Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm

Maarten T Van Beek,Søren J Andersen,Axelle Viré

doi:10.3390/en14051293

Maarten T Van Beek, Søren J Andersen + Show 1 more

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https://doi.org/10.3390/en14051293

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Abstract

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.

Highlights

The current model setup of FLOw Redirection and Induction in Steady-state (FLORIS) contains a total of six model parameters, which all affect the accuracy of the model results, but the influence differ for each parameter
Wake effects impact the aerodynamic performance of large wind farms, which results in decreased power production of wind turbines operating in clusters as opposed to freestanding turbines
This work uses the wind farm model FLORIS to estimate the potential of reducing wake losses by applying yaw-based wake steering

Summary

Introduction

Global wind power capacity has increased significantly from about 17 GW in 2000 to. The installed capacity of offshore wind has increased by a factor of 13 [1], where numerous wind turbines are generally clustered in wind farms. Clustering of turbines can result in significant reductions of the wind farm efficiency, e.g., Barthelmie et al [2] show wake losses of 10–20%, due to the wake interaction of the wind turbines. The wakes behind wind turbines are characterized by a velocity deficit and increased turbulence compared to the free stream flow that the upstream turbine experiences [3]. The velocity deficit results in lower power production for the downstream wind turbines, while the increased turbulence generally leads to higher fatigue loads

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