bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving site-dependent power curve prediction accuracy using regression trees
Authors: Barber S , Nordborg H 
Published: September 2020

Abstract
The accurate prediction of the power production of a wind turbine at a particular site is important in both the planning and operation phases; however, the standard power curve binning method is not specific to the atmospheric conditions at the site. In this work, the application of machine learning for improving the accuracy of site-specific power predictions by taking into account turbulence intensity and shear is investigated, by creating a set of 8,000 ten-minute long aero-servo-elastic simulations of the NREL 5MW reference wind turbine at a random combination of hub-height wind speeds, turbulence intensities and shear factors using cloud computing with the software ASHES. A regression tree with maximum depth of eight and optimised with Adaptive Boosting is trained using a random selection of half of the data. For a set of 50 random test cases, the Root Mean Square Error of the predicted power compared to the simulated power is found to be three times smaller than for the standard power curve method of binning. OEMs could use this method to train a model for the dependency of power production on wind speed, turbulence intensity and shear in the certification phase of a given wind turbine type, which could then be applied by the wind farm planner or operator at any given site for which measurements of the atmospheric conditions are available. Similar success is observed for out-of-plane tip deflections. This result is highly dependent on the quality and distribution of the input data, and therefore this work is being used as a basis for the analysis of real measurement data, as well as for comparisons to other methods such as Artificial Neural Networks.
Large eddy simulation study of extended wind farms with large inter turbine spacing
Authors: Stevens Richard J.A.M. , Meneveau Charles 
Published: September 2020

Abstract
We study the performance of extended wind farms with very large inter turbine spacings, i.e. with an inter turbine spacing of more than 10 turbine diameters, using large-eddy simulations (LES) for the special case of neutral conditions. We compare the results with predictions from different analytical modeling approaches and discuss in which parameter regimes the different model predictions agree with the LES predictions. We find in LES that the normalized power output variance increases further downstream in the wind farm. Besides, we analyze the power output correlation between the subsequent downstream turbines. We find that the correlations decrease with increasing turbine spacing due to the increased mixing induced by the wind turbine wakes. For all cases considered here, the power output correlation with downstream turbines becomes negligible after about 40 turbine diameters.
Actual Total Cost reduction of commercial CFD modelling tools for Wind Resource Assessment in complex terrain
Authors: Barber S , Schubiger A , Koller S , Rumpf A , Knaus H , Nordborg H 
Published: September 2020

Abstract
In this paper, new automated processes for applying the commercial Computational Fluid Dynamics (CFD) tools ANSYS Fluent and ANSYS CFX to wind modelling in complex terrain are developed with the goal of decreasing the Actual Total Costs (ATC) related to planning wind energy projects. Simulations are carried out at the complex terrain site Stotten in southern Germany using ANSYS Fluent and ANSYS CFX, and the ATCs related to the simulations estimated. The simulation set-up and post-processing effort are identified as having the highest effect on the ATC, and therefore the automated processes focus on reducing the effort of these tasks. Simulations of the same test site are carried out with the new automated processes, and compared to the manual processes as well as to an industry-standard tool, WindSim. The new automated tools are found to reduce the ATC of this case by a factor of 12 for Fluent and seven for CFX, to approximately half the value of WindSim. All three simulations show similar deviations compared to measurements and therefore these results are comparable. It should be noted that these results are highly specific to this case, and the absolute cost-saving values cannot be directly transferred to other cases. Nevertheless, it can be concluded that these new processes have significantly reduced the Actual Total Cost and are likely to have a large effect on the choice of the most cost-effective model for a given wind energy project. On-going work involves quantifying the effect of these cost savings on the choice of most appropriate model for this simulation site and further comparing the results to measurements.
The effects of Open Cellular Convection on Wind Farm Operation and Wakes
Authors: Göçmen Tuhfe , Larsén Xiaoli Guo , Imberger Marc 
Published: September 2020

Abstract
Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.
Influence of the direction of rotation on the wake characteristics of closely spaced counter-rotating vertical-axis wind turbines
Authors: Vergaerde Antoine , Troyer Tim De , Muggiasca Sara , Bayati Ilmas , Belloli Marco , Runacres Mark C. 
Published: September 2020

Abstract
Closely spaced counter-rotating vertical-axis wind turbines (VAWTs) exhibit a considerable power improvement compared to the same turbines in isolation. It thus makes sense to study their potential for wind farm production optimisation. With this objective in mind, the wake of an isolated VAWT is compared experimentally to the wake of counter-rotating VAWTs. Because of the unsteady aerodynamics, the wake of an isolated VAWT deflects towards the region behind its upwind moving blade. The direction of rotation thus directly influences the deflection of the wake. For paired configurations, it is possible to use this wake deflection as an advantage. A pair of counter-rotating VAWTs where the upwind moving blades are at the centre of the pair, exhibits a particularly narrow wake. With the benefit of a power improvement and a narrow wake, closely spaced counter-rotating VAWTs exhibit promising characteristics to improve the power density in a wind farm.
WRF-Simulated Springtime Low-Level Jets over Iowa: Implications for Wind Energy
Authors: Aird J A , Barthelmie R J , Shepherd T J , Pryor S C 
Published: September 2020

Abstract
High-resolution simulations with the Weather Research and Forecasting (WRF) model are analyzed to characterize the frequency, intensity, height, and duration of springtime low-level jets (LLJ) and their implications for wind energy resource assessment and planning in Iowa. The time evolution of short-duration LLJ is analyzed to understand wind behavior around LLJ events and to illustrate their importance for high-frequency (few hours) variability in wind speeds and rotor plane turbulent kinetic energy (TKE). During spring, the LLJ core height has a spatiotemporal mean value of 217 m, but the LLJ depth means it frequently intersects typical wind turbine rotor planes. Nearly one-quarter of LLJ exhibit a maximum within the height interval 50-150 m AGL. LLJ profiles are found to have higher mean wind speeds across typical wind turbine rotor planes than non-LLJ profiles and to exhibit lower values of TKE. LLJ occur under stable stratification (i.e. positive Richardson numbers) and are associated with low TKE and the occurrence of high vertical wind shear. The frequency and duration of LLJ exhibit geospatial variability across Iowa with highest values in the northeast of the state. Analyses of daytime and night-time LLJ indicate topographic variability is an important factor in the development of LLJ.
Towards the development of a wake meandering model based on neural networks
Authors: Yang Xiaolei 
Published: September 2020

Abstract
In this work, we develop a neural network model for predicting the instantaneous wake position, which is crucial for a wake meandering model. The data used for training are from the large-eddy simulation of a utility-scale wind turbine. A neural network of four hidden layers with 128 units for each layer is found to be effective when training the model. Effects of different input features on the accuracy of the trained model are systematically tested. It is found that the input features including the downwind and crosswind velocities at two locations upwind of the turbine and the thrust and torque acting on the turbine are enough to guarantee the accuracy of the trained model. Without using the thrust and torque as the input features, the accuracy of the model is significantly worse.
The impact of weather patterns on offshore wind power production
Authors: Cheneka Bedassa R , Watson Simon J , Basu Sukanta 
Published: September 2020

Abstract
Large-scale weather systems have the potential to modulate offshore wind energy production. The Northern European sea areas have recently seen a rapid increase in wind power capacity and thus there is a need to understand how different weather systems affect offshore production from the perspective of energy system integration. In this study, mean sea level pressure data from a new-generation reanalysis (ERA5) are utilised to classify synoptic systems into 30 different weather patterns using a self-organising map (SOM) approach. ERA5 wind speeds are then used in conjunction with a reference 8 MW wind turbine power curve to estimate wind power values at selected offshore sites. We assess how wind power output varies for different weather patterns, specifically, the impact on power production and power ramps.
How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?
Authors: Bodini Nicola , Optis Mike 
Published: September 2020

Abstract
As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.
Large-eddy simulations of the Northeastern US coastal marine boundary layer
Authors: Cheung Lawrence C. , Kaul Colleen M. , Hsieh Alan S. , Blaylock Myra L. , Churchfield Matthew J. 
Published: September 2020

Abstract
In this study, large eddy simulations (LES) of offshore boundary layers near the Nantucket coast are performed using Nalu-Wind. The marine boundary layer conditions are chosen to match the predominantly unstable and neutral conditions measured by the Cape Wind platform. The appropriate domain, resolution, and boundary condition settings required for the LES are established through this work. Differences between stable and unstable cases are found in the wind speed profiles, averaged statistics, and wind spectra, and explained in terms of stratification effects.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-5996C.
Characterization of the wake behind a horizontal-axis wind turbine (HAWT) at very high Reynolds numbers
Authors: Pique Alexander , Miller Mark A. , Hultmark Marcus 
Published: September 2020

Abstract
The wake of a three-bladed horizontal axis wind turbine was studied at aerodynamic conditions similar to what is experienced by commercially available turbines. Field relevant Reynolds numbers and tip speed ratios were obtained through the use of a high-pressure wind tunnel, at relatively low velocities. Measurements of the streamwise velocity were acquired through the use of the novel nano-scale thermal anemometry probe (NSTAP), which yields very high spatial and temporal resolution, enabling unattenuated turbulence measurements. Profiles of the mean velocity and turbulent fluctuations are presented, as they demonstrate important features of the wake development, such as wake recovery and tip vortex evolution. One dimensional energy spectra are also presented to provide details on the dominant flow features present in the wake. Reynolds number invariance is shown for mean velocity deficit and streamwise variance profiles for all downstream positions presented. Downstream evolution of streamwise variance profiles provides insight to the dynamic interactions between the tip and root vortex, such as their eventual coalescence. Spectral analysis show that the near wake flow-structures are dominated by the tip vortex, but that other larger structures are present as well, which may be related to the wake meandering phenomenon.
Three-dimensional wind-turbine wake characterization via tomographic particle-image velocimetry
Authors: Dar Arslan Salim , Porte-Agel Fernando 
Published: September 2020

Abstract
Wind turbines are often sited on different topographical features. In the current work, we performed wind-tunnel measurements of the wake behind a single wind turbine sited on two escarpments differing in the windward side shape using tomographic particle-image velocimetry. The escarpments are classified as forward facing step (FFS) and ramp-shape. The turbine sited on the FFS experiences an inflow with high flow shear and turbulence due to flow separation from the escarpment leading edge compared to the one on the ramp-shaped escarpment. As a consequence, the wake characteristics behind the turbine are strongly affected by the shape of the topography. The velocity deficit in the wake of the turbine is relatively higher in the forward facing step shape, but it also shows a faster recovery. The rotation of the wake is stronger for the turbine on the ramp-shaped escarpment, whereas the meandering of the wake is higher for the FFS case. The spatial coherence is observed to be higher in the near wake of the turbine sited on the FFS escarpment, while it is very similar in the far wake for both cases. Instantaneous vortices identified by the Q-criterion show that the development of tip and hub vortices is affected by the topography as well.
Data assimilation for the prediction of wake trajectories within wind farms
Authors: Lejeune M , Moens M , Coquelet M , Coudou N , Chatelain P 
Published: September 2020

Abstract
In this paper, we formulate a physics-based surrogate wake model in the framework of online wind farm control. A flow sensing module is coupled with a wake model in order to predict the behavior of the wake downstream of a wind turbine based on its loads, wind probe data and operating settings. Information about the incoming flow is recovered using flow sensing techniques and then fed to the wake model, which reconstructs the wake based on this limited set of information. Special focus is laid on limiting the number of input parameters while keeping the computational cost low in order to facilitate the tuning procedure. Once calibrated, comparison with high-fidelity numerical results retrieved from Large Eddy Simulation (LES) of a wind farm confirms the good potential of the approach for online wake prediction within farms. The two approaches are further compared in terms of their wake center and time-averaged speed deficit predictions demonstrating good agreement in the process.
Assessment of weak compressibility in actuator line simulations of wind turbine wakes
Authors: Asmuth Henrik , Janßen Christian F. , Olivares-Espinosa Hugo , Nilsson Karl , Ivanell Stefan 
Published: September 2020

Abstract
The trend of increasing rotor diameters and tip-speeds has brought about concerns of non-negligible compressibility effects in wind turbine aerodynamics. The investigation of such effects on wakes is particularly difficult when using actuator line models (ALM). This is because crucial regions of the flow, i.e. the direct vicinity of the blade, are not simulated but represented by body forces. To separately assess the impact of compressibility on the wake and the ALM itself, we conduct large-eddy simulations (LES) where the forces of the ALM are prescribed and based on the local sampled velocity (standard procedure), respectively. The LES are based on the weakly-compressible Lattice Boltzmann Method (LBM). Further to the comparison of (near-)incompressible to compressible simulations we investigate cases with artificially increased compressibility. This is commonly done in weakly-compressible approaches to reduce the computational demand. The investigation with prescribed forces shows that compressibility effects in the wake flow are negligible. Small differences in the wake velocity (of max. 1%) are found to be related to local compressibility effects in the direct vicinity of the ALM. Most significantly, compressibility is found to affect the sampled velocity and thereby accuracy of the ALM.
Experimental analysis of time delays in wind turbine wake interactions
Authors: Macrì S , Duc T , Leroy A , Girard N , Aubrun S 
Published: September 2020

Abstract
Wind farm control is a current hot topic that deals with the capacity to improve the overall wind power production of a farm by controlling individual wind turbines in order to mitigate their wake effects. The dynamic properties of these different strategies need to be taken into account in order to improve wind farm control models. In this work a post-processing of SCADA data acquired with a high sampling frequency on two neighboring wind turbines is performed through inter-correlation functions in order to assess the time delays between the wind turbine responses depending on wind direction and wind speed.
Comparison of different machine learning algorithms for prediction of wind evolution
Authors: Chen Yiyin , Wen Cheng Po 
Published: September 2020

Abstract
Wind evolution, i.e. the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years. Part of this increasing interest is resulted from the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, study of wind evolution can deepen our understanding of turbulence characteristics and, for example, could make 4D stochastic wind field simulations possible by integrating wind evolution into 3D simulations. This study compares different machine learning algorithms for the prediction of wind evolution, including three variants of Gaussian process regression, regression tree, support vector machine regression, and shallow neural network. The comparison is done using lidar data measured by a nacelle-mounted lidar, and it focuses on the prediction accuracy and the computational time of models, to provide some insights of the trade-off between these factors. In terms of computational time, both the training time of the final model and the time for hyperparameter optimization are taken into account. Moreover, up-scaling of the computational time with data size is also investigated. It is found that Gaussian process regression provides high prediction accuracy but needs long computational time, increasing significantly with data size. For cases where long computational time might be a critical problem, shallow neural network could be a good alternative thanks to its high training efficiency, although its prediction accuracy is lower than Gaussian process regression. The other methods have no advantages in wind evolution prediction compared to these two methods.
Validation of FAST.Farm Against Full-Scale Turbine SCADA Data for a Small Wind Farm
Authors: Shaler K. , Debnath M. , Jonkman J. 
Published: September 2020

Abstract
FAST.Farm is a new midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on some of the principles of the Dynamic Wake Meandering model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous Dynamic Wake Meandering (DWM) implementations. Previous FAST.Farm verification studies show the similarities and differences between FAST.Farm and large-eddy simulations for rigid and flexible turbines. In this validation study, FAST.Farm turbine responses are compared to multiturbine measurements from a subset of a full-scale wind farm. FAST.Farm predictions of turbine generator power, rotor speed, and blade pitch for five-turbine simulations are compared to supervisory control and data acquisition results. Results reveal that FAST.Farm generator power mean and standard deviation results reasonably match measured data for upstream and downstream turbines, as well as the mean rotor speed and blade pitch above rated wind speeds. However, FAST.Farm generally underpredicts the mean rotor speed and overpredicts the mean blade pitch below rated operation. These errors are likely related to inaccuracies in the generic controller simulated. Despite controller differences, FAST.Farm predicts the same overall relative rotor power trends for all waked turbines at all wind speeds.