scholarly journals The effect of wind direction shear on turbine performance in a wind farm in central Iowa

2019 ◽  
Author(s):  
Miguel Sanchez Gomez ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Wind Direction ◽  
Wind Shear ◽  
Wind Farm ◽  
Power Production ◽  
Atmospheric Conditions ◽  
Wind Speeds ◽  
Turbine Performance ◽  
A Site ◽  
Future Work

Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance, however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, affects wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Directional wind shear was found to follow a diurnal cycle and to monotonically decrease with increasing wind speeds. Using different thresholds to distinguish between high- and low-directional wind shear scenarios, we found that larger thresholds evidence statistically-significant effects on turbine power production for lower wind speeds. We further analyzed a threshold of 0.225 deg m−1 and found turbine underperformance in the order of 10 % for wind speed regimes below 8 m s−1. Considering a time period of ramping electricity demand (05:30–09:00 LT) exposed the fact that large direction shear occurs during this time and is undermining turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance, however, future work on segregating speed and direction shear should be pursued to quantify the effects of only one factor on turbine power production.

scholarly journals The effect of wind direction shear on turbine performance in a wind farm in central Iowa

2020 ◽  
Vol 5 (1) ◽  
pp. 125-139 ◽  
Author(s):  
Miguel Sanchez Gomez ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Wind Direction ◽  
Wind Farm ◽  
Atmospheric Conditions ◽  
North Central ◽  
Wind Speeds ◽  
Turbine Performance ◽  
Time Period ◽  
Wind Direction Shear ◽  
A Site

Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance; however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, and speed shear affect wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Wind direction and speed shear were found to follow a diurnal cycle; however, they evolved differently with increasing wind speeds. Using a combination of speed and direction shear values, we found large direction and small speed shear to result in underperformance. We further analyzed the effects of wind veering on turbine performance for specific values of speed shear and found detrimental conditions on the order of 10 % for wind speed regimes predominantly located in the middle of the power curve. Focusing on a time period of ramping electricity demand (06:00–09:00 LT – local time) exposed the fact that large direction shear occurred during this time and undermined turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance.

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals “Wind Theft” from Onshore Wind Turbine Arrays: Sensitivity to Wind Farm Parameterization and Resolution

2020 ◽  
Vol 59 (1) ◽  
pp. 153-174 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Patrick J. H. Volker ◽  
Andrea N. Hahmann ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Horizontal Resolution ◽  
Spatial Extent ◽  
Wind Speeds ◽  
Stable Conditions ◽  
Wake Effects ◽  
Turbine Arrays

AbstractHigh-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Optimum Selection of Wind Turbines Using Normalized Power and Capacity Factor Curves

2021 ◽  
pp. 2813-2823
Author(s):  
Firas A. Hadi ◽  
Zaid F. Makki ◽  
Rafa A. Al-Baldawi
Keyword(s):  
Wind Turbine ◽  
Performance Index ◽  
Shape Parameter ◽  
Parameters Estimation ◽  
Capacity Factor ◽  
Turbine Performance ◽  
Optimum Selection ◽  
Novel Method ◽  
A Site ◽  
Selection Of

The main objective of this paper is present a novel method to choice a certain wind turbine for a specific site by using normalized power and capacity factor curves. The site matching is based on identifying the optimum turbine rotation speed parameters from turbine performance index (TPI) curve, which is obtained from the higher values of normalized power and capacity factor curves. Wind Turbine Performance Index a new ranking parameter, is defined to optimally match turbines to wind site. The relations (plots) of normalized power, capacity factor, and turbine performance index versus normalized rated wind speed are drawn for a known value of Weibull shape parameter of a site, thus a superior method is used for Weibull parameters estimation which is called Equivalent Energy Method (EEM).

scholarly journals ESTUDO DA PRODUÇÃO DE ENERGIA EÓLICA A PARTIR DE MEDIÇÕES ANEMOMÉTRICAS DA ESTAÇÃO DE CRAÍBAS/AL E SIMULAÇÕES COM O MODELO WASP

2016 ◽  
Vol 38 ◽  
pp. 477
Author(s):  
Thays Paes de Oliveira ◽  
Rosiberto Salustiano da Silva Junior ◽  
Roberto Fernando Fonseca Lyra ◽  
Sandro Correia Holanda
Keyword(s):  
Power Generation ◽  
Probability Distribution ◽  
Wind Turbine ◽  
Wind Direction ◽  
Wind Farm ◽  
Scale Parameters ◽  
Wind Potential ◽  
Generation Capacity ◽  
Intensity Density ◽  
Different Levels

Wind energy is seen as one of the promising generation of electricity, as a source of cheap and renewable, is benefit to reduce the environmental impacts of the dam. Along with the hydroelectric networks, the energy produced by the wind will help to increase power generation capacity in the country. That from speed data and direction municipality Wind Craíbas in the corresponding period 2014 - 2015, estimated the wind potential of the region. The tool used in the treatment of the collected data was the Wasp, making simulations of three different levels of measurement, producing a fictitious wind farm with powerful wind turbine. With the model, WASP helps estimate the probability distribution of Weibull and scale parameters A and K. he predominant wind direction is southeast and the best wind power and intensity density levels took place in 70m and 100m high , with about 201 W / m² and 243 W / m² respectively. But when evalua ted the inclusion of fictitious wind farm, the best use happened at 100m tall with production around 73.039 GWh , which can be attributed this improvement to increased efficiency of the wind turbine used in the simulation.

Effect of Slotted Angle on Savonius Wind Turbine Performance

2021 ◽  
Vol 104 ◽  
pp. 83-88
Author(s):  
Rahmat Wahyudi ◽  
Diniar Mungil Kurniawati ◽  
Alfian Djafar
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Electrical Energy ◽  
Speed Ratio ◽  
Angle Variation ◽  
Wind Speeds ◽  
Turbine Performance ◽  
Savonius Wind Turbine ◽  
Low Wind Speeds

The potential of wind energy is very abundant but its utilization is still low. The effort to utilize wind energy is to utilize wind energy into electrical energy using wind turbines. Savonius wind turbines have a very simple shape and construction, are inexpensive, and can be used at low wind speeds. This research aims to determine the effect of the slot angle on the slotted blades configuration on the performance produced by Savonius wind turbines. Slot angle variations used are 5o ,10o , and 15o with slotted blades 30% at wind speeds of 2,23 m/s to 4,7 m/s using wind tunnel. The result showed that a small slot angle variation of 5o produced better wind turbine performance compared to a standard blade at low wind speeds and a low tip speed ratio.

Modeling of Flow Acceleration Around Wind Farms

2007 ◽  
Author(s):  
Patrick Moriarty ◽  
Tetsuya Kogaki
Keyword(s):  
Wind Farm ◽  
Computational Models ◽  
Three Dimensional ◽  
Wind Farms ◽  
Navier Stokes ◽  
Local Wind ◽  
Flow Acceleration ◽  
Wind Speeds ◽  
Optimum Spacing ◽  
Future Work

Recent measurements from operating wind farms demonstrate that the layout of the farm and interactions between turbine wakes strongly affects the overall efficiency of the wind farm. In some wind farms arranged in rectangular layouts, winds coming from the direction of the rectangular corner create a potential acceleration around the wind farm. This acceleration inherently leads to stronger local wind speeds at wind turbines downstream of the corner turbine, thereby increasing the power output of the downstream turbines. In this study, computational models are developed to predict this complex behavior seen in wind farms. The model used to examine these effects is a fully three-dimensional unsteady incompressible Navier-Stokes code, with the turbulence model turned off. Preliminary results show an optimum spacing configuration is possible. However, the results have yet to be verified at higher Reynolds number, which will be the effort of future work. Ultimately, these tools may lead to more optimal wind farm layouts.

scholarly journals A Study of the Impact of Pitch Misalignment on Wind Turbine Performance

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 8 ◽  
Author(s):  
Davide Astolfi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Production ◽  
Test Case ◽  
Before And After ◽  
Input Variables ◽  
The Impact

Pitch angle control is the most common means of adjusting the torque of wind turbines. The verification of its correct function and the optimization of its control are therefore very important for improving the efficiency of wind kinetic energy conversion. On these grounds, this work is devoted to studying the impact of pitch misalignment on wind turbine power production. A test case wind farm sited onshore, featuring five multi-megawatt wind turbines, was studied. On one wind turbine on the farm, a maximum pitch imbalance between the blades of 4.5 ° was detected; therefore, there was an intervention for recalibration. Operational data were available for assessing production improvement after the intervention. Due to the non-stationary conditions to which wind turbines are subjected, this is generally a non-trivial problem. In this work, a general method was formulated for studying this kind of problem: it is based on the study, before and after the upgrade, of the residuals between the measured power output and a reliable model of the power output itself. A careful formulation of the model is therefore crucial: in this work, an automatic feature selection algorithm based on stepwise multivariate regression was adopted, and it allows identification of the most meaningful input variables for a multivariate linear model whose target is the power of the wind turbine whose pitch has been recalibrated. This method can be useful, in general, for the study of wind turbine power upgrades, which have been recently spreading in the wind energy industry, and for the monitoring of wind turbine performances. For the test case of interest, the power of the recalibrated wind turbine is modeled as a linear function of the active and reactive power of the nearby wind turbines, and it is estimated that, after the intervention, the pitch recalibration provided a 5.5% improvement in the power production below rated power. Wind turbine practitioners, in general, should pay considerable attention to the pitch imbalance, because it increases loads and affects the residue lifetime; in particular, the results of this study indicate that severe pitch misalignment can heavily impact power production.

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