Comparison of Wind Farm Layout Optimization Results Using a Simple Wake Model and Gradient-Based Optimization to Large Eddy Simulations

Thomas, Jared J.; Annoni, Jennifer; Fleming, Paul; Ning, Andrew

doi:10.2514/6.2019-0538

Cited by 24 publications

(26 citation statements)

References 44 publications

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“…Finally, a full wind farm analysis was performed to quantify the potential of wake steering when effects such as yawadded recovery and secondary steering are included. For this analysis, we used a 38-turbine wind farm used as in Thomas et al (2019). The flow field for the baseline case is shown on the left in Fig.…”

Section: Wind Farm Analysismentioning

confidence: 99%

Control-oriented model for secondary effects of wake steering

et al. 2021

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Abstract. This paper presents a model to incorporate the secondary effects of wake steering in large arrays of turbines. Previous models have focused on the aerodynamic interaction of wake steering between two turbines. The model proposed in this paper builds on these models to include yaw-induced wake recovery and secondary steering seen in large arrays of turbines when wake steering is performed. Turbines operating in yaw-misaligned conditions generate counter-rotating vortices that entrain momentum and contribute to the deformation and deflection of the wake at downstream turbines. Rows of turbines can compound the effects of wake steering that benefit turbines far downstream. This model quantifies these effects and demonstrates that wake steering has greater potential to increase the performance of a wind farm due to these counter-rotating vortices especially for large rows of turbines. This is validated using numerous large-eddy simulations for three-turbine, five-turbine, and wind farm scenarios.

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Section: Wind Farm Analysismentioning

confidence: 99%

Control-oriented model for secondary effects of wake steering

et al. 2021

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“…These initial inputs are used to approximate the value of atmospheric characteristics at the location of every turbine within the wind farm and at each individual grid point of the FLORIS flow field. FLORIS performs methods of interpolation and extrapolation using software packages provided by SciPy: an open-source scientific computing library for the Python programming language (Virtanen et al, 2020). The packages used in this method include a piecewise linear interpolant and a nearest-neighbor interpolant, which are combined to create an algorithm that calculates a unique value for each x and y coordinate within the flow field.…”

Section: Initializing the Heterogeneous Flow Fieldmentioning

confidence: 99%

Design and analysis of a wake model for spatially heterogeneous flow

et al. 2021

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Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

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“…We used two different models to fit these two requirements. The model to calculate local turbulence intensity will be discussed in Section 2.4, while the algorithm to calculate the effective inflow turbulence intensity to a turbine is provided in Thomas et al (Thomas et al, 2019). Figure 6 shows the velocity profiles predicted by the wake model compared to the time average velocity data from our SOWFA runs for 4, 7, and 10 diameters downstream of a wind turbine and for two different freestream turbulence intensities.…”

Section: Calculate Steady-state Effective Turbine Wind Speedmentioning

confidence: 99%

A Model to Calculate Fatigue Damage Caused by Partial Waking during Wind Farm Optimization

Stanley¹,

King

Bay

et al. 2020

Preprint

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Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and loads models commonly used in farm research and design, and captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

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Comparison of Wind Farm Layout Optimization Results Using a Simple Wake Model and Gradient-Based Optimization to Large Eddy Simulations

Cited by 24 publications

References 44 publications

Control-oriented model for secondary effects of wake steering

Control-oriented model for secondary effects of wake steering

Design and analysis of a wake model for spatially heterogeneous flow

A Model to Calculate Fatigue Damage Caused by Partial Waking during Wind Farm Optimization

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