulence equation derived from the k-ϵ turbulence model to resolve atmospheric parameters u*, L, T*, and z0. The resulting system of nonlinear equations is solved with a Newton-Raphson algorithm. The disk-averaged wind speed u¯disk is then evaluated by numerically integrating the resulting velocity profile over the swept area of the rotor. Power performance and annual energy production (AEP) calculations for a Vestas Windane-34 turbine from a wind farm in Delabole, England, are carried out using both disk-averaged and hub height wind speeds. Although the power curves generated with each wind speed definition show only slight differences, there is an appreciable impact on the measured maximum turbine efficiency. Furthermore, when the Weibull parameters for the site are recalculated using u¯disk, the AEP prediction using the modified parameters falls by nearly 5% compared to current methods. The IEC assumption that the hub height wind speed can be considered representative tends to underestimate maximum turbine efficiency. When this assumption is further applied to energy predictions, it appears that the tendency is to overestimate the site potential.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, the number of wind turbines per model grid cell, and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

scholarly journals Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1486 ◽  
Author(s):  
Nicolas Tobin ◽  
Adam Lavely ◽  
Sven Schmitz ◽  
Leonardo P. Chamorro
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Temporal Correlations ◽  
Theoretical Predictions ◽  
Power Variance ◽  
Ambient Turbulence ◽  
The Impact

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

scholarly journals Wind turbine power production and annual energy production depend on atmospheric stability and turbulence

2016 ◽  
Author(s):  
Clara M. St. Martin ◽  
Julie K. Lundquist ◽  
Andrew Clifton ◽  
Gregory S. Poulos ◽  
Scott J. Schreck
Keyword(s):  
Energy Production ◽  
Atmospheric Stability ◽  
Power Production ◽  
Department Of Energy ◽  
Power Performance ◽  
Stability Parameters ◽  
Wind Speeds ◽  
Stable Conditions ◽  
Wind Measurements ◽  
Power Curves

Abstract. Using detailed upwind and nacelle-based measurements from a General Electric [GE] 1.5 sle model with a 77 m rotor diameter, we calculated power curves and annual energy production (AEP) and explored their sensitivity to different atmospheric parameters. This work provides guidelines for the use of stability and turbulence filters in segregating power curves to gain a clearer picture of the power performance of a turbine. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower and lidar vertical profiles. We calculated power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) and turbulence kinetic energy (TKE), as well as atmospheric stability parameters such as Bulk Richardson number (RB). AEP was also calculated with and without these atmospheric filters and differences between these calculations are highlighted in this article. The power curves for different TI and TKE regimes revealed that, at the U.S. Department of Energy (DOE) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL), increased TI and TKE undermined power production at wind speeds near rated, but increased power production at lower wind speeds. Similarly, power curves for different RB regimes revealed that periods of stable conditions produced more power at wind speeds near rated and periods of unstable conditions produced more power at lower wind speeds. AEP results suggest that calculations done without filtering for these atmospheric regimes may be overestimating the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to take atmospheric stability and turbulence across the rotor disk into account.

Small Wind Turbine Power Performance Testing with Uncertainty Analysis

2014 ◽  
Vol 875-877 ◽  
pp. 1944-1948
Author(s):  
Wen Lei Bai ◽  
Byun Gik Chang ◽  
Gerald Chen ◽  
Ken Starcher ◽  
David Carr ◽  
...  
Keyword(s):  
Wind Speed ◽  
Uncertainty Analysis ◽  
Wind Turbine ◽  
Performance Testing ◽  
Air Pressure ◽  
Power Performance ◽  
Small Wind Turbine ◽  
Uncertainty Sources ◽  
Measurement Uncertainties

Wind turbine power performance testing consists of power, temperature, air pressure and wind speed measurements collected for this study during which measuring uncertainties are involved. Due to the measurement uncertainties, the results of power performance testing are affected; therefore, it is necessary to consider the measurement uncertainties for evaluating the accuracy of turbine testing. For this purpose of this study, uncertainty analysis for one 5kW wind turbine power performance testing was conducted. The results of uncertainty analysis indicated that the uncertainty negatively affected the validity of conclusions drawn from power performance testing, and the uncertainty sources are various in different wind speed bins.

scholarly journals How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

2020 ◽  
Vol 5 (3) ◽  
pp. 1169-1190
Author(s):  
Patrick Murphy ◽  
Julie K. Lundquist ◽  
Paul Fleming
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Turbine Blades ◽  
Atmospheric Stability ◽  
Power Production ◽  
High Plains ◽  
Power Curve ◽  
Mean Power ◽  
Rotor Disk ◽  
Utility Scale

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

scholarly journals How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

2019 ◽  
Author(s):  
Patrick Murphy ◽  
Julie K. Lundquist ◽  
Paul Fleming
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Turbine Blades ◽  
Atmospheric Stability ◽  
Power Production ◽  
High Plains ◽  
Power Curve ◽  
Mean Power ◽  
Rotor Disk ◽  
Utility Scale

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, or shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer) and rotor-equivalent wind speed (REWS), a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured either by the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW, or up to 5 % of the rated power for a single 1.5-MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model

2020 ◽  
Vol 148 (12) ◽  
pp. 4823-4835
Author(s):  
Cristina L. Archer ◽  
Sicheng Wu ◽  
Yulong Ma ◽  
Pedro A. Jiménez
Keyword(s):  
Wind Speed ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Wind Farms ◽  
Near Surface ◽  
Large Eddy ◽  
The Way

AbstractAs wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called CTKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected CTKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

Wind Flow Conditions in Offshore Wind Farms: Validation and Application of a CFD Wake Model

Green ◽  
2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Annette Westerhellweg ◽  
Beatriz Cañadillas ◽  
Friederike Kinder ◽  
Thomas Neumann
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Flow ◽  
Flow Conditions ◽  
Offshore Wind Farms ◽  
Speed Reduction ◽  
Wake Model ◽  
The Impact

AbstractSince August 2009, the first German offshore wind farm ‘alpha ventus’ is operating close to the wind measurement platform FINO1. Within the research project RAVE-OWEA the wind flow conditions in ‘alpha ventus’ were assessed in detail, simulated with a CFD wake model and compared with the measurements. Wind data measured at FINO1 have been evaluated for wind speed reduction and turbulence increase in the wake. Additionally operational data were evaluated for the farm efficiency. The atmospheric stability has been evaluated by temperature measurements of air and water and the impact of atmospheric stability on the wind conditions in the wake has been assessed. As an application of CFD models the generation of power matrices is introduced. Power matrices can be used for the continual monitoring of the single wind turbines in the wind farm. A power matrix based on CFD simulations has been created for ‘alpha ventus’ and tested against the measured data.

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