bluuebunny/crossref_metadata_2025 huggingface dataset:
Control of photon transport in an optical structure of four microresonators using spectral engineering
Authors: Tsukanov A V
Published: June 2022

A theoretical model of a photonic switch based on a T-shaped optical structure with combined switching control is considered. The transmission coefficient of photons through the structure varies between current and vacuum modes by several physical mechanisms. The main one is the resonant interaction between microresonators with adjustable photon tunneling energy. An alternative way is also pump compensation by switching on an additional laser field. The influence of the model parameters on the switching efficiency is studied. It is shown that this scheme can be implemented on the basis of already existing technologies.
Memristor’s characteristics: From non-ideal to ideal
Authors: Sun Fan , Su Jing , Li Jie , Duan Shukai , Hu Xiaofang
Published: June 2022

Memristor has been widely studied in the field of neuromorphic computing and is considered to be a strong candidate to break the von Neumann bottleneck. However, the non-ideal characteristics of memristor seriously limit its practical application. There are two sides to everything, and memristors are no exception. The non-ideal characteristics of memristors may become ideal in some applications. Genetic algorithm (GA) is a method to search for the optimal solution by simulating the process of biological evolution. It is widely used in the fields of machine learning, combinatorial optimization, and signal processing. In this paper, we simulate the biological evolutionary behavior in GA by using the non-ideal characteristics of memristors, based on which we design peripheral circuits and path planning algorithms based on memristor networks. The experimental results show that the non-ideal characteristics of memristor can well simulate the biological evolution behavior in GA.
Slight Co-doping tuned magnetic and electric properties on cubic BaFeO3 single crystal
Authors: Qin Shijun , Zhou Bowen , Liu Zhehong , Ye Xubin , Zhang Xueqiang , Pan Zhao , Long Youwen
Published: June 2022

The single crystal of cubic perovskite BaFeO3 shows multiple magnetic transitions and external stimulus sensitive magnetism. In this paper, a 5%-Co-doped BaFeO3 (i.e. BaFe0.95Co0.05O3) single crystal was grown by combining floating zone methods with high-pressure techniques. Such a slight Co doping has little effect on crystal structure, but significantly changes the magnetism from the parent antiferromagnetic ground state to a ferromagnetic one with the Curie temperature T
C ≈ 120 K. Compared with the parent BaFeO3 at the induced ferromagnetic state, the saturated magnetic moment of the doped BaFe0.95Co0.05O3 increases by about 10% and reaches 3.64 μ
B/f.u. Resistivity and specific heat measurements show that the ferromagnetic ordering favors metallic-like electrical transport behavior for BaFe0.95Co0.05O3. The present work indicates that Co-doping is an effective method to tune the magnetic and electric properties for the cubic perovskite phase of BaFeO3.
Strain-modulated anisotropic Andreev reflection in a graphene-based superconducting junction
Authors: Zhou Xingfei , Xu Ziming , Cao Deliang , Qi Fenghua
Published: June 2022

We investigate the Andreev reflection across a uniaxial strained graphene-based superconducting junction. Compared with pristine graphene-based superconducting junction, three opposite properties are found. Firstly, in the regime of the interband conversion of electron–hole, the Andreev retro-reflection happens. Secondly, in the regime of the intraband conversion of electron–hole, the specular Andreev reflection happens. Thirdly, the perfect Andreev reflection, electron–hole conversion with unit efficiency, happens at a nonzero incident angle of electron. These three exotic properties arise from the strain-induced anisotropic band structure of graphene, which breaks up the original relation between the direction of velocity of particle and the direction of the corresponding wavevector. Our finding gives an insight into the understanding of Andreev reflection and provides an alternative method to modulate the Andreev reflection.
Blockage effects in a single row of wind turbines
Authors: Bleeg J , Montavon C
Published: June 2022

Reynolds-averaged Navier-Stokes simulations are used to assess the magnitude and sign of turbine interaction loss in single-row wind farms with flow perpendicular to the row. Consistent with previous studies, the simulations show that lateral interactions between turbines can lead to a production gain when assuming idealised neutral conditions. These gains are amplified by using over-constraining boundary conditions. When more realistic conditions are simulated, the gains turn to losses, suggesting that losses are more likely to occur in actual single-row wind farms. The study results also indicate that commonly used assumptions in engineering models for blockage effects risk the underprediction of turbine interaction loss.
Wind farm response to mesoscale-driven coastal low level jets: a multiscale large eddy simulation study
Authors: Chatterjee Tanmoy , Li Jing , Yellapantula Shashank , Jayaraman Balaji , Quon Eliot
Published: June 2022

Realistic atmospheric turbulence–wind farm interactions during coastal low-level jet (LLJ) events are captured using high-fidelity, mesoscale-driven large eddy simulations (LES) to understand wind turbine loads, wakes and overall performance. The simulation has been carried out using the ExaWind aeroelastic solver, AMR-Wind. The simulations have been compared against a baseline unstable case matching the wind speed, wind direction and TI at hub-height location. Results indicate that the LLJ has negative impacts on the turbine hub and tower loads, and opens up potential avenues for design load mitigation strategies.
Sensitivity of Wake Modelling Setups
Authors: Kemme L , Strnad S , Vollmer L , Schmidt J , Lukassen L J
Published: June 2022

Engineering wake modelling with single turbine wake models and wake superposition models (SupModel) requires the choice of two crucial settings: the type of wake superposition and the wake expansion rate parameter. Based on offshore wind farm measurements, the appropriate tuning of such wake modelling setups with respect to wind farm efficiency and their sensitivity to this tuning is investigated. For this purpose, universal and inflow-individual wake expansion rate parameters are optimised for different SupModels via minimising the deviation between simulated and measured power and to compare the results to simulations using a literature value. The work reveals a logarithmic, SupModel-independent sensitivity of the farm efficiency to the wake expansion rate, sufficient to allow equally accurate modelling performance for all setups if a matching parameter is chosen. The choice of the SupModel only matters in case of an unadapted wake expansion rate parameter. Beyond that, the results indicate a systematic flaw of wake models in reproducing farm power measurements for both weak and full wake situations with the same model tuning. Inflow-individual parameters obtained by this work mitigate this with a systematic pattern over wind speed and wind direction and can significantly decrease the modelling uncertainty over varying inflow conditions.
Wind farm optimization with multiple hub heights using gradient-based methods
Authors: Wolf Ciavarra Andreas , Valotta Rodrigues Rafael , Dykes Katherine , Réthoré Pierre-Elouan
Published: June 2022

Optimization of the Levelized Cost of Energy (LCoE) in wind farms helps ensure profitability and competitiveness of the project. Recent work has explored driving down LCoE by allowing multiple wind turbines in a single wind farm - with different hub heights, rotor diameters, and rated powers. In this work, we performed optimization of the Lillgrund wind farm with continuously varying hub-heights to mitigate wake interference, improve annual energy production (AEP) and reduce LCoE. The optimization converged to a configuration where the turbines were vertically staggered, resulting in an improvement in both AEP and internal rate of return (IRR) - a financial metric related to LCoE. Reducing the number of turbines to a discrete set of 2 or 3 retained nearly all the benefits of staggering but is more aligned with limitations related to manufacturing and logistics.
Extension and validation of an operational dynamic wake model to yawed configurations
Authors: Lejeune M , Moens M , Chatelain P
Published: June 2022

This work extends the capabilities of an operational dynamic wake model to yawed cases. The proposed framework brings together flow sensing and Lagrangian flow modeling into a unified framework: both the freestream flow field and the wake one are discretized as series of information-carrying particles. A source condition for these particles is thus obtained from the wind turbine measurements through flow sensing techniques. The estimated flow field state across the wind farm is finally reconstructed by propagating these particles downstream at their own characteristic velocity. The resulting framework is first presented and its extension to yawed turbine is then discussed. Comparison against high-fidelity Large Eddy Simulations of yawed wind turbines confirms the good potential of the approach: different yaw angles are considered and the performance of the model are evaluated. This study indicates that the proposed framework captures the relevant large scale wake features caused by the combined effect of yawing and wake meandering at a low computational cost thereby making it suitable for online model-based control.
Further calibration and validation of FLORIS with wind tunnel data
Authors: Campagnolo Filippo , Imširović Lejla , Braunbehrens Robert , Bottasso Carlo L.
Published: June 2022

This paper compares the accuracy of four different FLORIS model implementations characterized by increasing levels of accounted physics. The predictions of each model were tested against experimental observations obtained from wind tunnel tests of a cluster of three scaled wind turbines. The experiments were conducted for a wide range of environmental conditions, and mainly with yawed turbines. The results showed a significant improvement in the accuracy of the base model by adding more details to the physical effects of the simulation, such as secondary steering and a heterogeneous flow field. It is also interesting to note that the FLORIS predictions become particularly accurate when considering inflow-dependent power losses in yaw-misaligned conditions.
The WRF model and the diurnal cycle of orographically-influenced flow: A case study
Authors: Sile T , Hahmann A N
Published: June 2022

The Weather Research and Forecasting (WRF) model is widely used in modelling for wind energy purposes. Previous research indicated that under some situations, the WRF model fails to properly represent the diurnal cycle in wind speed. Here we present a case study with a North Sea jet where the WRF-simulated wind diurnal cycle is compared with observations at the Østerild tall mast in Northern Jutland, Denmark. The diurnal cycle in wind speed has different shapes at different heights and the WRF model can replicate the diurnal cycle at higher levels (∼ 250 m a.g.l.) but fails to replicate it near surface. We investigate the effect of using different Planetary Boundary Layer (PBL) and Surface Layer (SL) schemes and compare stability data between model and observations. The results suggest a weak link between the model’s ability to replicate stability, probably linked to incorrect surface roughness length specification, and the model’s ability to replicate diurnal cycle at lower levels.
Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering
Authors: Onnen D , Larsen G C , Lio W H , Liew J Y , Kühn M , Petrović V
Published: June 2022

In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.
Evaluation of a lattice Boltzmann-based wind-turbine actuator line model against a Navier-Stokes approach
Authors: Schottenhamml Helen , Anciaux-Sedrakian Ani , Blondel Frédéric , Borras-Nadal Adria , Joulin Pierre-Antoine , Rüde Ulrich
Published: June 2022

Due to the cost and difficulty to precisely measure aerodynamic quantities in onshore and offshore wind farms, researchers often rely on high-fidelity large eddy simulation, based on Navier-Stokes flow solvers. However, the cost of such simulation is very high and does not allow, in practice, extensive parametric studies for large wind farms. Among others, the lattice Boltzmann method is a good candidate for much faster, ExaScale wind farm flow simulations. The present paper aims to assess the validity of a lattice Boltzmann-based actuator line model and highlights its strengths and potential weaknesses. With this intent, comparisons against a Navier-Stokes approach commonly used in the wind energy community are performed. We assess the potential of the lattice Boltzmann method to reduce the computational cost of such simulations by analyzing the performance of the different solvers and their scalability. The lattice Boltzmann-based waLBerla solver reduces the computational costs significantly compared to SOWFA while maintaining the same accuracy as the Navier-Stokes-based method. Furthermore, we show that a multi-GPU implementation leads to an even more drastic reduction of the computational time, achieving faster-than-real-time simulations. This performance will allow extensive parametric studies over large wind farms in future studies.
Faster wind farm AEP calculations with CFD using a generalized wind turbine model
Authors: van der Laan M. P. , Andersen S. J. , Réthoré P.-E. , Baungaard M. , Sørensen J. N. , Troldborg N.
Published: June 2022

Wind farm Annual Energy Production (AEP) calculations are required to design energy efficient wind farm layouts. We investigate methods that can reduce the computational effort of AEP calculations using Reynolds-averaged Navier-Stokes simulations of an idealized atmospheric wind farm setup. In addition, we introduce a generalized wind turbine model that compares well with wind turbine aerodynamic data covering a large range of wind turbine sizes. We apply the general wind turbine model to reduce the computational effort of the AEP calculations by decreasing the number of independent wind speed flow cases. Furthermore, we apply Reynolds-number similarity to compute the wind speed flow cases faster and we show how wind farm layout mirror- and rotational-symmetry can reduce the number of independent wind direction flow cases.
Application of an open-loop dynamic wake model with high-frequency SCADA data
Authors: Braunbehrens R. , Schreiber J. , Bottasso C. L.
Published: June 2022

Future wind energy control applications require surrogate flow models that can capture processes on short time scales. Dynamic extensions of wake models might be a useful low cost solution. So far, studies have tested the proposals mostly in LES or LIDAR campaigns. At the same time, readily obtainable turbine SCADA measurements are increasingly being used directly for model input or validation. Newly available high resolution recordings allow for the study of processes on shorter time scales. The present study examines the modelling capabilities of a dynamic wake model by utilizing high frequency SCADA data. Therefore, the data is first analyzed for cross-correlation and time delays. Secondly, the dynamic wake model, initialized with SCADA measurements, is used to predict a two turbine wake interaction. An offline tuning algorithm is used to adapt the model for the shorter time scales.
Wind speed reconstruction from three synchronized short-range WindScanner lidars in a large wind turbine inflow field campaign and the associated uncertainties
Authors: Giyanani Ashim , Sjöholm Mikael , Rolighed Thorsen Gunhild , Schuhmacher Jakob , Gottschall Julia
Published: June 2022

Modern large wind turbines require high-resolution wind measurements as input to aerodynamic and aeroelastic simulations for modelling and validation purposes. Within the HighRe project, we aim at studying the aerodynamic effects at high Reynolds numbers by measuring four-dimensional wind fields (vx
,vy
,vz
,t) using three short-range WindScanners (SRWS). The systems were set up at the test site in Bremerhaven (Testfeld BHV) to perform an inflow wind field measurement campaign. In order to get a better understanding of the measurements, we describe the propagation of uncertainties in SRWS parameters to the measured wind field and propose an uncertainty model for a measurement setup with three SRWS lidars. In this study, we first evaluate the wind reconstruction and derive an uncertainty model for the wind components vx
, vy
, and vz
, which are mainly dependent on the input parameters, e.g., focus range, elevation angle, and azimuth angle. The effective intersection diameter at the intersection of three beams was found to be in the order of 2-5m. As expected, a high uncertainty was observed at lower heights in the vz
-component due to low elevation angles. This uncertainty evaluation forms the basis for comparing scanning patterns with regard to their accuracy in providing four-dimensional measurements.
Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment
Authors: Letizia Stefano , Moss Coleman , Puccioni Matteo , Jacquet Clément , Apgar Dale , Valerio Iungo Giacomo
Published: June 2022

A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.
Comparison of near wind farm wake measurements from scanning lidar with engineering models
Authors: Anantharaman A , Centurelli G , Schneemann J , Bot E , Kühn M
Published: June 2022

The presence of offshore wind farms causes downstream regions of reduced wind velocity, i.e. wind farm (cluster) wakes, which can affect the power of wind farms downstream. Engineering models are now being used to simulate the effects of these wakes, and an important requirement for model validation is a comparison with full-field measurements. Our objective in this paper is to parametrize and validate two engineering wake models with long-range lidar measurements. We use a long-range scanning Doppler lidar to scan the near wake region of a 400 MW offshore wind farm and compare the wind velocities in the wake to the outputs of two engineering models: FarmFlow and flappy. We adapt FarmFlow to solve the flow in highly unstable atmospheres by modifying the boundary conditions, which enables the comparison of velocity profiles behind the farm. The models perform qualitatively well in predicting the wake deficit and shape close to the farm and at lower heights. They predict higher wake losses within the farm when compared to production power data in a strongly unstable atmospheric case. However, the current analysis is limited due to the lack of inflow measurements for model initialization, compounded by limited data availability. We discuss the possibilities and limitations of long-range scanning lidar data for cluster wake model validation and the need for inflow measurements for model initialization. We conclude that with detailed inflow measurements, scanning long-range lidars could serve as a good tool for the validation of wind farm wake models.
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Authors: Bentsen Lars Ødegaard , Dilp Warakagoda Narada , Stenbro Roy , Engelstad Paal
Published: June 2022

With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.
Wakes in and between very large offshore arrays
Authors: Pryor S.C. , Barthelmie R.J. , Shepherd T.J. , Hahmann A.N. , Garcia Santiago O.M.
Published: June 2022

Projected power output and wake extents are presented from new simulations with the Weather Research and Forecasting (WRF) model v4.2.2 for the large offshore wind energy lease areas along the U.S. east coast. These simulations assume nearly 2000 IEA 15 MW reference turbines are deployed with a spacing equal to the mean of smaller European offshore wind farms (7.7 rotor diameters). Results show marked differences across two wind farm parameterizations. Generally, the modified Fitch parameterization (wherein TKE generation by the rotor has been decreased) generates lower power production estimates, and more spatially extensive and deeper wind farm wakes than are manifest in output from the Explicit Wake Parameterization (EWP). For example, under conditions of moderate freestream wind speeds (∼ 4-10 ms−1 at hub-height) and turbulent kinetic energy (TKE ∼ 0.2 to 1 m2s−2), cumulative power output (summed over all 15 lease areas) is substantially greater (∼ 25% higher) in output from EWP than Fitch. These differences have real implications for power production and thus both expected revenues and grid integration. The cumulative power production and mean normalized wake extent also exhibit sensitivity to the order in which the overlapping inner domains are computed and the number of inner domains. This effect is smaller than differences from two wind farm parameterizations. Analyses focusing on the seven adjoining lease areas south of Massachusetts indicate differences in the two schemes are magnified over the largest offshore wind clusters (with expected installed capacity of > 10 GW and spatial extent of 3675 km2).
Large eddy simulation of an onshore wind farm under different operating regimes including topographic effects
Authors: Draper M , López B , Maiuri D , Decaro C , Campagnolo F
Published: June 2022

High-fidelity simulations of actual wind farms with the Actuator Line Model, in a Large Eddy Simulation framework, have been performed in the past years, for both onshore and offshore sites. It has become the state of the art to simulate the wind flow through wind turbines and wind farms and it could be a powerful tool to assess the operation of actual wind farms and potential improvements. The objective of the present paper is two-fold: develop a high-fidelity numerical model of an onshore wind farm to be used to assess the influence of different environmental and aerodynamic conditions in its power performance and analyze, as case study, the effect of the topography in the wind flow and power production.
Effects of self-induced gravity waves on finite wind-farm operations using a large-eddy simulation framework
Authors: Lanzilao Luca , Meyers Johan
Published: June 2022

In the present study, we use large-eddy simulation (LES) to investigate how a capping inversion in combination with a stable free atmosphere influences the flow development and energy extraction in a large finite wind farm with a staggered and aligned layout. In the conventionally neutral boundary layer (CNBL), we find that gravity waves induce an unfavourable pressure gradient in the induction region of the farm which contributes to the upstream blockage, decreasing the available energy for first-row turbines. However, a favourable pressure gradient establishes through the farm in such conditions, which redistributes the energy and enhances wake recovery. These results are compared with a farm operating in the neutral boundary layer (NBL). Here, we find that only hydrodynamic effects induced by the turbines drag play a role, which cause minor pressure perturbations across the domain. For the selected atmospheric conditions, the power losses generated by the upstream blockage are balanced by the enhanced wake recovery promoted by the favourable pressure gradient throughout the farm. Consequently, the staggered farm efficiency in the CNBL is 8.8% higher than in the NBL. We note that this difference in efficiency is slightly enhanced by the 0.5? difference in wind direction at the location of the first-row turbines between the CNBL and NBL cases, which is caused by the presence of flow blockage. Since both simulations are forced with an equal turbulent velocity profile, the variation in performance is solely caused by the different vertical temperature profiles in the main domain. Finally, the staggered layout leads to a slightly stronger flow blockage than the aligned one when both farms operate in the CNBL.
Wind Farm Inflow Wind Simulation based on Mesoscale and Microscale Coupling
Authors: Zhang Zhaoyu , Bakhoday-Paskyabi Mostafa , Schito Paolo , Reuder Joachim , Zasso Alberto
Published: June 2022

Inflow wind simulation is a critical issue that dominates the wind farm design regarding the actual wind environment. Different from traditional real measurement based wind simulation, this paper proposed a mesoscale and microscale coupling strategy, which applies the forecasting information from WRF model to the LES model based SOWFA. Firstly, an offline coupling strategy is implemented with the modular software interface between WRF and SOWFA. The wind speed, potential temperature and pressure data are converted from Geographic Coordinate to Cartesian coordinate that is a readable format to SOWFA. Then, the simulation domain is selected in daytime for neutral ABL condition at a 1km×1km region where the wind information from WRF is interpolated and averaged at center point with 100m height. Time-series ABL conditions are extracted from center point and force the SOWFA internal solver to simulate the same environment with predictive data. The mesoscale lacked information, turbulence, is generated by periodically running precursor with the surface roughness and boundary conditions. Finally, the comparison between WRF exacted data and SOWFA output verifies the coupling strategy. The result shows that the mesoscale and microscale coupling has high fidelity and accuracy simulation at stable ABL conditions and slow-changing wind environments. This work provides a low cost and reliable data source which allows the inflow wind simulation to have the predictive ability for actual wind.
Tuning of an engineering wind farm model using measurements from Large Eddy Simulations
Authors: Sood Ishaan , Meyers Johan
Published: June 2022

In this work, we develop a model tuning framework to calibrate analytical wind farm models by using virtual measurements from Large Eddy Simulations (LES). A Gaussian wake deficit model coupled with a recursive wake merging methodology is utilized to simulate different inflow conditions through a virtual wind farm, which has been previously used in a suite of LES to providing a reference wind farm database. Using velocity and power measurements from the reference database, a ridge regression based calibration is conducted for the wake model parameters. Without calibration, large errors up to 20% initially persisted between the analytical model and LES for the same inflow conditions, which were subsequently observed to reduce below 6% after model tuning. The tuned models were then used to analyze farm performance for cases with optimal wake steering set points, and it was observed that the predicted gains were in better accordance with LES results.
Hybrid use of an observer-based minute-scale power forecast and persistence
Authors: Theuer F , Schneemann J , van Dooren M F , von Bremen L , Kühn M
Published: June 2022

Lidar-based minute-scale wind power forecasts are valuable to support grid stability and electricity trading. Current methodologies are able to outperform the benchmark persistence only during transient situations and unstable stratification. So far, methods that extend lidar-based forecasts to observer-based forecasts by embedding turbine operational data are not able to outperform persistence during stable atmospheric conditions either. In this paper we therefore analyse the complementary use of an observer-based power forecast and persistence. To do so, we implemented two hybrid approaches: The first is based on a binary decision algorithm, while the second is weighting the two methods by minimizing a cost function. We evaluated 5-minute-ahead deterministic power forecasts of the hybrid and individual models at an offshore wind farm and found the weighting approach to be most skillful. Further, the data set was extended to represent the atmospheric conditions on site for an entire typical year. The weighting approach outperformed the binary decision algorithm for both the 5-minute sample forecasts and the one year-long data set. We discuss the advantages and disadvantages of the two hybrid models and conclude that the weighting approach is the better choice. Further, it can be concluded that also when evaluating the forecasts over a longer period, in this case one year, the additional use of observer-based forecasts is beneficial compared to solely relying on persistence.
Turbine-level clustering for improved short-term wind power forecasting
Authors: González Sopeña J M , Maury C , Pakrashi V , Ghosh B
Published: June 2022

At the present time, new types of data are collected at a turbine level, and can be used to enhance the skill of short-term wind power forecasts. In particular, high resolution measurements such as wind power and wind speed are gathered using SCADA systems. These data can be used to build turbine-tailored forecasting models, but at a higher computational cost to predict the production of the overall wind farm compared to a single farm-level model. Thus, we explore the potential of the DBSCAN clustering algorithm to group wind turbines and build forecasting models at a cluster-level to find a middle ground between forecasting accuracy and computational cost. The proposed approach is evaluated using SCADA data collected in two Irish wind farms.
Handling Individual Pitch Control within an Actuator Disk framework: verification against the Actuator Line method and application to wake interaction problems
Authors: Moens M. , Coquelet M. , Trigaux F. , Chatelain P.
Published: June 2022

The present study aims at assessing the Actuator Disk (AD) method supplemented with an Individual Pitch Control (IPC) strategy, at a resolution appropriate for the Large Eddy Simulation of large wind farms. The IPC scheme is based on a state-of-the art individual pitch control, generalized to be applied to an AD approach. This procedure also requires an accurate recovery of the flapwise bending moment on each blade, which is not trivial for a disk-type model. In order to compute flapwise moments on each blade, blade trajectories are reproduced through the disk and the AD aerodynamic forces are interpolated onto these virtual blades at each time step. We verify the AD model with IPC in simulations of an isolated wind turbine, for different wind speeds and turbulence intensities, and in a configuration with two rotors. We compare the AD statistics with those obtained using an Actuator Line (AL) method. The comparison done in terms of equivalent moment shows that the AD and AL simulations produce very similar results.
Effectiveness of dynamic induction control strategies on the wake of a wind turbine
Authors: Montenegro Montero M , Arcari V , Cacciola S , Croce A
Published: June 2022

Dynamic Induction Control (DIC) has been recently proposed as means for enhancing wake recovery and, in turn, for increasing the overall produced power. A faster wake recovery is triggered by a Periodic Collective Motion (PCM), following a single sine function (S-PCM), or by a combination of Gaussian functions (G-PCM). Both techniques are associated with power gains in simple two- or three-turbine farms, but entail an increase in machine loading. A technique named the Helix approach generates a dynamic induction through a thrust that varies in direction but not in magnitude, reducing the tower loading. This work aims to analyse the impact of bluff bodies, such as nacelle and tower on the performances of PCD techniques, and to quantify the DIC impact on the loads. A 5 MW reference wind turbine is used for the model, implemented in OpenFAST and SOWFA to perform large-eddy simulations (LES). The results obtained at a distance of 3D downstream, show less evidence of the bluff bodies using the PCM than the baseline, as an effect of the increased in-wake mixing. In a two-turbine wind farm with a separation of 3D between turbines, this effect leads to an increment in the overall power output of the farm, despite the presence of the tower and nacelle. The blockage itself does not seem to hamper the effectiveness of DIC. In both cases, DIC is responsible for an increment of about 7% in the overall power output.
Active power control of wind farms: an instantaneous approach on waked conditions
Authors: Gonzalez Silva Jean , Matthijs Doekemeijer Bart , Ferrari Riccardo , Wingerden Jan-Willem van
Published: June 2022

This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator. The proposed design enhances power tracking stability and allows for simple understanding, where each turbine is considered as a pure time-delay system. The paper investigates the control performance with different nominal power distributions in a fully waked condition and limited power availability. Results demonstrate the improvement in power production obtained by closing the control loop, compared to greedy operation. Additionally, power tracking capabilities are enhanced with a nominal power distribution favored by axial-induction, as well as the occurrence of turbine saturation and the distribution of loads.
Wind turbine power performance characterization through aeroelastic simulations and virtual nacelle lidar measurements
Authors: Sebastiani Alessandro , Peña Alfredo , Troldborg Niels
Published: June 2022

The power performance of a wind turbine depends on several characteristics of the inflow, such as wind speed, turbulence and wind shear. Additionally, wind turbine control strategies affect the power performance of the wind turbines; under certain conditions one might want to, e.g., intentionally misalign the turbine with respect to the main wind direction. Here, we evaluate whether the accuracy in power performance evaluation can be improved by using multi-dimensional power curves in the form of multivariate polynomial regressions, which define the power output as function of wind speed, turbulence and yaw misalignment. The analysis is conducted on a dataset of virtual power performance measurements, which is generated through aeroelastic simulations combined with a simulator of nacelle lidar measurements. Results show that the multi-dimensional power curves can provide higher accuracy than those derived using the IEC standard for power curve measurements; the error in power prediction is nearly halved compared to that using the IEC standard power curve method. Additionally, we show that nacelle lidar measurements increase the accuracy of the multi-dimensional power curves when compared to using mast-based anemometer measurements.
Structure of Offshore Low-Level Jet Turbulence and Implications to Mesoscale-to-Microscale Coupling
Authors: Jayaraman Balaji , Quon Eliot , Li Jing , Chatterjee Tanmoy
Published: June 2022

This paper explores realistic nonstationary atmospheric boundary layer (ABL) turbulence arising from nonstationarity at the mesoscale, particularly within offshore low-level jets with implications to offshore wind farms, using high-fidelity multiscale large-eddy simulations (LES). To this end, we analyzed the single-point turbulence statistical structure of a North-Atlantic offshore LLJ event simulated using high-resolution LES (AMR-Wind). The nonstationary LLJ is simulated using a mesoscale-to-microscale coupled (MMC) simulation procedure involving data assimilation of mesoscale velocity and temperature data from the Weather Research and Forecasting (WRF) model. Unlike the assimilation of mesoscale velocity data into the LES, the direct assimilation of temperature profiles had a strong impact on turbulence stratification, thereby causing erroneous predictions of turbulence both above and within the jet layer. Various approaches to mitigate this effect have resulted in multiple (four) variants of this MMC strategy. Outcomes from this work clearly show that the turbulence within the low-level jet is a strong function of the MMC approach as the turbulence structure within the low-level jet is dependent on the flux of residual turbulence from outside the jet, which in turn depends on the temperature forcing history. Additionally, the turbulence predicted by all these different methods (as well as the observation data) show similar deviations from equilibrium as evidenced by comparisons with idealized atmospheric turbulence structure obtained using the same numerical method. In general, we observe that the predicted LLJ turbulence tends to differ from canonical ABL turbulence with comparable shear. Particularly, the combination of shear and turbulence observed in such nonstationary low-level turbulence cannot be matched using equilibrium settings and therefore, represents a critical use-case for both testing and leveraging meso–micro coupling strategies.
Evaluation of a Wind Field Parametrisation Methodology for Lidar-Measured Wind Turbine Inflow
Authors: van Dooren M F , Kühn M
Published: June 2022

There are different methods for the reconstruction of wind fields from lidar measurements, either expressing the wind condition as a set of global parameters, or by 2D or 3D wind vector reconstruction. In this work, we propose a four-parameter wind field parametrisation model as a further development of the three-parameter model introduced by Kapp and Kühn (2014, 2017), which includes a rotor-equivalent wind speed, the horizontal inclination angle of the wind with respect to the measurement plane, a wind shear exponent and a turbulence parameter. In our approach, the turbulence parameter is based on the spatial variations with respect to the rotor-equivalent wind speed over the measurement plane. The model is evaluated by applying it on a spatially and temporally highly resolved SpinnerLidar data set, measured from the nacelle of a wind turbine situated in flat terrain, with a met mast data set for comparison. First results show a good correlation for the rotor-equivalent wind speed and the horizontal inclination, however, a poor fit of the exponential shear. The turbulence parameter follows a physically different definition than the common definition of turbulence intensity, however, the analysis shows that they are weakly correlated with each other. Overall, the model shows potential, but must be further evaluated, e.g. with additional data or simulated lidar measurements within an LES wind field.
Experimental study of mean and turbulent velocity fields in the wake of a twin-rotor vertical axis wind turbine
Authors: van der Deijl W. , Obligado M. , Sicot C. , Barre S.
Published: June 2022

The wake of a twin-rotor vertical axis wind turbine (VAWT) was measured experimentally in a wind tunnel using particle image velocimetry. Only the horizontal centre plane was measured, showing that there is no wake recovery in this central plane. The wake deficit in this plane actually increases up to x = 5.5D, independent of tip-speed-ratio. The wake of a VAWT is, therefore, not homogeneous but three-dimensional. Which also means that the aspect ratio of a VAWT is an important parameter if one wants to optimise for wake recovery. In addition, the shed vortices at the edge of the wake do not play a role in the wake recovery. They do not enhance the recovery, nor do they shield the wake from re-energising. A reason for this is that their persistence strongly depends on tip-speed-ratio yet them breaking down has no effect on the wake recovery. Furthermore, the turbulence intensity levels at the edge of the wake are relatively low and often lower than the centre part of the wake. Hence, the dominant process of wake recovery of a VAWT must be happening in the vertical plane, which is in correspondence with previous research.
Tailoring wind turbine wake models to incoming free-stream turbulence
Authors: Gambuzza Stefano , Ganapathisubramani Bharathram
Published: June 2022

Analytical wake models describe the main wake statistics using a reduced number of parameters: for instance, the Gaussian wake model relies on the single parameter k
∗ to predict the mean wake velocity. However, the robustness of these models to different inflows has been seldom tested and the effects of ambient turbulence on these coefficients are reported only in a limited number of studies.

With this objective, we measured the wake generated by a model-scale wind turbine operating in a wind tunnel at a ReD
⋍ 105 under different ambient conditions, with turbulence intensities between 3% and 12% and integral time scales between 1/10 and 10 times the convective time-scale. The wake generated by the turbine has been measured by means of planar PIV in a region spanning between 1.25 and 8.75 rotor diameters downstream of the turbine.

The results show that a description of the turbine wake with the single parameter k
∗ is not sufficient to obtain accurate predictions, while the quality of the wake estimations can be dramatically improved by introducing a virtual origin x
0. Analysis of the higher-order wake statistics shows how the former can be related to the intensity of the Reynolds shear stress in the wake, and the latter to the erratic motion of the tip-vortices.
An approach and discussion of a simulation based measurement uncertainty estimation for a floating lidar system
Authors: Bischoff Oliver , Wolken-Möhlmann Gerrit , Cheng Po Wen
Published: June 2022

In recent years, floating lidar systems (FLS) have been used in increasing numbers for project planning and design of offshore wind farm projects. To assess the quality of the measured data or the wind resources, an exact knowledge of the measurement uncertainty of the FLS is important. However, this is difficult because there is usually no reference measurement system at the offshore sites where FLSs are to be used to assess wind resources. In order to estimate the quality of the measurements or the measurement uncertainty for a site without a reference measurement system, in this case only measurement uncertainties from measurement campaigns or trial campaigns that have already been carried out can be used. However, the respective wind and wave conditions differ from location to location. In this respect, a direct transfer of the measurement uncertainty from one measurement location to another location is again subject to a certain uncertainty.

In this paper, we explore the possibilities of a different, simulation-based, approach. First, we discuss which parts of the measurement uncertainty of the FLS can be investigated in the context of simulations. Additionally, we present a workflow for the simulation of an FLS for different wind and wave conditions and an approach to determine and compare the simulated bin-averaged deviations for these conditions with real measurement data. Finally, we show a comparison of the simulated uncertainties with measurement uncertainties determined during a six-month measurement campaign of an FLS against an offshore measurement mast and a fixed reference lidar and give an outlook on possible further use cases of this simulation-based methodology.
Overcoming Blade Interference: A Gappy-POD Data Reconstruction Method for Nacelle-Mounted Lidar Measurements
Authors: Kidambi Sekar A. P. , van Dooren M. F. , Kühn M.
Published: June 2022

Nacelle-mounted lidar systems suffer data loss due to unfavourable atmospheric conditions such as rain or fog and most importantly the rotation of the blades that obstruct the laser beam from measuring upstream of the turbine. In this paper, we apply Gappy Proper Orthogonal Decomposition (Gappy-POD) to reconstruct incomplete flow fields from nacelle-mounted lidar measurements. For this purpose, two scanning nacelle-based SpinnerLidar simulations are performed inside a Large Eddy Simulation, one measuring the undisturbed wind inflow and the other in the wake of a reference turbine. Data loss of up to 90 % is simulated by artificially removing measurement points. The performance of Gappy-POD in reconstructing the wind fields is evaluated by comparing metrics such as effective wind speeds, vertical shear, yaw misalignment, wake deficit, wake meandering and the turbulent spectra in fixed and rotating frames of reference. We see that Gappy-POD is capable of accurately reconstructing missing data in comparison to normally used spatial interpolation techniques even in cases where 90 % of the data was missing. As a result, the dynamics of the reconstructed wind fields can be investigated based on highly accurate lidar-based wind field retrievals. The methodology can be used as a tool to develop effective wind field reconstruction techniques from sparse data.