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

Abstract

The development of new wake models is currently one of the key approaches envisioned to further improve the levelized cost of energy of wind power. While the wind energy literature abounds with operational wake models capable of predicting in fast-time the behavior of a wind turbine wake based on the measurements available (e.g., SCADA), only few account for dynamic wake effects. The present work capitalizes on the success gathered by the Dynamic Wake Meandering formulation and introduces a new operational dynamic wake modeling framework aimed at capturing the wake dynamic signature at a low computational cost while relying only on information gathered at the wind turbine location. In order to do so, the framework brings together flow sensing and Lagrangian flow modeling into a unified framework. The features of the inflow are first inferred from the turbine loads and operating settings: a Kalman filter coupled to a Blade Element Momentum theory solver is used to determine the rotor-normal flow velocity while a Multi-Layer Perceptron trained on high-fidelity numerical data estimates of the transverse wind velocity component. The information recovered is in turn fed to a Lagrangian flow model as a source condition and is propagated in a physics-informed fashion across the domain. The ensuing framework is presented and then deployed within a numerical wind farm where its performances are assessed. The computational affordability of the proposed model is first confirmed: 7 × 10−4 wall-clock seconds per simulation second are required to simulate a small 12 turbines wind farm. Large Eddy Simulations of wind farm using advanced actuator disks are then used as a baseline and a strong focus is laid on the study of the wake meandering features. Comparison against the Large Eddy Simulation baseline reveals that the proposed model achieves good estimates of the flow state in both low and high Turbulence Intensity configurations. The model distinctly provides additional insight into the wake physics when compared to the traditional steady state approach: the wake recovery is consistently accounted for and the wake meandering signature is captured as far as 12D downstream with a correlation score ranging from 0.50 to 0.85.

Extending the Dynamic Wake Meandering Model in HAWC2Farm: Lillgrund Wind Farm Case Study and Validation

“…The DWM model uses a concept described as passive tracers by Larsen et al (2008), and more recently, observation points by Lejeune et al (2022b), ?gebraad2014control), and Becker et al (2022). Passive tracers are emitted from the turbine rotor, endowed with axial induction and orientation information.…”

“…Steady-state models are unable to resolve dynamic flow interactions between turbines, which is important in the design of closed-loop wind farm control strategies. For these tasks, quasi-dynamic wind farm simulators, such as FLORIDyn (Becker et al (2022)), LongSim (Bossanyi et al (2022)), SimWindFarm (Grunnet et al (2010)), WFSim (Boersma et al (2018)), and OnWaRDS (Lejeune et al (2022b, a)), use low-fidelity rotors and wake profiles in a time-stepping simulation. Such tools are suitable for simulating closed-loop control strategies, but due to the simple rotor model, are unable to resolve mechanical loading effects on the turbine structure without additional modelling.…”

Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm

“…The large-scale meandering of the wake deficit is modelled by warping its path as it advects through the turbulent wind field. The DWM model uses a concept described as passive tracers by Larsen et al (2008) and, more recently, observation points by Lejeune et al (2022b), Gebraad and Van Wingerden (2014), and Becker et al (2022). Passive tracers are emitted from the turbine rotor, endowed with turbine axial induction and orientation information.…”

“…Steady-state models are unable to resolve dynamic flow interactions between turbines, which is important in the design of closed-loop wind farm control strategies. For these tasks, quasi-dynamic wind farm simulators, such as FLORIDyn (Becker et al, 2022), LongSim (Bossanyi et al, 2022), SimWindFarm (Grunnet et al, 2010), WFSim (Boersma et al, 2018), and OnWaRDS (Lejeune et al, 2022b, a), use low-fidelity rotors and wake profiles in a time-marching simulation. Such tools are suitable for simulating closed-loop control strategies but due to the simple rotor model are unable to resolve mechanical loading effects on the turbine structure without additional modelling.…”

Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects

“…This includes for example the curled wake model (Martínez-Tossas et al, 2019), which has been extended with dynamics (Branlard et al, 2023) as the steady-state models are limited for use in time-varying conditions. Another approach is the use of Lagrangian particle methods to use the wakemodels within FLORIS for dynamic wake prediction (Becker et al, 2022;Lejeune et al, 2022).…”