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1. 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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2. One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

   https://doi.org/10.1002/we.2452  

   Santoni, Christian; García‐Cartagena, Edgardo J.; Ciri, Umberto; Zhan, Lu; Valerio Iungo, Giacomo; Leonardi, Stefano (January 2020, Wind Energy)

   Abstract One‐way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in‐house high‐resolution large‐eddy simulation code (UTD‐WF). Each simulation contains five nested WRF domains, with the largest domain spanning the north Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD‐WF LES higher‐resolution domain for a subset of six turbines at a resolution of ∼5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met‐tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD‐WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD‐WF high‐resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models. 

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3. Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

   https://doi.org/10.1109/DCAS53974.2022.9845651  

   Karami, Farzad; Kehtarnavaz, Nasser; Rotea, Mario (June 2022, 2022 IEEE 15th Dallas Circuit And System Conference (DCAS))

   Each year a growing number of wind farms are being added to power grids to generate sustainable energy. The power curve of a wind turbine, which exhibits the relationship between generated power and wind speed, plays a major role in assessing the performance of a wind farm. Neural networks have been used for power curve estimation. However, they do not produce a confidence measure for their output, unless computationally prohibitive Bayesian methods are used. In this paper, a probabilistic neural network with Monte Carlo dropout is considered to quantify the model or epistemic uncertainty of the power curve estimation. This approach offers a minimal increase in computational complexity and thus evaluation time. Furthermore, by adding a probabilistic loss function, the noise or aleatoric uncertainty in the data is estimated. The developed network captures both model and noise uncertainty which are found to be useful tools in assessing performance. Also, the developed network is compared with the existing ones across a public domain dataset showing superior performance in terms of prediction accuracy. The results obtained indicate that the developed network provides the quantification of uncertainty while maintaining accurate power estimation. 

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4. Investigating the impact of wind speed variability on optimal sizing of hybrid wind-hydrogen microgrids for reliable power supply

   https://doi.org/10.1016/j.ijhydene.2025.01.444  

   Eniola, Victor; Cimorelli, Jack; Niezrecki, Christopher; Willis, David; Jin, Xinfang (March 2025, International Journal of Hydrogen Energy)

   Addressing resource intermittency is crucial for designing effective and economical renewable energy systems for many applications. Hydrogen as long-term energy storage medium shows promise for increasing renewables penetration into the grid. Cost-effective hybrid wind-hydrogen microgrids (HWHMs) require system-level sizing of each subcomponent. This study employs low-order HWHM component models in a system-level framework to predict HWHM performance. It introduces a novel approach to investigate the optimal sizing of HWHMs. The study uniquely addresses the impact of wind speed fluctuation amplitudes and frequency variations on system design – an area not previously explored. The model is run for 7 days using several different wind speed profiles and real load demand data from an off-grid Naval facility on an island in California. In our test cases, the findings indicate that fewer wind turbines and more hydrogen tanks are required to successfully meet demand when wind speed fluctuations increase. For example, when the wind speed fluctuation increases from 0.68 to 2.04 m/s, and the wind turbine is expected to maintain an average power equivalent to 90% of the peak load, the turbine capacity drops by 17%, requiring a 304% rise in the number of tanks. However, the frequency of wind speed variation has a negligible effect on the optimal HWHM configuration. Through a rule-based optimization algorithm, this research offers important insights for designing reliable microgrids capable of meeting critical loads despite highly variable wind conditions. 

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5. Wind tunnel experiments and model predictions of the performance of a floating offshore wind turbine undergoing pitch motion

   https://doi.org/10.1063/5.0301237  

   Panthi, Keshav; Iungo, Giacomo Valerio (December 2025, Journal of Renewable and Sustainable Energy)

   Floating offshore wind turbines (FOWTs) experience multiple degree-of-freedom (DOF) motion as a result of the non-linear interactions between the aerodynamic and hydrodynamic forces exerted on the turbine rotor and the floating platform, respectively, which create complex dynamics for FOWT operations and, in turn, variability in rotor angular speed and power capture. In this work, wind tunnel experiments are performed with a down-scaled FOWT model installed on top of a robotic emulator that reproduces 4-DOF motions. Rotor rotational speed, ω, and power capture are measured for pitch motions with different amplitudes and frequencies. These experimental data are first analyzed, then used for the validation of a non-linear dynamic analytical model that predicts the variation in ω and power capture by leveraging the aerodynamic quasi-steady assumption, namely, the FOWT power curve measured under static conditions and null pitch angle is used to predict operations under dynamic conditions. The results show that good accuracy is generally achieved with the analytical model. However, dynamic aerodynamic effects occur during pitch motion that can jeopardize the accuracy of the analytical model, especially with increasing ω, motion amplitude, and in correspondence with pitch angles where the inversion of the motion direction occurs. Furthermore, it is found that these dynamic aerodynamic effects can be accurately predicted through a random forest model by providing as input pitch angle, velocity, and acceleration of the incoming wind. Among the different FOWT motion parameters, the pitch angle is found to be the most influential factor for the magnitude of the dynamic aerodynamic effects. 

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