Research on the Influence of Turbulence Intensity to the Power Curve and AEP of Wind Turbine

2013 ◽  
Vol 336-338 ◽  
pp. 885-889
Author(s):  
Bo Jiao ◽  
Yang Xue ◽  
De Yi Fu ◽  
Xiao Jing Ma ◽  
Wei Bian ◽  
...  

It is known that turbulence intensity will affect on power performance and Annual Energy Production (AEP) of wind turbine. But it is unknown how big the influence is. The article quantifies the concrete influence by testing. After calculating the output of wind turbine in different turbulence intensity level, it has shown that the more intensive turbulence will lead more negative impact on the output of wind turbine. The investigation provides some basis for the site sitting of wind farm.

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner

Abstract. The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.


2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV 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 min 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 or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in 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, the 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 preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.


2019 ◽  
Author(s):  
Maarten Paul van der Laan ◽  
Søren Juhl Anderson ◽  
Néstor Ramos García ◽  
Nikolas Angelou ◽  
Georg Raimund Pirrung ◽  
...  

Abstract. Numerical simulations of the Vestas multi-rotor demonstrator (4R-V29) are compared with field measurements of power performance and remote sensing measurements of the wake deficit by a short-range WindScanner lidar system. The simulations predict a gain of 0–2 % in power due to the rotor interaction, for wind speeds below rated. The power curve measurements also show that the rotor interaction increases the power performance below rated by 1.8 ± 0.2 %, which can result in a 1.5 ± 0.2 % increase in the annual energy production. The wake measurements and numerical simulations show four distinct wake deficits in the near wake, which merge into a single wake structure further downstream. Numerical simulations show that the wake recovery distance of a simplified 4R-V29 wind turbine is 1.03–1.44 Deq shorter than for an equivalent single-rotor wind turbine with a rotor diameter Deq. In addition, the numerical simulations show that the added wake turbulence of the simplified 4R-V29 wind turbine is lower in the far wake compared to the equivalent single-rotor wind turbine. The faster wake recovery and lower far-wake turbulence of such a multi-rotor wind turbine has the potential to reduce the wind turbine spacing within a wind farm while providing the same production.


Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 739 ◽  
Author(s):  
Kyoungboo Yang

The wake of a wind turbine is a crucial factor that decreases the output of downstream wind turbines and causes unsteady loading. Various wake models have been developed to understand it, ranging from simple ones to elaborate models that require long calculation times. However, selecting an appropriate wake model is difficult because each model has its advantages and disadvantages as well as distinct characteristics. Furthermore, determining the parameters of a given wake model is crucial because this affects the calculation results. In this study, a method was introduced of using the turbulence intensity, which can be measured onsite, to objectively define parameters that were previously set according to the subjective judgement of a wind farm designer or general recommended values. To reflect the environmental effects around a site, the turbulence intensity in each direction of the wind farm was considered for four types of analytical wake models: the Jensen, Frandsen, Larsen, and Jensen–Gaussian models. The prediction performances of the wake models for the power deficit and energy production of the wind turbines were compared to data collected from a wind farm. The results showed that the Jensen and Jensen–Gaussian models agreed more with the power deficit distribution of the downstream wind turbines than when the same general recommended parameters were applied in all directions. When applied to energy production, the maximum difference among the wake models was approximately 3%. Every wake model clearly showed the relative wake loss tendency of each wind turbine.


2019 ◽  
Vol 4 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Maarten Paul van der Laan ◽  
Søren Juhl Andersen ◽  
Néstor Ramos García ◽  
Nikolas Angelou ◽  
Georg Raimund Pirrung ◽  
...  

Abstract. Numerical simulations of the Vestas multi-rotor demonstrator (4R-V29) are compared with field measurements of power performance and remote sensing measurements of the wake deficit from a short-range WindScanner lidar system. The simulations predict a gain of 0 %–2 % in power due to the rotor interaction at below rated wind speeds. The power curve measurements also show that the rotor interaction increases the power performance below the rated wind speed by 1.8 %, which can result in a 1.5 % increase in the annual energy production. The wake measurements and numerical simulations show four distinct wake deficits in the near wake, which merge into a single-wake structure further downstream. Numerical simulations also show that the wake recovery distance of a simplified 4R-V29 wind turbine is 1.03–1.44 Deq shorter than for an equivalent single-rotor wind turbine with a rotor diameter Deq. In addition, the numerical simulations show that the added wake turbulence of the simplified 4R-V29 wind turbine is lower in the far wake compared with the equivalent single-rotor wind turbine. The faster wake recovery and lower far-wake turbulence of such a multi-rotor wind turbine has the potential to reduce the wind turbine spacing within a wind farm while providing the same production output.


2016 ◽  
Vol 9 (4) ◽  
pp. 1653-1669 ◽  
Author(s):  
Hui Wang ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor ◽  
Gareth. Brown

Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.


2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie

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.


2020 ◽  
pp. 0309524X2092540
Author(s):  
Addisu Dagne Zegeye

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.


2011 ◽  
Vol 347-353 ◽  
pp. 3545-3550 ◽  
Author(s):  
Chang Xu ◽  
Yan Yan ◽  
De You Liu ◽  
Yuan Zheng ◽  
Chen Qi Li

In order to increase wind energy utilization efficiency by the optimization of the wind farm micro sitting, a method which could calculate the wind farm velocity is proposed by consideration of multi turbines wake loss and superposition. Based on the given velocity data of a wind farm, the maximal annual energy production is set as the optimal objective and the ordinates of wind turbines would be the optimal variables, micro sittings of the wind farm turbines are optimized by genetic algorithm. Layout calculation result of the optimal method is quite similar to that of other successful search method, but higher efficiency is reached, and the micro sitting layout is agreement with the regular plum-type layout. Annual energy productions are also calculated under the condition of different wind turbine number. Results show annual energy production increases with the wind turbine number increased, but the increasing trend is lower and lower. The research could provide a reference to wind farm micro-sitting.


2018 ◽  
Vol 64 ◽  
pp. 06010
Author(s):  
Bachhal Amrender Singh ◽  
Vogstad Klaus ◽  
Lal Kolhe Mohan ◽  
Chougule Abhijit ◽  
Beyer Hans George

There is a big wind energy potential in supplying the power in an island and most of the islands are off-grid. Due to the limited area in island(s), there is need to find appropriate layout / location for wind turbines suited to the local wind conditions. In this paper, we have considered the wind resources data of an island in Trøndelag region of the Northern Norway, situated on the coastal line. The wind resources data of this island have been analysed for wake losses and turbulence on wind turbines for determining appropriate locations of wind turbines in this island. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. In this work, semi empirical wake model has been used for wake losses analysis with wind speed and turbine spacings. The Jensen wake model used for the wake loss analysis due to its high degree of accuracy and the Frandsen model for characterizing the turbulent loading. The variations of the losses in the wind energy production of the down-wind turbine relative to the up-wind turbine and, the down-stream turbulence have been analysed for various turbine distances. The special emphasis has been taken for the case of wind turbine spacing, leading to the turbulence conditions for satisfying the IEC 61400-1 conditions to find the wind turbine layout in this island. The energy production of down-wind turbines has been decreased from 2 to 20% due to the lower wind speeds as they are located behind up-wind turbine, resulting in decreasing the overall energy production of the wind farm. Also, the higher wake losses have contributed to the effective turbulence, which has reduced the overall energy production from the wind farm. In this case study, the required distance for wind turbines have been changed to 6 rotor diameters for increasing the energy gain. From the results, it has been estimated that the marginal change in wake losses by moving the down-stream wind turbine by one rotor diameter distance has been in the range of 0.5 to 1% only and it is insignificant. In the full-length paper, the wake effects with wind speed variations and the wind turbine locations will be reported for reducing the wake losses on the down-stream wind turbine. The Frandsen model has been used for analysing turbulence loading on the down-stream wind turbine as per IEC 61400-1 criteria. In larger wind farms, the high turbulence from the up-stream wind turbines increases the fatigues on the turbines of the wind farm. In this work, we have used the effective turbulence criteria at a certain distance between up-stream and down-stream turbines for minimizing the fatigue load level. The sensitivity analysis on wake and turbulence analysis will be reported in the full-length paper. Results from this work will be useful for finding wind farm layouts in an island for utilizing effectively the wind energy resources and electrification using wind power plants.


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