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1. Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver

   https://doi.org/10.2514/6.2018-2018  

   Santhanagopalan, Vignesh ; Letizia, Stefano ; Zhan, Lu ; Al-Hamidi, Louay Yahia ; Iungo, Giacomo Valerio ( January 2018 , 2018 Wind Energy Symposium)

   null (Ed.)

   This work focuses on the optimization of performance and profitability of a wind farm carried out by means of an economic model and Reynolds-Averaged Navier-Stokes (RANS) simulations of wind turbine wakes. Axisymmetric RANS simulations of isolated wind turbine wakes are leveraged with a quadratic super-positioning model to estimate wake interactions within wind farms. The resulting velocity field is used with an actuator disk model to predict power production from each turbine in the wind farm. Design optimization is performed by considering a site in North Texas, whose wind resource statistics are obtained from a meteorological tower. The RANS solver provides capabilities to simulate different incoming wind turbulence intensities and, hence, the wind farm optimization is performed by taking the daily cycle of the atmospheric stability into account. The objective functional of the optimization problem is the levelized cost of energy (LCoE) encompassing capital cost, operation and maintenance costs, land cost and annual power production. At the first level of the optimization problem, the wind farm gross capacity is determined by considering three potential turbine types with different rated power. Subsequently, the optimal wind farm layout is estimated by varying the uniform spacing between consecutive turbine rows. It is found that increasing turbine rated power, the wind farm profitability is enhanced. Substituting a wind farm of 24 turbines of 2.3-MW rated power with 18, 3-MW turbines could reduce the LCoE of about 1.56 $/MWh, while maintaining a similar gross capacity factor. The optimization of the spacing between turbine rows was found to be sensitive to the land cost. For a land cost of 0.05 $/m2, the layout could be designed with a spacing between 6 to 15 rotor diameters without any significant effect on the LCoE, while an increased land cost of 0.1 $/m2 leads to an optimal spacing of about 6 rotor diameters. 

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2. Quantification of wind turbine energy loss due to leading‐edge erosion through infrared‐camera imaging, numerical simulations, and assessment against SCADA and meteorological data

   https://doi.org/10.1002/we.2798  

   Panthi, Keshav ; Iungo, Giacomo Valerio ( December 2022 , Wind Energy)

   Quantification of the performance degradation on the annual energy production (AEP) of a wind farm due to leading‐edge (LE) erosion of wind turbine blades is important to design cost‐effective maintenance plans and timely blade retrofit. In this work, the effects of LE erosion on horizontal axis wind turbines are quantified using infrared (IR) thermographic imaging of turbine blades, as well as meteorological and SCADA data. The average AEP loss of turbines with LE erosion is estimated from SCADA and meteorological data to be between 3% and 8% of the expected power capture. The impact of LE erosion on the average power capture of the turbines is found to be higher at lower hub‐height wind speeds (peak around 50% of the turbine rated wind speed) and at lower turbulence intensity of the incoming wind associated with stable atmospheric conditions. The effect of LE erosion is investigated with IR thermography to identify the laminar to turbulent transition (LTT) position over the airfoils of the turbine blades. Reduction in the laminar flow region of about 85% and 87% on average in the suction and pressure sides, respectively, is observed for the airfoils of the investigated turbines with LE erosion. Using the observed LTT locations over the airfoils and the geometry of the blade, an average AEP loss of about 3.7% is calculated with blade element momentum simulations, which is found to be comparable with the magnitude of AEP loss estimated through the SCADA data.

    

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3. Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR

   https://doi.org/10.2514/6.2021-1181  

   Maulik, Romit ; Rao, Vishwas ; Renganathan, S. Ashwin ; Letizia, Stefano ; Iungo, Giacomo Valerio ( January 2021 , AIAA Scitech 2021 Forum)

   null (Ed.)

   Wind turbine wakes are responsible for power losses and added fatigue loads of wind turbines. Providing capabilities to predict accurately wind-turbine wakes for different atmospheric conditions and turbine settings with low computational requirements is crucial for the optimization of wind-farm layout, and for improving wind-turbine controls aiming to increase annual energy production (AEP) and reduce the levelized cost of energy (LCOE) for wind power plants. In this work, wake measurements collected with a scanning Doppler wind Li- DAR for broad ranges of the atmospheric static stability regime and incoming wind speed are processed through K-means clustering. For computational feasibility, the cluster analysis is performed on a low-dimensional embedding of the collected data, which is obtained through proper orthogonal decomposition (POD). After data compression, we perform K-means of the POD modes to identify cluster centers and corresponding members from the LiDAR data. The different cluster centers allow us to visualize wake variability over ranges of atmospheric, wind, and turbine parameters. The results show that accurate mapping of the wake variability can be achieved with K-means clustering, which represents an initial step to develop data-driven wake models for accurate and low-computational-cost simulations of wind farms. 

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4. Near-wake structure of full-scale vertical-axis wind turbines

   https://doi.org/10.1017/jfm.2020.578  

   Wei, Nathaniel J. ; Brownstein, Ian D. ; Cardona, Jennifer L. ; Howland, Michael F. ; Dabiri, John O. ( May 2021 , Journal of Fluid Mechanics)

   null (Ed.)

   To design and optimize arrays of vertical-axis wind turbines (VAWTs) for maximal power density and minimal wake losses, a careful consideration of the inherently three-dimensional structure of the wakes of these turbines in real operating conditions is needed. Accordingly, a new volumetric particle-tracking velocimetry method was developed to measure three-dimensional flow fields around full-scale VAWTs in field conditions. Experiments were conducted at the Field Laboratory for Optimized Wind Energy (FLOWE) in Lancaster, CA, using six cameras and artificial snow as tracer particles. Velocity and vorticity measurements were obtained for a 2 kW turbine with five straight blades and a 1 kW turbine with three helical blades, each at two distinct tip-speed ratios and at Reynolds numbers based on the rotor diameter $D$ between $1.26 \times 10^{6}$ and $1.81 \times 10^{6}$ . A tilted wake was observed to be induced by the helical-bladed turbine. By considering the dynamics of vortex lines shed from the rotating blades, the tilted wake was connected to the geometry of the helical blades. Furthermore, the effects of the tilted wake on a streamwise horseshoe vortex induced by the rotation of the turbine were quantified. Lastly, the implications of this dynamics for the recovery of the wake were examined. This study thus establishes a fluid-mechanical connection between the geometric features of a VAWT and the salient three-dimensional flow characteristics of its near-wake region, which can potentially inform both the design of turbines and the arrangement of turbines into highly efficient arrays. 

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5. Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF

   https://doi.org/10.1175/JAMC-D-19-0135.1  

   Shepherd, T. J. ; Barthelmie, R. J. ; Pryor, S. C. ( March 2020 , Journal of Applied Meteorology and Climatology)

   The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different wind farm parameterizations: 1) the “Fitch” parameterization that is included in WRF releases and 2) the recently developed Explicit Wake Parameterization (EWP) scheme. We compare the schemes using a single yearlong simulation for a domain centered on the highest density of current turbine deployments in the contiguous United States (Iowa). Pairwise analyses are applied to diagnose the downstream wake effects and impact of wind turbine arrays on near-surface climate conditions. On average, use of the EWP scheme results in small-magnitude wake effects within wind farm arrays and faster recovery of full WT array wakes. This in turn leads to smaller impacts on near-surface climate variables and reduced array–array interactions, which at a systemwide scale lead to summertime capacity factors (i.e., the electrical power produced relative to nameplate installed capacity) that are 2%–3% higher than those from the more commonly applied Fitch parameterization. It is currently not possible to make recommendations with regard to which wind farm parameterization exhibits higher fidelity or to draw inferences with regard to whether the relative performance may vary with prevailing climate conditions and/or wind turbine deployment configuration. However, the sensitivities documented herein to the wind farm parameterization are of sufficient magnitude to potentially influence wind turbine array siting decisions. Thus, our research findings imply high value in undertaking combined long-term high-fidelity observational studies in support of model validation and verification.

    

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