scholarly journals CFD wind turbines wake effects by using UDF

2021 ◽  
Vol 766 (1) ◽  
pp. 012025
Author(s):  
Yi Zhang ◽  
Xuemin Li ◽  
Maulidi Barasa ◽  
Weiming Xu
Keyword(s):  
Wind Turbines ◽  
Wake Effects

Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

2011 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbines ◽  
Wind Farm ◽  
Optimization Problem ◽  
Vertical Axis ◽  
Optimal Placement ◽  
Vertical Axis Wind Turbines ◽  
Wake Effects ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

A comprehensive comparative investigation of the Lifting Line Theory and Blade Element Momentum Theory applied to small wind turbines

2021 ◽  
pp. 1-25
Author(s):  
K.A.R. Ismail ◽  
Willian Okita
Keyword(s):  
Wind Turbines ◽  
Power Coefficient ◽  
Computational Time ◽  
Local Flow ◽  
Small Wind Turbines ◽  
Wake Effects ◽  
Line Theory ◽  
Lifting Line ◽  
Lifting Line Theory ◽  
Blade Element

Abstract Small wind turbines are adequate for electricity generation in isolated areas to promote local expansion of commercial activities and social inclusion. Blade element momentum (BEM) method is usually used for performance prediction, but generally produces overestimated predictions since the wake effects are not precisely accounted for. Lifting line theory (LLT) can represent the blade and wake effects more precisely. In the present investigation the two methods are analyzed and their predictions of the aerodynamic performance of small wind turbines are compared. Conducted simulations showed a computational time of about 149.32 s for the Gottingen GO 398 based rotor simulated by the BEM and 1007.7 s for simulation by the LLT. The analysis of the power coefficient showed a maximum difference between the predictions of the two methods of about 4.4% in the case of Gottingen GO 398 airfoil based rotor and 6.3% for simulations of the Joukowski J 0021 airfoil. In the case of the annual energy production a difference of 2.35% is found between the predictions of the two methods. The effects of the blade geometrical variants such as twist angle and chord distributions increase the numerical deviations between the two methods due to the big number of iterations in the case of LLT. The cases analyzed showed deviations between 3.4% and 4.1%. As a whole, the results showed good performance of both methods; however the lifting line theory provides more precise results and more information on the local flow over the rotor blades.

Skewed Wake Induction Effects on Thrust Distribution on Small Wind Turbine Rotors

2001 ◽  
Vol 123 (4) ◽  
pp. 290-295 ◽  
Author(s):  
Ken Chaney ◽  
Alfred J. Eggers, ◽  
Patrick J. Moriarty ◽  
William E. Holley
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Speeds ◽  
Small Wind Turbine ◽  
Cylindrical Vortex ◽  
Thrust Distribution ◽  
Wake Effects ◽  
Rotor Disk ◽  
Turbine Rotors ◽  
Wake Model

Accurate prediction of both the center of thrust location and the magnitude of the thrust on a rotor disk are critical to satisfactory modeling of the yawing of small wind turbines to large angles to passively control overshoots in power and loads at higher wind speeds. Of the two, the prediction of the center of thrust location upwind of the center of a yawed rotor disk appears to be the most uncertain and potentially in serious error. This error is due to uncertainties in skewed wake effects on the thrust distribution on the disk. Three skewed wake models are examined to better understand the potential sources of error. First is the dynamic inflow model originally developed for helicopters, and second is a modification of this model developed for wind turbines. Third is an earlier cylindrical vortex wake model which pioneered the study of skewed wake effects for helicopters, and which can be generalized for wind turbine applications. It is concluded that this generalized model and the original dynamic inflow model are the most promising for small wind turbine applications, and their predictions of center of thrust and blade root moments are compared for an idealized rotor. The focus is on static equilibrium loads, and note is taken of the potential importance of accounting for expanding wake effects. The basic results of the study are applicable to large as well as small wind turbine rotors.

Investigation of Wake Effects on the Energy Production of Small Wind Turbines

2013 ◽  
Vol 37 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Kenneth W. Corscadden ◽  
William David Lubitz ◽  
Allan Thomson ◽  
John McCabe
Keyword(s):  
Wind Turbines ◽  
Energy Production ◽  
Small Wind Turbines ◽  
Wake Effects

System Reliability for Offshore Wind Turbines: Fatigue Failure

2013 ◽  
Author(s):  
S. Márquez-Domínguez ◽  
J. D. Sørensen
Keyword(s):  
Wind Turbines ◽  
Limit State ◽  
Wind Farms ◽  
Offshore Wind ◽  
Design Standards ◽  
State Equations ◽  
Offshore Wind Farms ◽  
Offshore Wind Turbines ◽  
Cost Of Energy ◽  
Wake Effects

Deeper waters and harsher environments are the main factors that make the electricity generated by offshore wind turbines (OWTs) expensive due to high costs of the substructure, operation & maintenance and installation. The key goal of development is to decrease the cost of energy (CoE). In consequence, a rational treatment of uncertainties is done in order to assess the reliability of critical details in OWTs. Limit state equations are formulated for fatigue critical details which are not influenced by wake effects generated in offshore wind farms. Furthermore, typical bi-linear S-N curves are considered for reliability verification according to international design standards of OWTs. System effects become important for each substructure with many potential fatigue hot spots. Therefore, in this paper a framework for system effects is presented. This information can be e.g. no detection of cracks in inspections or measurements from condition monitoring systems. Finally, an example is established to illustrate the practical application of this framework for jacket type wind turbine substructure considering system effects.

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.

scholarly journals Analytical model for the power-yaw sensitivity of wind turbines operating in full wake

2019 ◽  
Author(s):  
Jaime Liew ◽  
Albert M. Urbán ◽  
Søren Juhl Andersen
Keyword(s):  
Wind Turbines ◽  
Wind Field ◽  
Wind Farm ◽  
Power Production ◽  
Loss Coefficient ◽  
Wake Flow ◽  
Wind Farm Optimization ◽  
Wake Effects ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increase for turbines operating in wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated with the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the waked wind field in order to determine a power-yaw loss coefficient. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation of the incoming wind field, due to the presence of wake(s), plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power-yaw loss coefficient can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30°. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the effects of wake effects and yaw misalignment as well as uncertainty on power output.

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