A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting

Choukulkar, Aditya; Pichugina, Yelena L.; Clack, C.; Calhoun, Ronald; Banta, Robert M.; Brewer, Alan; Hardesty, Michael

doi:10.1002/we.1929

Cited by 60 publications

(57 citation statements)

References 16 publications

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“…The opposite trend was however found when the turbine operates in conditions closer to the rated wind speed. This concept is also supported by the equivalent power mathematics of Choukulkar et al that suggest that an increase in TI can only increase the available power if operating below rated conditions. The conventional representation of performance is with the coefficient of power defined by

C_{P} = \frac{P}{\frac{1}{2} ρ A U_{\infty}^{3}},

where P is the turbine output power and A is the projected frontal area of the turbine.…”

Section: Introductionmentioning

confidence: 99%

Performance and wake of a Savonius vertical‐axis wind turbine under different incoming conditions

et al. 2019

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In this study, the performance, drag, and horizontal midplane wake characteristics of a vertical‐axis Savonius wind turbine are investigated experimentally. The turbine is drag driven and has a helical configuration, with the top rotated 180° relative to the bottom. Both performance and wake measurements were conducted in four different inflow conditions, using Reynolds numbers of ReD≈1.6×105 and ReD≈2.7×105 and turbulence intensities of 0.6% and 5.7%. The efficiency of the turbine was found to be highly dependent on the Reynolds number of the incoming flow. In the high Reynolds number flow case, the efficiency was shown to be considerably higher, compared with the lower Reynolds number case. Increasing the incoming turbulence intensity was found to mitigate the Reynolds number effects. The drag of the turbine was shown to be independent of the turbine's rotational speed over the range tested, and it was slightly lower when the inflow turbulence was increased. The wake was captured for the described inflow conditions in both optimal and suboptimal operating conditions by varying the rotational speed of the turbine. The wake was found to be asymmetrical and deflected to the side where the blade moves opposite to the wind. The largest region of high turbulent kinetic energy was on the side where the blade is moving in the same direction as the wind. Based on the findings from the wake measurements, some recommendations on where to place supplementary turbines are made.

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C_{P} = \frac{P}{\frac{1}{2} ρ A U_{\infty}^{3}},

where P is the turbine output power and A is the projected frontal area of the turbine.…”

Section: Introductionmentioning

confidence: 99%

Performance and wake of a Savonius vertical‐axis wind turbine under different incoming conditions

et al. 2019

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“…Wharton and Lundquist (2012b) also found that vertical TI and turbulence kinetic energy (TKE) affect power performance and Rareshide et al (2009) found that veer affects power performance. Atmospheric stability induces deviations of power from the manufacturer power curve (MPC) (Motta et al, 2005;van den Berg, 2008;Vanderwende and Lundquist, 2012;Wharton and Lundquist, 2012b), and atmospheric variations across the rotor disk can influence power performance results (Sumner and Masson, 2006;Wagner et al, 2009;Choukulkar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Wind turbine power production and annual energy production depend on atmospheric stability and turbulence

Martin¹,

Lundquist

Clifton

et al. 2016

Wind Energ. Sci.

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Abstract. Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number (R B ). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different R B regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate 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 incorporate effects of atmospheric stability and turbulence across the rotor disk.

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“…Coherent Doppler lidars (CDL) are used in applications ranging from basic atmospheric boundary-layer research to model data assimilation (Riishøjgaard et al, 2004;Chai et al, 2004;Newsom and Banta, 2004a, b;Newsom et al, 2005;Weissmann and Cardinali, 2007;Pu et al, 2010) and to wind resource assessment (Pena et al, 2009;Lang and McKeogh, 2011;Koch et al, 2012;Pichugina et al, 2012;Hsuan et al, 2014;Newsom et al, 2015;Newman et al, 2016;Choukulkar et al, 2016). Within the wind energy industry, these instruments are viewed as cost-effective alternatives to instrumented towers for wind resource assessment provided the measurement uncertainties are within acceptable limits.…”

Section: Introductionmentioning

confidence: 99%

Validating precision estimates in horizontal wind measurements from a Doppler lidar

et al. 2017

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Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocityazimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

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A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting

Cited by 60 publications

References 16 publications

Performance and wake of a Savonius vertical‐axis wind turbine under different incoming conditions

Performance and wake of a Savonius vertical‐axis wind turbine under different incoming conditions

Wind turbine power production and annual energy production depend on atmospheric stability and turbulence

Validating precision estimates in horizontal wind measurements from a Doppler lidar

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