Extension of the DWM model towards a static model for site-specific load simulations

Reinwardt, Inga; Schilling, Levin; Dalhoff, Peter; Steudel, Dirk; Breuer, M.

doi:10.1088/1742-6596/1618/6/062007

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…We believe that such an operational meandering-capturing model could prove invaluable for the mitigation of fatigue loads in wind farms. 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: Discussionsupporting

confidence: 68%

“…This approximation works relatively well for slow wind farm control but leads to inconsistent results if finer time scales are considered. It further fails to correctly account for meandering-induced fatigue loads (Reinwardt et al, 2020). Medium-fidelity wake models aim to bridge the gap between the two ends of the model spectrum: they provide a dynamic reasonably-faithful description of the flow while remaining computationally tractable.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…The quadratic least-squares assume parabolic DEL with respect to input features, except for the wake centre y 0 . The DWM model predicts a DEL-y 0 dependence with two peaks, which is symmetric or asymmetric based on the load channel [22]. To address this, y 2 0 and y 3 0 were added to the input database and removed y 0 to prevent rank deficiency.…”

Section: Quadratic Least-squaresmentioning

confidence: 99%

Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling

Liew,

Riva,

Göçmen

2023

J. Phys.: Conf. Ser.

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Surrogate models are vital for offshore wind farm optimisation, or digital twin for rapid execution. Reliable surrogates are difficult to make, especially for large wind farms and for fatigue loads. One limitation is the high resolution of turbulence needed for fatigue calculations and the vast size of a wind farm. We present code optimisations for implementing the Mann model for farm-sized flows, which are infeasible otherwise. Additionally, we present a case study of a full load surrogate for the Lillgrund offshore wind farm using large turbulence box generation in aeroelastic wind farm simulations using HAWC2Farm. Various mappings between the wind field in front of a turbine and the corresponding turbine structural fatigue loads are presented. The best-performing surrogate model, using a proper orthogonal decomposition of the input space with an artificial neural network, is able to significantly reduce the error in fatigue load estimates by as much as a factor of 5 compared to conventional methods. The presented recommendations are crucial to generating reliable wind farm load surrogates for wind farm optimisation purposes.

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Extension of the DWM model towards a static model for site-specific load simulations

Cited by 2 publications

References 6 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

Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling

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