bluuebunny/crossref_metadata_2025_split huggingface dataset:
Control co-design of a large offshore wind farm considering the effect of wind extractability
Authors: Pahus M L , Nishino T , Kirby A , Vogel C R 
Published: June 2024

Abstract
We present a prototype of a novel control co-design (CCD) method for large offshore wind farms. A traditional wind farm internal flow model using a Gaussian wake model and a Boolean yaw optimisation method is coupled, via the two-scale momentum theory, with an analytical model of ‘wind extractability’, to account for the farm-scale blockage effect that depends on the farm size and atmospheric boundary layer height. We also introduce a ‘gridded’ turbine layout optimisation into the CCD, which allows us to quickly find optimal angles of the primary axes of a regular turbine array to maximise the annual energy production (AEP) of a given number of turbines at a given offshore farm site. The developed CCD method is used to demonstrate an important trade-off between the minimum number of turbines and minimum farm area required to achieve a certain AEP at the Seagreen offshore farm site as an example. Our results also suggest that the yaw optimisation could help reduce the number of turbines required, but only when we aim to achieve a high AEP with a small farm area. The developed model could be further coupled with a financial model of wind farms in future studies.
Turbulent flow field reconstruction in wind-farms using power measurements
Authors: Alreweny A , Vandewalle S , Meyers J 
Published: June 2024

Abstract
Knowledge of the turbulent wind field within the atmospheric boundary layer is of great importance in maximizing wind-farm power generation and reducing structural loading. In this context, we propose a 4D-Var algorithm that integrates wind-turbine power measurements, a large-eddy simulation model, and background information to reconstruct the wind-farm’s inflow field. Following the collection of measurements, the 4D-Var problem is efficiently solved using the L-BFGS optimization algorithm. The results show fast convergence and good tracking of both reference power and wind-speed measurements across the turbines.
RANS wake surrogate: Impact of Physics Information in Neural Networks
Authors: Schøler J. P. , Rosi N. , Quick J. , Riva R. , Andersen S. J. , Murcia Leon J. P. , Van Der Laan M. P. , Réthoré P.-E. 
Published: June 2024

Abstract
Artificial Neural Networks (ANNs) are being applied as a faster alternative to Computational Fluid Dynamics (CFD) for wind turbine engineering wake models. Unfortunately, ANNs can fail to generalize if the data is insufficient. Physics-Informed Neural Networks (PINNs) can improve convergence while lowering the required data amounts. This paper investigates the PINN methodology systematically by considering varying amounts of data and physics collocation points. This work considers the rotationally symmetric Reynolds Averaged Navier-Stokes (RANS) formulation. Initially, a baseline fully data-driven ANN is studied to determine a suitable network size. Then, multiple PINN-based wake surrogates are trained with continuity and momentum conservation knowledge, varying amounts of data, and physics collocation points. It was found that including physics information under the best circumstances could improve accuracy by 18% at the cost of increasing the training time by a factor of 116. The findings imply that physics information can improve neural network based wake surrogates.
Development and Verification of an Improved Wake-Added Turbulence Model in FAST.Farm
Authors: Branlard E. , Jonkman J. , Platt A. , Thedin R. , Martínez-Tossas L. A. , Kretschmer M. 
Published: June 2024

Abstract
We introduce a generalized wake-added turbulence (WAT) model in the multiphysics, multiturbine simulation tool FAST.Farm. The WAT model introduces additional small-scale turbulence that represents the breakdown of vortical structures and shear layers in the wake. The article describes the development, implementation, calibration, and verification of the model. The novelties of the model include support for wake asymmetry, buildup of WAT across the wind farm, and secondary effects of wake-induced turbulence (e.g., wake meandering) driven by smaller-scale turbulence structures that arise from wake breakdown. Large-eddy simulations were run to support the calibration of the WAT parameters and verification of the model. Previous studies hypothesized that the lack of WAT modeling was the source of underprediction of fatigue loads, in particular for cases at low turbulence intensities and/or stable atmospheric boundary layers. This study confirms that the newly implemented WAT model enhances the loads predictions in these cases.
Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling
Authors: Liew Jaime , Riva Riccardo , Friis-Møller Mikkel , Göçmen Tuhfe 
Published: June 2024

Abstract
In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.
Bias correction of wind power forecasts with SCADA data and continuous learning
Authors: Jonas S , Winter K , Brodbeck B , Meyer A 
Published: June 2024

Abstract
Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, electricity trading, or maintenance scheduling. In this work, we present, evaluate, and compare four machine learning-based wind power forecasting models. Our models correct and improve 48-hour forecasts extracted from a numerical weather prediction (NWP) model. The models are evaluated on datasets from a wind park comprising 65 wind turbines. The best improvement in forecasting error and mean bias was achieved by a convolutional neural network, reducing the average NRMSE down to 22%, coupled with a significant reduction in mean bias, compared to a NRMSE of 35% from the strongly biased baseline model using uncorrected NWP forecasts. Our findings further indicate that changes to neural network architectures play a minor role in affecting the forecasting performance, and that future research should rather investigate changes in the model pipeline. Moreover, we introduce a continuous learning strategy, which is shown to achieve the highest forecasting performance improvements when new data is made available.
Mimicking the Helix with a porous disc for wind tunnel testing
Authors: De Vos Brian , Harder Bjorn M. , Huisman Thomas A. , Gutknecht Jonas , Van Wingerden Jan-Willem 
Published: June 2024

Abstract
A promising method to reduce wake effects in offshore wind farms is the Helix approach, which increases the mixing of the wake with the surrounding flow by exciting the individual blade pitch. This increases the wind speed in the wake, resulting in a higher power output at a downstream turbine. Wind tunnel testing is crucial to gather further understanding of the governing mechanisms behind the Helix and its efficiency in larger wind farm arrays. However, model turbines are expensive and complex. Porous Discs (PD) have proven to supply a less expensive and less complex alternative for wake-focused wind tunnel studies. In this study we present a novel PD model to mimic the Helix. The fundamental idea is to mimic the non-uniform, unsteady energy extraction over the rotor plane as observed at a Helix-controlled turbine. For this purpose, we derive a non-uniform porosity distribution over the PD, based on Large Eddy Simulations of a three-bladed turbine controlled with the Helix approach, and the actuator disc theory. The resulting non-uniform PD rotates at the excitation frequency described by the Strouhal number to mimic the Helix. We verified the novel experimental setup with smoke visualisation techniques and thrust measurements at a second PD in the wake and observed the typical characteristics of the Helix wake of a model turbine: First, the wake was deformed into a helical shape, and second, the wake velocity increased depending on the excitation Strouhal number.
Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup
Authors: Van Der Hoek Daan , Ferreira Carlos Simão , Van Wingerden Jan-Willem 
Published: June 2024

AbstractInduction control methods offer a potential solution to minimizing wake effects that occur in large wind farms. This paper presents an experimental study on multiple induction control methods for wind farm power maximization. Wind tunnel experiments were conducted on two aligned scaled wind turbines. The upstream turbine was operated with static induction control, periodic dynamic induction control with collective pitch actuation, and dynamic individual pitch control (the helix approach). All wind farm control implementations were compared to a baseline case, which optimized the individual power extraction of both turbines. Tomographic particle image velocimetry was used to measure the wake of the upstream turbine. Based on turbine measurements, grid searches were employed to discover the optimal frequency and amplitude of the pitch actuation in the dynamic induction control cases. While static induction control showed increased wake velocities in the near wake, it did not provide an overall increase in power production of the two-turbine array. Dynamic induction control methods, especially the helix approach in the counterclockwise direction, were seen to significantly increase the total power output compared to the baseline control case. However, this improvement came with a larger amount of pitch actuation and increased fatigue loading of structural components in the fore-aft direction.
Power performance of a model floating wind turbine subjected to cyclic pitch motion: A wind tunnel study
Authors: Duan Guiyue , Gattari Daniele , Porté-Agel Fernando 
Published: June 2024

Abstract
Wind tunnel experiments were performed with a miniature floating wind turbine model to study the effects of cyclic pitch motion on its power performance. The cyclic pitch motion was prescribed by two key parameters: pitch frequency and amplitude. The power performance of the turbine model was investigated at a frequency range of 0.1 − 5.0Hz and an amplitude range of 0 − 30°. Both the mean and time variation of the power production were analyzed, and the effects of the pitch parameters, i.e., the pitch amplitude and frequency, were investigated and discussed. The results show a clear periodicity of power variation and its dependence on pitch frequency and amplitude. For relatively small pitch frequencies (0.5 − 3.0Hz), the mean power and periodic power variation can be predicted based on the uniform and steady flow assumption. Compared to the power output in the baseline case of no pitch dynamics, cyclic pitch motions were found to cause higher power fluctuations, which were contributed by both the pitch motion and flow turbulence. Finally, the temporal variation of the free-rotation speed, used as an indicator of available aerodynamic power, is found to be periodic when the turbine is under cyclic pitch motion. This suggests the possibility of applying dynamic rotor control strategies to maximize power production.
A wind farm consisting of two turbines - The combined influence of turbine spacing and rotational direction
Authors: Englberger Antonia , Dörnbrack Andreas 
Published: June 2024

Abstract
Large-eddy simulations are conducted to investigate the impact of an actual decrease in turbine spacing on the wake structure for a small wind park consisting of two wind turbines in a row with a given rotor diameter D. A systematic variation of the inflow conditions (near-neutral and veering inflow) and the rotational direction of the rotors (clockwise and counterclockwise) provides an initial overview of the atmospheric flow fields in the wakes of both turbines. Whereas a smaller turbine spacing under near-neutral conditions results in a decrease of the streamwise flow component and an increase of turbulence, the situation is much more complicated under veering inflow. If a clockwise (counterclockwise) rotating turbine interacts with a Northern Hemispheric Ekman spiral, the combination of a rotor-induced v-component, which reduces (slightly amplifies) the meridional inflow velocity component, leads to a weakening (slight intensification) of this meridional wind in the vicinity of the downwind rotor.
Impact of inflow characteristics on the instantaneous power production using detrended fluctuation analysis
Authors: Vratsinis Konstantinos , Pauscher Lukas , Daems Pieter-Jan , Van Beeck Jeroen , Helsen Jan 
Published: June 2024

Abstract
This study investigates the influence of inflow characteristics on wind turbine power production using Detrended Fluctuation Analysis (DFA). The research focuses on extracting indicators for the energy of flow fluctuations at small timescales and their scaling properties. This methodology is demonstrated using one year of 1-second SCADA data from an offshore wind farm. These indicators are then compared with the commonly used Turbulence Intensity (TI). The findings demonstrate that DFA coefficients, specifically the DFA y-intercept and the DFA slope, account for a significantly larger portion of the variability in active power. This research enhances SCADA-based performance analysis.
Verification and Validation of Wind Farm Flow Models
Authors: Quick Julian , Mouradi Rem-Sophia , Devesse Koen , Mathieu Antoine , Paul Van Der Laan M. , Murcia Leon Juan Pablo , Schulte Jonas 
Published: June 2024

Abstract
Wind farm flow models allow for the analysis of wind power plant wake effects. There are several mathematical models available to describe this complex flow, and some of them are implemented across multiple code bases. This manuscript presents a framework for verification and validation of these models of different complexities. As part of this work, an API was developed to interface the different flow tools using the WindIO wind plant schema. Then, verification techniques are applied to the embedded tools as a test to make sure there are no mistakes in the codes, and across different flow models. This reveals similarities and differences in stochastic trends given realistic measurement uncertainty. Validation is performed using a database of large eddy simulations to quantify model agreement with high fidelity data and develop a predictor of what this implies for future simulations.
The use of LES to analyse higher-order moments of two-point statistics
Authors: Bock Marcel , Fuchs André , Friedrich Jan , Peinke Joachim 
Published: June 2024

Abstract
The models prescribed by the IEC standard for the generation of wind fields, as well as intermittent modifications of the original models, are used as inflows in LES. The behaviour of the wind fields is investigated with regard to higher-order moments. It is shown that all models reach an intermittent state in the LES. This intermittent state can be determined by the intermittency parameter and by higher orders of the moments. It can be seen that the Kaimal model generates significantly lower intermittency than the other models. The convergence length is significantly reduced with a correct intermittency description in the wind fields.
Observation and modelling of asymmetric loading on large offshore wind turbines in wake conditions
Authors: Bernard V , Andersen S J , Murcia Leon J P , Beaudet L , Verelst D , Iliopoulos A 
Published: June 2024

Abstract
We study the dynamic loading of a wind turbine in the presence of a single wake in the wind speed range of 8 to 12 m/s and a wind direction range of 40° around the wake alignment direction. Measurement data from an offshore wind farm in the North Sea is compared to simulations. Damage equivalent loads and order spectra are compared. Five wake models are used: Frandsen added turbulence model, three parametrizations of DWM, and LES wake simulations. A new parametrization of DWM with an added wake distortion model is introduced improving the wake effect on fatigue loading of tower top torsion and tilt bending moment. All DWM models compare more favourably to measurements than the added turbulence model, but only the wake distortion model allows for a conservative fatigue assessment on all channels. LES has the potential to be even more accurate in capturing the spectral response in wake, but results are preliminary as limited LES data was available.
Benchmarking engineering wake models for farm-to-farm interactions
Authors: Vollmer Lukas , Wurps Hauke , Dörenkämper Martin 
Published: June 2024

Abstract
Inter-farm wakes between offshore wind farms can cause a significant production loss at the downstream wind farm. We compare the power production at two wind farms that are separated by approximately 20 km to engineering model results provided by three different wind farm developers. Model inflow conditions were derived from the nacelle anemometry of the wake-creating wind farm and a mesoscale simulation. The comparison reveals that the model results show in general a good approximation of the wake losses, but underestimate the increase of losses in stable stability conditions. Here, specifically the measured wakes appear to be much wider than the modelled wakes.
A new parametrization of the Global Blockage Effect
Authors: Centurelli G , Vollmer L , Lukassen L J , Peinke J 
Published: June 2024

Abstract
An existing potential flow model for describing the interaction of complex structures and flow currents is adapted for the modeling of a wind farm. The aim is to investigate if such a simple formulation could improve the representation of the global blockage effect (GBE) in engineering models. The model is then coupled with a parametrization of GBE from LES in order to describe the power extraction redistribution GBE is normally associated with. Despite the original model formulation being found to not apply particularly well to the description of a wind farm, the further development introduces dependency on the atmospheric stratification above the wind farm, a feature of GBE observed in LES. Furthermore, it agrees with the trend of power extraction redistribution in a wind farm. However, more research is necessary to promote a better quantitative match between LES and the proposed model.
Nexus between the deficit in moisture transport and drought occurrence in regions with projected drought trends
Authors: Gimeno-Sotelo Luis , Stojanovic Milica , Sorí Rogert , Nieto Raquel , Vicente-Serrano Sergio M , Gimeno Luis 
Published: June 2024

Abstract
In this article, we focus on studying the nexus between moisture transport deficit and drought occurrence in nine key regions across the world where the magnitude of meteorological drought is projected to increase from 1850 to 2100 under a high anthropogenic emission scenario. These regions are central America, southwestern South America, northern Brazil, the Amazon, northeastern Brazil, the western Mediterranean, southern Africa, the eastern Mediterranean, and southwestern Australia. Using the Lagrangian particle dispersion model FLEXPART, we identify the specific moisture sources of the regions (the own region, the nearby continental source and the oceanic sources) and obtain their contributions to the precipitation in the regions for the period 1980–2018. For each region and specific moisture source, the conditional probability of meteorological drought occurrence given an equivalent contribution deficit from the source is estimated using copula models, a statistical methodology that allows us to capture complex relationships between variables. We identify the dominant moisture source in each region, which is the source for which the contribution deficit maximises drought probability. A variety of cases are found: in three regions, the dominant source is the region itself, in one region, it is the nearby terrestrial source, and in five regions, it is an oceanic source. In general, contribution deficits from specific moisture sources are associated with only slightly greater drought probabilities than those from major global moisture sources. We also reveal that the source that contributes the most to precipitation in a given region is not necessarily the dominant source of drought in the region. These results highlight the importance of understanding the role of dominant moisture sources and moisture transport deficits on meteorological drought occurrence at a regional scale.
Evaluating Performance and Grid Impacts of On-Grid Rooftop PV System: Case Study of A Mosque
Authors: Anugrah Pinto , Pratama Rizki Wahyu , Sholekan Muhammad , Fikriyadi Zakariya Arif 
Published: June 2024

Abstract
This study presents a load flow analysis of on-grid rooftop PV installed at Masjid Tablighiyah, Bukittinggi, Indonesia. The analysis was conducted using the Newton-Raphson method and simulated using ETAP software. Two scenarios was explored for the analysis: i) connecting the PV into 20 kV bus, and ii) connecting into 380 V bus. Results obtained showed that grid voltage are meeting the utility standards, with all bus voltages are within ±5% threshold. However, certain nodes have moderately low voltages, suggesting a potential need for pre-emptive measures during peak demand. The power factor performance falls within acceptable ranges (84-91%), indicating reasonable system efficiency. However, there is still needed for continuous monitoring to prevent degradation from harmonic-producing loads. In addition, substation transformers and distribution feeders show ample capacity for future expansions as electricity loads grow. The findings provide a robust baseline for additional grid integration studies, emphasizing the need for further enhancements at weaker nodes to ensure sustained stability and reliability in future demand conditions.