Extension and validation of an operational dynamic wake model to yawed configurations

Lejeune, Maxime; Moens, Maud; Chatelain, Philippe

doi:10.1088/1742-6596/2265/2/022018

Cited by 4 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is supported by recent studies (e.g., Reinwardt et al, 2020) that clearly demonstrate that the meandering behavior definitely governs fatigue loads; this clearly pleads for the capture of the phenomenon by an operational model if one targets fatigue alleviating model-predictive control of wind farms. Further work toward evaluating the dynamic response of the flow model to a control step input change is also under progress (Lejeune et al, 2022) and shall further demonstrate the applicability of the presented framework to operational wind farm control.…”

Section: Discussionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…For accurate short-term predictions of the wind farm flow field, it is however highly relevant to capture dynamic effects, including changes of wind speed and direction and time variable control actions. Several fast dynamic wake models for control applications have been developed and introduced in recent years [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

Bohrer,

Petrović,

Rott

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.

show abstract

“…While adaptations on the controller side, such as improved filtering [11], preview control [12], and consensus estimation techniques [13], have proven effective in enhancing yaw steering dynamics, this limitation of classical steady-state wake models also advocates for a more thorough consideration of flow physics, particularly in transient scenarios. The importance of accurate plant dynamics modeling has therefore led to the emergence of a series of controloriented dynamic wake models [14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation

Lejeune,

Frère,

Moens

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

We leverage Large Eddy Simulation (LES) of wind farms to study the performances of an open-loop wake steering controller. This global controller is elaborated based on Lookup Table (LUT) generated using FLORIS. Our objectives are to highlight the potential problems that can arise from model errors due to its steady-state formulation and from difficulties to properly define the ambient flow state. We consider two wind farm configurations: a first theoretical layout of six machines, and a second layout representing a commercial wind farm that is currently studied for wake steering strategy, the King Plains wind farm. We represent our wind farm environment using LES within which the turbines are modeled as actuator disks. In addition to the standard local controller, the wind farm utilizes a LUT to compute the optimal yaw offset angles. Before its integration into the controller, this LUT is first populated using the yaw optimizer integrated into FLORIS. The primary input parameters for the LUT are derived from the freestream atmospheric conditions, which are parametrized in terms of TI, hub height wind speed, and wind direction. In this work, we investigate different strategies for estimating the ambient wind direction fed as input to the LUT during the control and their impact on the dynamics of the yaw actuation. While results confirm the potential of this type of controller, they also highlight the importance of a proper sensing to determine the freestream state.

show abstract

Extension and validation of an operational dynamic wake model to yawed configurations

Cited by 4 publications

References 18 publications

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

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

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation

Contact Info

Product

Resources

About