Yaw induced wake deflection-a full-scale validation study

Abstract

Aerodynamic wake interactions between turbines located in wind power plants cause both a loss in power production and an increase in fatigue loading of the wind farm turbines. Yaw induced active wake deflection is one possible wind farm control strategy, which can be applied to mitigate wake effects on nearby downstream located wind turbines. In the present study three flow models of different fidelities are applied to mimic a full-scale study of wake deflection recorded by an advanced synchronised setup of two long-range pulsed scanning lidars. The investigated case studies encompass a base case with (approximately) zero yaw setting supplemented by two non-zero yaw cases of 17.5° and -14.5°, respectively. The model results are compared mutually as well as with the result of the full-scale measurement campaign.

Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation

“…A later study by [5] reconstructed the wake-centre dynamics using three short-ranging scanning LiDARs [6] to investigate the wake turbulence. Recently, a study by [7] performed a wake model validation study, investigating the yaw-induced wake deflection using the reconstructed wakecentre from scanning LiDAR measurements.…”

A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction

“…Consistent with Hill's vortex theory and experimental investigations (Machefaux et al, 2014;Larsen et al, 2020), the wake advection velocity is assumed to be directly related to the maximum speed deficit observed. Following Eq.…”

“…The identified constant for the wake convective velocity, C w , is in line with the values reported in the literature. Larsen et al (2020) found C w = 0.4 by exploiting the wake/ring-vortex analogy along Hill's vortex theory to approximate the wake self-induction. This theory is investigated in more details by Machefaux et al (2014) who report slightly higher values (0.63 and 0.51) based on LES and experimental data.…”

LES verification of HAWC2Farm aeroelastic wind farm simulations with wake steering and load analysis

“…This is achieved by using a digital second-order low-pass Butterworth filter to ensure the tracers react only to the low-frequency components of the wind field in realtime. Wake deflection is modeled using a Hill's vortex method which has been described and verified in [21]. Calculations in the DWM layer are performed using a Python library written in the Rust programming language for computational efficiency and memory safety [22].…”