scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2016 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Probability Distribution ◽  
Wind Turbines ◽  
Energy Production ◽  
Power Curve ◽  
Power Performance ◽  
Mean Wind Speed ◽  
Set Up ◽  
Power Curves

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 turbine, 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 turbines using the met-mast. The NTF (Nacelle Transfer Function) was measured on the reference 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 PC and two NPC) were compared in terms of AEP (Annual Energy Production) for a Rayleigh wind speed probability distribution. For each turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference turbine and within 0,38 % for the second turbine, for a mean wind speed of 8 m/s.

scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Power Curve ◽  
Power Performance ◽  
Mean Wind Speed ◽  
Set Up ◽  
Power Curves

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.

scholarly journals Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1087 ◽  
Author(s):  
Dongheon Shin ◽  
Kyungnam Ko
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Performance Measurement ◽  
Wind Turbine ◽  
Cumulative Distribution ◽  
Light Detection And Ranging ◽  
Power Curve ◽  
Power Performance ◽  
Light Detection ◽  
Power Curves

To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PCNTF, NL) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PCNTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PCNTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.

Calibration Procedures and Uncertainty in Wind Power Anemometers

2007 ◽  
Vol 31 (5) ◽  
pp. 303-316 ◽  
Author(s):  
Rachael V. Coquilla ◽  
John Obermeier ◽  
Bruce R. White
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Wind Power ◽  
Small Error ◽  
Qualitative Description ◽  
Test Facility ◽  
Power Performance ◽  
Site Assessment ◽  
Linear Transfer Function ◽  
Power Curves

Accurate wind measurements are critical in evaluating wind turbine power performance and site assessment. In a turbine power performance evaluation, wind speed readings are matched with corresponding turbine power measurements to produce a power curve for the turbine. For site assessment, the distribution of measured wind speed is used to determine the predicted annual energy production from the wind. Since wind power is proportional to the cube of the wind speed, a small error in the wind measurement could translate to a much greater error in the predicted wind power, which emphasizes the importance of having accurate wind speed readings. To acquire such precision in wind data, it is recommended that individually calibrated anemometers be employed. With these calibrations, it is also recommended that the uncertainty in the calibration be reported so that it may be used not only in the overall uncertainty for turbine power curves and site assessments, but also in improving the performance of an anemometer. A method of presenting calibration uncertainty is defined in the standard IEC 61400-12-1. However, the standard only refers to the measurement uncertainty of the reference wind speed from the particular test facility. It does not include the uncertainty in the anemometer linear transfer function and the errors directly made by the anemometer signal. This paper will discuss: 1) the details of uncertainty reporting as defined by IEC 61400-12-1, 2) a method of extending the uncertainty to include the errors when using the linear transfer function, and 3) a qualitative description of how to determine the uncertainty in a wind speed measurement in the field.

scholarly journals The Scoraig wind trials – In situ power performance measurements of locally manufactured small wind turbines

2020 ◽  
pp. 0309524X2093250
Author(s):  
Jon Leary ◽  
Hugh Piggott ◽  
Robert Howell
Keyword(s):  
Wind Turbines ◽  
Low Cost ◽  
Wind Tunnel Test ◽  
Rural Electrification ◽  
Power Curve ◽  
Performance Measurements ◽  
Power Performance ◽  
Small Wind Turbines ◽  
Power Curves

This article presents new insight into the real-world performance of a range of open source locally manufactured small wind turbines designed to enable sustainable rural electrification. The power performance of seven machines was measured in situ and compared to wind tunnel, test site and other in situ data to produce a set of generic power curves. This article shows that the shape and size of the curve (and therefore the energy that will be generated) varies considerably. However, over-performance was just as likely as under-performance, validating the designer’s predicted energy yields. Nonetheless, optimising the power curve by tuning the small wind turbine increased energy yields by up to 156%. Developing low-cost practical tools that can enable rapid power curve measurements in the field could help reduce uncertainty when planning rural electrification programmes and ensure that small wind turbines are able to deliver vital energy services in off-grid regions of developing countries.

scholarly journals Comparative Study on Impacts of Power Curve Model on Capacity Factor Estimation of Pitch-Regulated Turbines

2012 ◽  
Vol 9 (2) ◽  
pp. 36 ◽  
Author(s):  
MH Albadi ◽  
EF El-Saadany
Keyword(s):  
Wind Speed ◽  
Average Energy ◽  
Capacity Factor ◽  
Power Curve ◽  
Performance Curve ◽  
Power Performance ◽  
Wind Speeds ◽  
Curve Model ◽  
Mean Wind Speed ◽  
Turbine Output

The amount of energy produced by a turbine depends on the characteristics of both wind speed at the site under investigation and the turbine's power performance curve. The capacity factor (CF) of a wind turbine is commonly used to estimate the turbine's average energy production. This paper investigates the effect of the accuracy of the power curve model on CF estimation. The study considers three CF models. The first CF model is based on a power curve model that underestimates the turbine output throughout the ascending segment of the power curve. To compensate for the aforementioned discrepancy, the Weibull parameters, c and k, which are used to describe wind profile, are calculated based on cubic mean wind speed (CMWS). The second CF model is based on the most accurate generic power curve model available in open literature. The third CF model is based on a new model of power performance curve which mimics the behavior of a typical pitch-regulated turbine curve. As the coefficients of this power curve model are based on a general estimation of the turbine output at different wind speeds, they can be further tuned to provide a more accurate fit with turbine data from a certain manufacturer. 

Development and Field Testing of an Inclined Flanged Compact Diffuser for a Micro Wind Turbine

2014 ◽  
Author(s):  
Sandip Kale ◽  
S. N. Sapali
Keyword(s):  
Wind Turbine ◽  
Wind Velocity ◽  
Wind Turbines ◽  
Energy Production ◽  
Cost Effective ◽  
Field Testing ◽  
International Electrotechnical Commission ◽  
Average Wind Speed ◽  
Average Wind ◽  
Power Curves

Micro wind turbines installed in various applications, experience average wind speed for most of the time during operations. Power produced by the wind turbine is proportional to the cubic power of the wind velocity and a small increase in wind velocity results increases power output significantly. The approach wind velocity can be increased by covering traditional wind turbine with a diffuser. Researchers are continuously working to develop a compact, lightweight, cost effective and feasible diffuser for wind turbines. The present work carried out to develop a diffuser with these stated objectives. A compact, lightweight inclined flanged diffuser developed for a micro wind turbine. Bare micro wind turbine and wind turbine covered with developed efficient inclined flanged diffuser tested in the field as per International Electrotechnical Commission (IEC) standards and results presented in the form of power curves. The prediction of annual energy production for both wind turbines determined as per IEC standards.

Determination of rated wind speed for maximum annual energy production of variable speed wind turbines

2017 ◽  
Vol 205 ◽  
pp. 781-789 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Arash Hassanzadeh ◽  
Jamaloddin Jamali ◽  
Ali Mostafaeipour ◽  
Wei-Hsin Chen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Energy Production ◽  
Variable Speed

scholarly journals Lidar arc scan uncertainty reduction through scanning geometry optimization

2016 ◽  
Vol 9 (4) ◽  
pp. 1653-1669 ◽  
Author(s):  
Hui Wang ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor ◽  
Gareth. Brown
Keyword(s):  
Theoretical Model ◽  
Wind Speed ◽  
Wind Energy ◽  
Turbulence Intensity ◽  
Wind Direction ◽  
Energy Production ◽  
Elevation Angle ◽  
Relative Standard Error ◽  
Power Performance ◽  
Relative Standard

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.

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.

scholarly journals Evaluation of the Wind Potential and Optimal Design of a Wind Farm in The Arzew Industrial Zone in Western Algeria

2020 ◽  
Vol 9 (2) ◽  
pp. 177-187
Author(s):  
Salah Marih ◽  
Leila Ghomri ◽  
Benaissa Bekkouche
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Clean Energy ◽  
Appropriate Technology ◽  
Environmental Study ◽  
Net Energy ◽  
Electricity Grid ◽  
Wind Potential ◽  
Mean Wind Speed

This work presents an assessment of the wind potential and a design methodology for a 10 MW wind farm in the Arzew industrial region, located in northwest Algeria, to improve the quality of service of the electricity grid and increase Algeria's participation in the use of renewable energy. The hourly wind data of 10 years (2005-2015) that correspond to the wind potential of the site were analyzed, such as: dominant wind directions, probability distribution, Weibull parameters, mean wind speed and power potential. The site has a mean annual wind speed of 4.46 m/s at 10m height, and enough space to locate the wind turbines. A comparative study was carried out between four wind turbine technologies to improve the site's efficiency and select the appropriate technology: PowerWind 56/ 900 kW, Nordex N50/800 kW, Vestas V50/850 kW, NEG-Micon 44/750 kW. The estimate of the energy produced using WAsP software and the choice of the optimal architectural configuration for wind turbines installation was confirmed. A techno-economic and environmental study was carried out by HOMER software, to choose the model that produces the maximum annual net energy with a competitive cost in the global wind energy market, $ 0.068/kWh, and that provides clean energy with a reduced emission of polluting gases. Finally, this work provides a good indicator for the construction of a wind farm in Arzew. ©2020. CBIORE-IJRED. All rights reserved

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