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

Martin, Clara M. St.; Lundquist, Julie K.; Clifton, Andrew; Poulos, Gregory S.; Schreck, S.

doi:10.5194/wes-1-221-2016

Cited by 92 publications

(105 citation statements)

References 31 publications

(49 reference statements)

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“…Stability regimes based on L are similar to those defined by Muñoz-Esparza (2012). Regimes or classifications for these stability and turbulence parameters are defined in Table 2 and described in detail in St. Martin et al (2016), along with more detailed descriptions of the data from the lidar, tower, and turbine, as well as filtering methods.…”

Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 90%

“…Regimes of TI, TKE, and α are determined by splitting the distributions of each parameter roughly into thirds. Regimes of R B are similarly determined, as in Aitken et al (2014) and St. Martin et al (2016), and uncertainty in the R B values calculated from propagation of instrument accuracy ensures that the regimes are wide enough.…”

Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 99%

“…Though some previous studies combine metrics to define stability (Vanderwende and Lundquist, 2012), the three atmospheric stability metrics discussed here are treated separately with regard to the NTFs because of slight differences between their definitions of unstable and stable conditions (see Fig. 11 in St. Martin et al, 2016).…”

Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 99%

“…However, we filter for normal turbine operation based on curtailment using generator speed set point for wind speeds greater than 5.5 m s −1 , whereas for wind speeds less than 5.5 m s −1 , we discard data when the turbine is not grid-connected and is faulted (Fig. 6 in St. Martin et al, 2016).…”

Section: Meteorological and Turbine Datamentioning

confidence: 99%

“…However, previous work on power performance and annual energy production (AEP) does acknowledge the role of atmospheric stability, wind shear, and turbulence intensity (TI) in inducing deviations in power from the manufacturer power curve (MPC) (e.g., Sumner and Masson, 2006;Antoniou et al, 2009;Rareshide at el., 2009;Wagenaar and Eecen, 2011;Wharton and Lundquist, 2012;Vanderwende and Lundquist, 2012;St. Martin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Atmospheric turbulence affects wind turbine nacelle transfer functions

Martin¹,

Lundquist

Clifton

et al. 2017

Wind Energ. Sci.

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Abstract. Despite their potential as a valuable source of individual turbine power performance and turbine array energy production optimization information, nacelle-mounted anemometers have often been neglected because complex flows around the blades and nacelle interfere with their measurements. This work quantitatively explores the accuracy of and potential corrections to nacelle anemometer measurements to determine the degree to which they may be useful when corrected for these complex flows, particularly for calculating annual energy production (AEP) in the absence of other meteorological data. Using upwind meteorological tower measurements along with nacelle-based measurements from a General Electric (GE) 1.5sle model, we calculate empirical nacelle transfer functions (NTFs) and explore how they are impacted by different atmospheric and turbulence parameters. This work provides guidelines for the use of NTFs for deriving useful wind measurements from nacelle-mounted anemometers. Corrections to the nacelle anemometer wind speed measurements can be made with NTFs and used to calculate an AEP that comes within 1 % of an AEP calculated with upwind measurements. We also calculate unique NTFs for different atmospheric conditions defined by temperature stratification as well as turbulence intensity, turbulence kinetic energy, and wind shear. During periods of low stability as defined by the Bulk Richardson number (R B ), the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of high stability at some wind speed bins below rated speed, leading to a steeper NTF during periods of low stability. Similarly, during periods of high turbulence, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of low turbulence at most wind bins between cut-in and rated wind speed. Based on these results, we suggest different NTFs be calculated for different regimes of atmospheric stability and turbulence for power performance validation purposes.

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Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 90%

Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 99%

Section: M St Martin Et Al: Atmospheric Turbulence Affects Windmentioning

confidence: 99%

Section: Meteorological and Turbine Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Atmospheric turbulence affects wind turbine nacelle transfer functions

Martin¹,

Lundquist

Clifton

et al. 2017

Wind Energ. Sci.

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Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

2017

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Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.

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Limitations of reanalysis data for wind power applications

Davidson

Millstein

2022

Wind Energy

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Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground).However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7-year-long data set of hourly, plant-level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1-year of records (2019) to evaluate an operational, high-resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

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

Cited by 92 publications

References 31 publications

Atmospheric turbulence affects wind turbine nacelle transfer functions

Atmospheric turbulence affects wind turbine nacelle transfer functions

Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

Limitations of reanalysis data for wind power applications

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