scholarly journals Atmospheric Stability Impacts on Power Curves of Tall Wind Turbines - An Analysis of a West Coast North American Wind Farm

2010 ◽  
Author(s):  
S Wharton ◽  
J Lundquist
Keyword(s):  
Wind Turbines ◽  
North American ◽  
West Coast ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Power Curves

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.

Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production

2019 ◽  
pp. 0309524X1988092
Author(s):  
Mohamed Marouan Ichenial ◽  
Abdellah El-Hajjaji ◽  
Abdellatif Khamlichi
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Wind Farm ◽  
Layer Structure ◽  
Atmospheric Stability ◽  
Weather Conditions ◽  
Boundary Layer Structure

The assessment of climatological site conditions, airflow characteristics, and the turbulence affecting wind turbines is an important phase in developing wake engineering models. A method of modeling atmospheric boundary layer structure under atmospheric stability effects is crucial for accurate evaluation of the spatial scale of modern wind turbines, but by themselves, they are incapable to account for the varying large-scale weather conditions. As a result, combining lower atmospheric models with mesoscale models is required. In order to realize a reasonable approximation of initial atmospheric inflow condition used for wake identification behind an NREL 5-MW wind turbine, different vertical wind profile models on equilibrium conditions are tested and evaluated in this article. Wind farm simulator solvers require massive computing resources and forcing mechanisms tendencies inputs from weather forecast models. A three-dimensional Flow Redirection and Induction in Steady-state engineering model was developed for simulating and optimizing the wake losses of different rows of wind turbines under different stability stratifications. The obtained results were compared to high-fidelity simulation data generated by the famous Simulator for Wind Farm Applications. This work showed that a significant improvement related to atmospheric boundary layer structure can be made to develop accurate engineering wake models in order to reduce wake losses.

scholarly journals Modified Power Curves for Prediction of Power Output of Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1805 ◽  
Author(s):  
Mohsen Vahidzadeh ◽  
Corey D. Markfort
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Sonic Anemometer ◽  
Wind Farms ◽  
Operating Conditions ◽  
Horizontal Axis Wind Turbine ◽  
Power Curves

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

Wind turbines abnormality detection through analysis of wind farm power curves

Measurement ◽  
2016 ◽  
Vol 93 ◽  
pp. 178-188 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Abnormality Detection ◽  
Power Curves

scholarly journals Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study

2016 ◽  
Vol 1 (2) ◽  
pp. 129-141 ◽  
Author(s):  
Lukas Vollmer ◽  
Gerald Steinfeld ◽  
Detlev Heinemann ◽  
Martin Kühn
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Stability Control ◽  
Energy Yield ◽  
Multiple Sources ◽  
Ambient Wind ◽  
Precise Knowledge ◽  
Temporal Averaging

Abstract. An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the inflow wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty in the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as a source of uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. Control of the wake position in a highly convective environment is therefore not recommended.

scholarly journals Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: An LES study

2016 ◽  
Author(s):  
Lukas Vollmer ◽  
Gerald Steinfeld ◽  
Detlev Heinemann ◽  
Martin Kühn
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Energy Yield ◽  
Multiple Sources ◽  
Atmospheric Boundary Layers ◽  
Ambient Wind ◽  
Precise Knowledge ◽  
Temporal Averaging

Abstract. An intentional yaw misalignment of wind turbines is currently discussed as one possibility to increase the overall energy yield of wind farms. The idea behind this control is to decrease wake losses of downstream turbines by altering the wake trajectory of the controlled upwind turbines. For an application of such an operational control, precise knowledge about the wind conditions, the magnitude of wake deflection by a yawed turbine and the propagation of the wake is crucial. The dependency of the wake deflection on the ambient wind conditions as well as the uncertainty of its trajectory are not sufficiently covered in current wind farm control models. In this study we analyze multiple sources that contribute to the uncertainty of the estimation of the wake deflection downstream of yawed wind turbines in different ambient wind conditions. We find that the wake shapes and the magnitude of deflection differ in the three evaluated atmospheric boundary layers of neutral, stable and unstable thermal stability. Uncertainty to the wake deflection estimation increases for smaller temporal averaging intervals. We also consider the choice of the method to define the wake center as an uncertainty as it modifies the result. The variance of the wake deflection estimation increases with decreasing atmospheric stability. A control of the wake position in a highly convective environment is therefore not recommended.

FAILURE ANALYSIS OF WIND TURBINES IN ZAFARANA WIND FARM, SUEZ GULF, EGYPT

2012 ◽  
Vol 15 (15) ◽  
pp. 1-20
Author(s):  
H. Saleh ◽  
A. Abou El-Azm ◽  
S. Abd El-Hady
Keyword(s):  
Failure Analysis ◽  
Wind Turbines ◽  
Wind Farm ◽  
Suez Gulf ◽  
Zafarana Wind Farm

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

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