Optimization Under Uncertainty for Wake Steering Strategies

Quick, Julian; Annoni, Jennifer; King, Ruth; Dykes, Katherine; Fleming, Paul; Ning, Andrew

doi:10.1088/1742-6596/854/1/012036

Cited by 61 publications

(62 citation statements)

References 11 publications

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“…Even then, the variability and the measurement (or information) uncertainty of the local inflow wind directions might have a significant impact to the power gain likelihood presented here. The challenges of implementing yaw misalignment under uncertainty is briefly discussed in Quick et al (2017) and should be further investigated for the analysed setup here in this study.…”

Section: Power-driven Optimizationmentioning

confidence: 99%

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

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Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

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Section: Power-driven Optimizationmentioning

confidence: 99%

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

2020

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“…Static yaw control, in which upstream turbines intentionally misalign their rotors with incoming winds, has been proven as a promising approach to increasing power extraction, in numerical studies [25,26], but also in wind-tunnel tests [27,28] and full-scale field measurements [29,30]. Studies on dynamic yaw control however are much more scarce.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization

2018

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Abstract:In wind farms, wakes originating from upstream turbines cause reduced energy extraction and increased loading variability in downstream rows. The prospect of mitigating these detrimental effects through coordinated controllers at the wind-farm level has fueled a multitude of research efforts in wind-farm control. The main strategies in wind-farm control are to influence the velocity deficits in the wake by deviating from locally optimal axial induction setpoints on the one hand, and steering wakes away from downstream rows through yaw misalignment on the other hand. The current work investigates dynamic induction and yaw control of individual turbines for wind-farm power maximization in large-eddy simulations. To this end, receding-horizon optimal control techniques combined with continuous adjoint gradient evaluations are used. We study a 4 × 4 aligned wind farm, and find that for this farm layout yaw control is more effective than induction control, both for uniform and turbulent inflow conditions. Analysis of optimal yaw controls leads to the definition of two simplified yaw control strategies, in which wake meandering and wake redirection are exploited respectively. Furthermore it is found that dynamic yawing provides significant benefits over static yaw control in turbulent flow environments, whereas this is not the case for uniform inflow. Finally, the potential of combining overinductive axial induction control with yaw control is shown, with power gains that approximate the sum of those achieved by each control strategy separately.

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Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

Hulsman¹,

Andersen²,

Göçmen³

2019

Preprint

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Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

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Optimization Under Uncertainty for Wake Steering Strategies

Cited by 61 publications

References 11 publications

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization

Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

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