Do we really need rotor equivalent wind speed?

Sark, W.G.J.H.M. van; Velde, H.C. van der; Coelingh, J.P.; Bierbooms, W.A.A.M.

doi:10.1002/we.2319

Cited by 27 publications

(13 citation statements)

References 34 publications

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“…These results confirm the Sark et al (2019) conclusion that measurement of REWS for power production purposes is necessary for complex terrain sites. Cost-benefit analyses are advised on the cost of implementation of installation and upkeep of inflow sensing equipment (like a Doppler lidar) to provide REWS measurements and the benefit of REWS for power production prediction.…”

Section: Discussionsupporting

confidence: 87%

“…Although former studies used REWS and similar metrics to explore the impact of shear and atmospheric stability on the prediction of power production from megawatt-scale turbines (Elliott and Cadogan, 1990;Rohatgi and Barbezier, 1999;Pedersen, 2004;Sumner and Masson, 2006;Albers et al, 2007;Van den Berg, 2008;Antoniou et al, 2009;Walter et al, 2009;Belu and Koracin, 2012;Wharton and Lundquist, 2012b;Vanderwende and Lundquist, 2012;Sanchez Gomez and Lundquist, 2019;Vahidzadeh and Markfort, 2019), a more recent study (Sark et al 2019) concludes that turbines in regions with flat terrain do not benefit from using REWS rather than a hub-height wind speed. Here, we explore how different regimes of speed and directional veer across the turbine rotor disk affect power production of a megawatt-scale onshore turbine in a wind farm in the high plains of North America.…”

Section: Introductionmentioning

confidence: 99%

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How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Murphy¹,

Lundquist²,

Fleming³

2019

Preprint

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Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, or shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer) and rotor-equivalent wind speed (REWS), a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured either by the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW, or up to 5 % of the rated power for a single 1.5-MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Murphy¹,

Lundquist²,

Fleming³

2019

Preprint

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“…Median REU and wind speeds at hub-height (U) from the four sites indicate relatively small spatial variability across the transect, but wind speeds in the west have a median REU and U for all four WT scenarios approximately 5% higher than those in the east (Figure 5 and Table 1). Although REU are used in the subsequent analysis, in accordance with Reference [53], differences between median REU and median U are smaller than the site differences, even for the WT scenario using the highest hub-height and largest rotor (Table 1). However, as shown by linear fitting slope and intercept values, there is an increasing divergence between U and REU with increasing H and D (Figure 5).…”

Section: Wind Speed Profiles and Reumentioning

confidence: 98%

“…The rotor equivalent wind speed (REU) is used herein, rather than simply wind speed at hub-height, to fully represent wind speed variation across the rotor plane and to provide an estimate of power output for each WT scenario that fully includes the influence of wind shear across the rotor plane [52,53]. It is computed as [52]: (5) where:…”

Section: Rotor Equivalent Wind Speedmentioning

confidence: 99%

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

et al. 2020

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Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month simulations, focused on the eastern Central Plains, show that the power scales broadly as expected with the increase in rotor diameter (D) and wind speeds at hub-height (H). Increasing wind turbine dimensions from current values (approximately H = 100 m, D = 100 m) to those of the new International Energy Agency reference wind turbine (H = 150 m, D = 240 m), the power across the rotor plane increases 7.1 times. The mean domain-wide wind shear exponent (α) decreases from 0.21 (H = 100 m, D = 100 m) to 0.19 for the largest wind turbine scenario considered (H = 168 m, D = 248 m) and the frequency of extreme positive shear (α > 0.2) declines from 48% to 38% of 10-min periods. Thus, deployment of larger wind turbines potentially yields considerable net benefits for both the wind resource and reductions in fatigue loading related to vertical shear.

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“…Furthermore, it is not rare that nacelle anemometers are subjected to failures or bias [34]. These considerations have even led to the formulation of the concept of rotor equivalent wind speed [35,36]: the idea is that, since the rotor speed is controlled on the grounds on the torque and not on the grounds of the nacelle anemometer, the rotor itself can be considered as a probe for estimating the wind speed.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Operation Curves Modelling Techniques

Astolfi

2021

Electronics

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Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

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Do we really need rotor equivalent wind speed?

Cited by 27 publications

References 34 publications

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

Wind Turbine Operation Curves Modelling Techniques

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