Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data

Abstract

Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output. The observations indicate an initial velocity deficit of 50%−60% immediately behind the turbine, which gradually declines to 15%−25% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.

Observing and Simulating Wind-Turbine Wakes During the Evening Transition

“…7). Previous studies (Aitken et al 2014a;Bhaganagar and Debnath 2015;Abkar and Porté-Agel 2015) did not examine the effects of atmospheric stratification on the height of the maximum wind-speed deficit in the wake, and our study has demonstrated the strong influence of evolving stability on the height of the maximum wake. Empirical reduced-order wake models (Jensen 1983;Katíc et al 1986) could therefore be modified to account for evolving stability.…”

“…This sensitivity of the height of the maximum downwind wind-speed deficit to atmospheric stability has yet to be examined in the literature. Aitken et al (2014a) summarized the dis- crepancy on the altitudes of peak wind-speed deficit among previous investigations, although the role of atmospheric stability was not discussed, since stability was not always quantified in the historical observational studies. Using LES, Bhaganagar and Debnath (2015) characterized wind-speed deficit in two stable scenarios with different surface cooling rates.…”

Atmospheric turbulence affects wind turbine nacelle transfer functions

“…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.…”

Identification and quantification of vortical structures in wind turbine wakes for operational wake modeling

“…More specifically, a fitting procedure is carried out by computing the least square error of the axial velocity azimuthal average with respect to the actual flow field. This can be compared to the approach from [11] where the deficit model is instead single-or double-Gaussian-shaped.…”