Wind Energy

Published by Wiley
Online ISSN: 1099-1824
Print ISSN: 1095-4244
Discipline: Energy
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Aims and scope

Wind power is one of the major energy resources that are important components of future energy scenarios. Wind Energy offers a major forum for the reporting of advances in this rapidly developing technology with the goal of realising the world-wide potential to harness clean energy from land-based and offshore wind. The journal aims to reach all those with an interest in this field from academic research, industrial development through to applications, including individual wind turbines and components, wind farms and integration of wind power plants. Contributions across the spectrum of scientific and engineering disciplines concerned with the advancement of wind power capture, conversion, integration and utilisation technologies are essential features of the journal.

Recent publications
  • Itoje H. JohnItoje H. John
  • David H. WoodDavid H. Wood
  • Jerson R.P. VazJerson R.P. Vaz
Multi‐bladed windmills usually pump water for agriculture and domestic consumption, often in remote locations. Although they have been around for over 150 years, their aerodynamic performance is still poorly understood. This paper describes the use of helical vortex theory (HVT) and blade element momentum (BEM) analysis to predict windmill thrust, torque, and extracted power. We emphasize the unusual features of windmills: low Reynolds numbers and tip speed ratios and high solidity, all related to the generation of high torque at low wind speeds. Wind tunnel tests on a model rotor with 3, 6, 12, and 24 circular‐arc, constant‐chord blades determined the thrust, torque, and extracted power over a range of tip speed ratio that extended to runaway. For comparison, BEM was implemented with a correction for finite blade number derived from HVT, as well as the classical Prandtl tip loss factor. The HVT correction predicted the rotor power coefficient to within 3% of the test data on the average. At low tip speed ratios and smaller blade numbers, HVT was consistently more accurate than the Prandtl factor. At all blade numbers, the measured rotor torque exceeded the BEM predictions at the lowest tip speed ratios indicating stall delay which became more important (and more beneficial for windmill performance) as the blade number increased. The Prandtl formulation predicted the thrust to within a mean accuracy of 13% and was more accurate than the HVT method.
Aeroelastic parked testing of a unique downwind two‐bladed subscale rotor was completed to characterize the response of an extreme‐scale 13‐MW turbine in high‐wind parked conditions. A 20% geometric scaling was used resulting in scaled 20‐m‐long blades, whose structural and stiffness properties were designed using aeroelastic scaling to replicate the nondimensional structural aeroelastic deflections and dynamics that would occur for a lightweight, downwind 13‐MW rotor. The subscale rotor was mounted and field tested on the two‐bladed Controls Advanced Research Turbine (CART2) at the National Renewable Energy Laboratory's Flatiron Campus (NREL FC). The parked testing of these highly flexible blades included both pitch‐to‐run and pitch‐to‐feather configurations with the blades in the horizontal braked orientation. The collected experimental data includes the unsteady flapwise root bending moments and tip deflections as a function of inflow wind conditions. The bending moments are based on strain gauges located in the root section, whereas the tip deflections are captured by a video camera on the hub of the turbine pointed toward the tip of the blade. The experimental results are compared against computational predictions generated by FAST, a wind turbine simulation software, for the subscale and full‐scale models with consistent unsteady wind fields. FAST reasonably predicted the bending moments and deflections of the experimental data in terms of both the mean and standard deviations. These results demonstrate the efficacy of the first such aeroelastically scaled turbine test and demonstrate that a highly flexible lightweight downwind coned rotor can be designed to withstand extreme loads in parked conditions.
The thermal heterogeneity between the land and sea might affect the wind patterns within wind farms (WF) located near seashores. This condition was modeled with a large‐eddy simulation of a numerical weather prediction model (Weather Research and Forecasting) that included the wind turbine actuator disk model (ADM). The assumed condition was that the downstream surface temperature was relatively higher (unstably stratified condition) than the neutrally stratified upstream wind. Under this condition, a thermal internal boundary layer (TIBL) was developed from an area where a step‐changed surface temperature was implemented. The combined effect of the wake deficit due to the WF and velocity recovery as a result of enhanced mixing under unstable stratification showed significant modulation of the wind speed at the hub height when local atmospheric stability affected the wind turbine (WT). We show that TIBL height depends on the variables to be evaluated as the threshold. A precise prediction of the TIBL height is beneficial for better estimation of power generation. A prediction model was proposed as an extension of the internal boundary layer (IBL) model for neutral stratification, and the results tracked TIBL development reasonably well. The effects of WFs on surface properties (e.g., friction velocity, heat flux, and Obukhov length) and the tendency of IBL growth were minor. A single WT wake was also assessed under several TIBL developmental stages (i.e., location) and thermal stratification conditions. The standard deviation of the wake deficit increased vertically during the development stage of the TIBL. In contrast, the coefficients in the horizontal and vertical directions were comparable when the WT was deep inside the TIBL.
Growing horizontal axis wind turbines are increasingly exposed to significant sources of unsteadiness, such as tower shadowing, yawed or waked conditions and environmental effects. Due to increased dimensions, the use of steady tabulated airfoil coefficients to determine the airloads along long blades can be questioned in those numerical fluid models that do not have the sufficient resolution to solve explicitly and dynamically the flow close to the blade. Various models exist to describe unsteady aerodynamics (UA). However, they have been mainly implemented in engineering models, which lack the complete capability of describing the unsteady and multiscale nature of wind energy. To improve the description of the blades' aerodynamic response, a 2D unsteady aerodynamics model is used in this work to estimate the airloads of the actuator line model in our fluid–structure interaction (FSI) solver, based on 3D large eddy simulation. At each section along the actuator lines, a semi‐empirical Beddoes‐Leishman model includes the effects of noncirculatory terms, unsteady trailing edge separation, and dynamic stall in the dynamic evaluation of the airfoils' aerodynamic coefficients. The aeroelastic response of a utility‐scale wind turbine under uniform, laminar and turbulent, sheared inflows is examined with one‐ and two‐way FSI coupling between the blades' structural dynamics and local airloads, with and without the enhanced aerodynamics' description. The results show that the external half of the blade is dominated by aeroelastic effects, whereas the internal one is dominated by significant UA phenomena, which was possible to represent only thanks to the additional model implemented.
Vertical seismic waves, which are primarily due to pressure waves in the ground, can propagate with the same intensity in the seawater and impact floating bodies such as floating wind turbines (FWTs). Part of this wave can further propagate in the tower and generate large vertical accelerations in the nacelle. This paper presents a methodology for computation of the pressure waves generated by vertical earthquake shaking, referred to as seaquake, its impact on submerged bodies, and the induced dynamic response in the structure. A FWT concept with catenary mooring is used for the assessment of the effects of earthquake shaking. The pressure during a seaquake is determined using a 2D acoustic finite element (FE) model in Abaqus. The acoustic model is benchmarked against a 1D analytical solution. The response due to the environmental loads, namely, wind, current, and waves, is also studied and used as a reference for assessment of the relative significance of the seaquake. Considerable vertical accelerations can occur in the nacelle due to amplification of the platform accelerations through the tower. It is shown that this acceleration could exceed a commonly used operational limit range of 0.2 g to 0.3 g even for moderate accelerations at the seabed. This indicates that earthquake loading should be considered in the design of FWTs in seismic regions. The mooring tensile forces, due to motion of the platform during a seaquake, do not exceed the design tension computed for the extreme environmental conditions. However, the leeward mooring lines could experience zero tension, which could cause snap tension.
As the height of wind turbine increases, the lightning strike accident has become a non‐negligible issue. In this paper, the lightning attachment characteristic of a 2‐MW wind turbine generator (WTG) is investigated using a model with a reduced scale of 100. The WTG model is equipped with receptors on the blades and a lightning rod on the nacelle, both serving as its external lightning protection system (LPS). The high‐voltage electrode, which delivers a lightning impulse voltage from a Marx generator, is used to simulate the final stage of downward negative lightning strikes from 29 coming‐leader positions. The experimental results indicate that lightning leaders from either front directions or side directions could be intercepted effectively by LPS, whereas the back‐direction lightning could not. Moreover, with the increase of striking distance, the capture ratio for the insulation part of blade decreases. Electric field intensity distribution simulations for the full‐scale WTG model, using conditions similar to their experimental counterparts, are conducted and compared with the lightning attachment distribution. Based on classical electro‐geometrical model, a simulative method is proposed to predict the lightning attachment distribution of WTG. Our results indicate that it is feasible with this method to produce a satisfactory approximation to the experimentally obtained lightning attachment distribution.
Curved tip extensions are among the rotor innovation concepts that can contribute to the higher performance and lower cost of horizontal axis wind turbines. One of the key drivers to exploit their advantages is the use of accurate and efficient computational aerodynamic models during the design stage. The present work gives an overview of the performance of different state‐of‐the‐art models. The following tools were employed, in descending order of complexity: (i) a blade‐resolved Navier Stokes solver, (ii) a lifting line model, (iii) a vortex‐based method coupling a near‐wake model with a far‐wake model, and (iv) two implementations of the widely used blade element momentum method (BEM), with and without radial induction. The predictions of the codes were compared when simulating the baseline geometry of a reference wind turbine and different tip extension designs with relatively large sweep angle and/or dihedral angle. Four load cases were selected for this comparison, to cover several aspects of the aerodynamic modeling: steady power curve, pitch step, extreme operating gust impact, and standstill in deep stall. The present study highlighted the limitations of the BEM‐based formulations to capture the trends attributed to the introduction of curvature at the tip. This was true even when using the radial induction submodel. The rest of the computational methods showed relatively good agreement in most of the studied load cases. An exception to this was the standstill configuration, as the blade‐resolved Navier‐Stokes solver was the only code able to capture the highly unsteady effects of deep stall.
This study investigates the implementation of the vortex particle method (VPM) with the goal of efficiently and accurately estimating the power performances and flow characteristics for a Savonius rotor. The accuracy and efficiency of simulation methods are critical for the reliable design of Savonius rotors. Among various approaches, VPM is chosen because it can be flexibly incorporated with self‐correction techniques, and the distribution of bound vortex particles can effectively represent complex geometries. In this work, a double‐trailing‐edge‐wake‐modeling vortex particle method (DTVPM) is presented to extend the working range of VPM for dealing with large rotating amplitudes and high tip speed ratios (TSRs). DTVPM addresses asymmetrical torque predictions for a Savonius rotor without gap width. However, DTVPM performs poorly at high TSRs due to the absence of viscous effects near the surface. To capture complex wake structures, such as reverse flow structures, the viscous correction for tip vortices is suggested. The current research focuses on the implementation and validation of DTVPM for predicting torque coefficients and wake patterns, as well as comparisons to OpenFOAM results. Two‐dimensional and incompressible flow is estimated at λ$$ \lambda $$ = 0.2–1.2. For the studied cases, a maximum power coefficient is obtained at λ≈0.8$$ \lambda \approx 0.8 $$, consistent with published experimental data. In addition, the process of trailing‐edge vortices generation and detachment is captured by DTVPM. The comparison results between OpenFOAM and DTVPM show that DTVPM allows to efficiently simulate a Savonius rotor without any empirical parameters. DTVPM will help to improve existing engineering models for wind energy fields.
Active trailing‐edge technology is a promising application for localized load alleviation of large‐diameter wind turbine rotors, accomplished using one or more control surfaces in the rotor blade's outer region. This work focuses on identifying noise contributions from the flap side‐edge and the trailing edge in a laboratory condition. Measurements were conducted in the Acoustic Wind Tunnel Braunschweig (AWB) at the German Aerospace Center's (DLR) Braunschweig site. The small‐scale model has a span of 1,200 mm and a chord length of 300 mm. The control surface, a plain flap, has a span of 400 mm and a chord length of 90 mm. Far‐field noise was measured using a phased‐microphone array for various flow speeds, angles of attack, and flap deflection angles. Due to the size of the model and assumed closeness of the sound sources, two noise reduction addons were installed interchangeably: trailing‐edge brush and flap side‐edge porous foam for sound source identification. Analysis of the far‐field noise reveals that, while changes to the flap deflection angle alter the far‐field noise spectra, the trailing‐edge noise remains the predominant noise source at deflection angles −5∘$$ -{5}^{\circ } $$ and 5∘$$ {5}^{\circ } $$. No additional noise level was observed from the flap side edge within the measurable frequency range at these angles. The flap side‐edge noise has an increased role for frequency larger than 2 kHz for the larger flap deflection angles of −10∘$$ -1{0}^{\circ } $$ and 10∘$$ 1{0}^{\circ } $$.
The cover image is based on the Review Article Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends by Omer Khalid et al., https://doi.org/10.1002/we.2773.
Wind turbine design encompasses many different aspects including aerodynamic, structural, electrical, and control system design. To achieve optimal plant performance, a system design approach is utilized in which the performance of the whole wind turbine is evaluated and quantified during operational scenarios with subsystem interactions. In this paper, the design for a Segmented Ultralight Morphing Rotor (SUMR) 50‐MW wind turbine is presented utilizing levelized cost of energy (LCOE) for design choices, with additional quantification of simulated performance shortcomings at the 50‐MW scale. The multi‐disciplinary design process results in a final ultra‐scale turbine configuration that outperforms other existing offshore wind farms regarding the LCOE.
Many wind energy projects start with resource assessments based on outputs from numerical models followed by meteorological campaigns. These models are imperfect and have a substantial input parameter space. It is difficult to discern how the inputs affect the simulation results. Surface roughness is generally crudely represented in mesoscale models. In this work, we use the Weather Research and Forecast model to simulate winds at a meteorological mast in Mexico with wind observations at 80 m. The model sensitivity to changes in surface roughness is contrasted with permissively perturbed orography fields to gauge the observed changes' relative importance. Changes in surface roughness affect the root mean square error of the simulated 80‐m winds compared to the observations, in the same order of magnitude as orography perturbations. Using a roughness field derived from a synthetic aperture radar improved the wind speed bias. Wind speed predictions using high‐resolution (185 m) simulations sometimes showed excellent agreement with the observations, but there were several instances when medium‐resolution (1.6 km) simulations performed better. A wind speed time series with high temporal (10 min) resolution from a low spatial (6 km) resolution simulation was used to train a deep neural network regression (DNNR) model. The trained DNNR reduced the wind speed error the most compared to any of the other simulations performed in this study. The second‐best model performance was obtained using the radar roughness‐derived field. The results of the simulations at medium resolution with perturbed orography were very similar to those using different roughness fields inputs.
The rotor of a large diameter wind turbine experiences more substantial and more dynamic loads due to the fluctuating and heterogeneous wind field. The project SmartBlades 2.0 investigated rotor blade design concepts that alleviate aerodynamic loading using active and passive mechanisms. The present work evaluates the acoustics of the two load alleviating concepts separately, an inboard slat and an outboard flap, using the Fast Random Particle Mesh/Fast Multipole Code for Acoustic Shielding (FRPM/FMCAS) numerical prediction toolchain developed at DLR with input from the averaged flow field from RANS. The numerical tools produce a comparable flap side‐edge noise spectrum with that of the measurement conducted in the Acoustic Wind Tunnel Braunschweig (AWB). The validated FRPM/FMCAS was then used to analyze the self‐noise from a slat at the inboard section of a rotor blade with a 44.45 m radius and compared with that from the outboard trailing edge. Furthermore, the rotational effect of the rotor was included in the post‐processing to emulate the noise observed at ground level. The findings show an increase in the slat's overall sound pressure level and a maximum radiation upwind of the wind turbine for the case with the largest wind speed that represents the off‐design condition. In operational conditions, the slat adds at most 2 dB to the overall sound pressure level. The toolchain evaluates wind turbine noise with conventional or unconventional blade design, and the problem can be scaled up for a full‐scale analysis. As such, the tools presented can be used to design low‐noise wind turbines efficiently.
The spatiotemporal characteristics of the near‐surface wind speed (NWS), wind speed at 100 m hub height (HWS), and wind power density (WPD) over China are assessed during 1980–2021. A homogenization process is applied to NWS at 292 basic meteorological stations. A total of 336 breakpoints are recognized, with 122 associated with instrument replacement, 113 attributed to station relocation, and 101 due to unrecorded reasons. The homogenization method does not alter the spatial patterns or seasonal variations of NWS, but it does boost the mean NWS over China annually and seasonally, while also strengthening the long‐term decreasing trends. As for the temporal standard deviation (STD) for NWS, high values are primarily found over Inner Mongolia and Northeast China, with the seasonal maximum occurring in spring. After homogenization, the STD of NWS over China is reduced annually and seasonally, and the long‐term decreasing trends are somewhat weaker. The results for HWS are comparable to those for NWS. Notably, the lower mean state and weaker fluctuation of wind speed in recent years have two opposing implications for wind power production. Similar to the NWS mean state, the annual mean WPD over China is largely increased after homogenization with a faster decreasing trend.
The yaw moment of wind turbine rotors has never been in the focus of wind turbine aerodynamics. With the increasing activities in the development of support structures for Floating Offshore Wind Turbines (FOWT), which passively align with the wind, the interest has shifted, as an accurate determination of the yaw moment is a crucial issue for a successful design of such power plants. A downwind coned rotor is a promising option to increase the yaw moment and therefore the self‐alignment capability of a passively yawing FOWT. Unfortunately, experimental and numerical studies on the estimation of the yaw moment of wind turbine rotors are rare. This is especially the case for downwind coned rotors. The aim of the present work is to provide reliable knowledge in this field. For this purpose, an extensive experimental and numerical study is carried out to determine the yaw moment of a downwind coned rotor. The results obtained from measurements in the wind tunnel are compared to those of simulations using a high fidelity RANS method and a blade element momentum theory (BEMT) method. BEMT is widely applied and can be considered as state of the art for predicting aerodynamic loads on FOWTs. However, the basic assumptions of BEMT do not account for a realistic influence of the skewed wake, so that the application of a correction method is necessary. In this work, the frequently used wake skew correction method based on Pitt and Peters is utilised and its influence on the calculation of the yaw moment is investigated. It is shown that this correction method yields a significant overprediction of the yaw moment in comparison to the measurements and consequently even impairs the quality of the simulation in this case. In contrast to this, the wake‐resolving RANS method is capable of reproducing the measurements with reasonable accuracy and provides valuable insight into the role of the lateral force for the measurement of the yaw moment.
This article proposes an efficient correction model that enables the extension of the blade element momentum method (BEM) for swept blades. Standard BEM algorithms, assuming a straight blade in the rotor plane, cannot account for the changes in the induction system introduced by blade sweep. The proposed extension corrects the axial induction regarding two aspects: the azimuthal displacement of the trailed vorticity system and the induction of the curved bound vortex on itself. The extended algorithm requires little additional processing work and maintains BEM's streamtube independent approach. The proposed correction model is applied to simulations of swept blade geometries based on the IEA 15 MW reference wind turbine. Results show good agreement with lifting line simulations that inherently can account for the swept blade geometry. Blade sweep couples bending and torsion deformations by curving the blade axis in the inplane direction. As such, it can be used to passively alleviate loads and, thus, aeroelastically tailor wind turbine blades. The implementation of aeroelastic tailoring techniques, and the aeroelastic analysis in general, becomes increasingly significant with the size of wind turbine rotors continually rising. Due to its low computing complexity, BEM remains a crucial tool in the aerodynamic and aeroelastic analysis of wind turbine rotors. Thus, the proposed correction model contributes to a fast and accurate evaluation of swept blade designs.
Gusty wind in urban areas contains significant energy potential that can be harnessed, but the existing wind turbine rotor speed control systems based on continuous wind speed tracking have a noticeable response delay as compared to the duration of the common short gusts, inducing a significant deficit in harnessed excess energy content (EEC) from gusty wind. This work scrutinizes the energy content distribution among the gusty wind components and their differential impacts on the control response to identify factors to improve response timeliness. Cross‐correlation spectrum analysis between the wind turbine response and the local wind spectrum was used to identify the critical tracking frequency that can significantly reduce the average response delay of wind turbines in gusty wind conditions while maximizing the gust energy harvesting ability was developed and validated by various time‐domain simulation results. Combining the critical tracking frequency with the theoretical estimation of the EEC of a wind turbine, the expected EEC harnessing ability of a wind turbine can be estimated before installation. For the sample Savonius wind turbine in this work, the response delay of the wind turbine was shortened by 65%, leading to 25% more harnessed EEC from gusty wind.
In this article, we present a time‐varying formulation of the curled wake model that we implemented in FAST.Farm. The curled wake model, originally developed for steady‐state conditions, is used to produce realistic wake profiles behind a wind turbine in yawed (or skewed) conditions. We begin by introducing the key elements of the FAST.Farm framework. Then, after briefly summarizing the original wake dynamics formulation of FAST.Farm based on a polar wake profile, we present the new time‐varying formulation of the curled wake model, compare the two, and highlight the differences with the original curled wake model. After discussing some implementation details, we present different applications with increasing levels of complexity: single turbine with uniform and turbulent inflow, fixed and transient yaw, and multiple turbines. We verify our results using the original FAST.Farm implementation and large‐eddy simulations. The results with the new curled wake model are improved compared to the original implementation, as they include cross‐flow velocities and wake asymmetry. Yet, large‐eddy simulation results show a more pronounced lateral convection of the wake and a stronger concentration of vorticity at the top vortex. The new curled wake implementation in FAST.Farm should enable the calculation of not only generator power but also wind turbine structural loads for applications involving intentional or unintentional skewed flow and wind‐farm control involving wake steering.
Wake steering via deliberate yaw offset is an emerging wind farm control technique that has the potential to mitigate wake losses and further increase wind farm energy yield. The loads impact of this technique has been studied, but there is limited insight into wind‐farm‐wide impacts of wake steering. Understanding such impacts is crucial to determining the feasibility of using wake steering in commercial wind farms. To that end, this work investigates the impacts of wake steering on the loads of all turbine components across all turbines in a wind farm operating under a broad set of inflow conditions, including inflow velocity, shear exponent, turbulence class, and inflow angle. This was done by performing FAST.Farm simulations of a 12‐turbine wind farm array, excerpted from a larger hypothetical wind farm. The International Energy Agency Wind 15‐MW reference wind turbine was modeled atop a monopile substructure, an open‐source model that closely approximates the properties of similar commercial options. Wake steering was included via yaw offsets that were computed using an offline optimization with the National Renewable Energy Laboratory tool FLORIS. For each inflow case, the 12‐turbine array was simulated with and without wake steering. Results were compared in terms of time‐averaged means, standard deviations, ultimate loads, and damage‐equivalent loads. The findings show that because wake steering is generally applied at rated wind speeds and below, it is unlikely to drive ultimate loads. For fatigue loads, wake steering does increase the overall fatigue accumulation for some load channels, such as blade‐root and shaft bending. This is to be expected when overall power yield increases but may cause the damage accumulation to be more uniform throughout the array. The significance of the added fatigue loading is dependent on how frequent wake steering is utilized in the overall set of inflow conditions across the wind rose.
Experimental data are reported for a wind turbine array boundary layer (WTABL) in a model wind farm. An array of 95 model wind turbines consisting of 5 streamwise columns by 19 spanwise rows was studied in a high Reynolds number boundary layer in the Flow Physics Facility (FPF) at the University of New Hampshire. The wind turbine array was constructed of porous disks of 0.25 m diameter, which were drag (thrust) matched to typical offshore wind turbine operating conditions. The turbine spacing was 8 diameters in the streamwise and 4 diameters in the spanwise directions. Spires were used to thicken the boundary layer and achieve a boundary layer thickness on the order of 1 m at the first row of the wind turbine array, which is located 33 m downstream from the test section inlet, thus placing the turbines in the bottom 1/3 of the boundary layer. Velocity profiles were measured with a pitot tube in the center column of the array. To within experimental uncertainty, a fully developed WTABL condition is observed in the mean velocity, for defined inlet conditions and spacings, from row 12 on. The wind turbine array acts as a sparse displaced roughness: it creates an internal layer whose origin (in the wall‐normal direction) remains fixed in space, while the turbulent boundary layer the array was placed in continues to grow. Careful consideration was given to an expanded uncertainty analysis, which elucidates the need for long measurement times in large facilities. Porous disk turbine models are the experimental equivalent of numerical actuator disks; therefore, this publicly available data set is expected to be useful for numerical model validation.
Wind power prediction (WPP) is extremely important in promoting the power grid's consumption of wind power. To improve the accuracy of WPP, a three‐stage multiensemble short‐term WPP method based on ensemble learning and deep learning is proposed in this paper. In the first stage, variational mode decomposition and wavelet transform were applied to decompose the original data sequence into different frequency bands. In the second stage, based on the decomposition sequences, the stacked denoising autoencoder (SDAE), long short‐term memory (LSTM), and bidirectional long short‐term memory (BLSTM) were used to predict the sequences; and 42 submodels were obtained. In the third stage, a support vector machine (SVM) was applied to give weight to each submodel to obtain the final ensemble prediction results. Based on three‐stage integration, a new multi‐integration model is proposed that repeats the third‐stage integration operation. A case study is presented to verify the effectiveness and superiority of the proposed three‐stage multi‐integration WPP method. The normalized root mean square error (NRMSE) decreased by 0.0343 compared with LSTM, decreased by 0.0336 compared with BLSTM, and decreased by 0.0323 compared with SDAE, which demonstrated the effectiveness of the proposed new multistage ensemble and deep learning WPP method.
Modern wind turbines have multiple sensors installed and provide constant data stream outputs; however, there are some important quantities where installing physical sensors is either impractical or the sensor technology is not sufficiently advanced. An example of such a problem is, for example, sensing the shape and location of wake‐induced wind deficits caused by upwind turbines—a feature which would have relevant application in wind farm control; however, it is hard to detect physically due to the need of scanning the airflow in front of the turbine in multiple locations. Another control‐related example is monitoring and predicting the blade tip‐tower clearance. A “virtual sensor” can be created instead, by establishing a mathematical relationship between the quantity of interest and other, measurable quantities such as readings from already available sensors (e.g., SCADA, lidars, and met‐masts). Machine Learning (ML) approaches are suitable for this task as ML algorithms are capable of learning and representing complex relationships. This study details the concept of ML‐based virtual sensors and showcases three specific examples: blade root bending moment prediction, detection of wind turbine wake center location, and forecasting of blade tip‐tower clearance. All examples utilize sequence models (Long Short‐Term Memory, LSTM) and use aeroelastic load simulations to generate wind turbine response time series and test model performance. The data types used in the examples correspond to channels that would be available from high‐frequency SCADA data combined with a blade and tower load measurement system. The resulting model performance demonstrates the feasibility of the ML‐based virtual sensor approach.
Realizing carbon neutral energy generation creates the challenge of accurately predicting time‐series generation data for long‐term capacity planning and for short‐term operational decisions. The key challenges for adopting data‐driven decision‐making, specifically predictive analytics, can be attributed to data volume and velocity. Data volume poses challenges for data storage and retrieval. Data velocity poses challenges for processing the data near real time for operational decisions or for capacity building. This manuscript proposes a novel prediction method to tackle the above two challenges by using an event‐based prediction in place of traditional time series prediction methods. The central concept is to extract meaningful information, denoted by events, from time‐series data and use these events for predictive analysis. These extracted events retain the information required for predictive analytics while significantly reducing the volume of the velocity of data; consequently, a series of events present the information at a glance, effectively enabling data‐driven decision‐making. This method is applied to a data set consisting of six years of historical wind power capacity factor and temperature measurements. Deploying five deep learning models, a comparison is drawn between classical time‐series predictions and series of events predictions based on computational time and several error metrics. The computational analysis results are presented in graphical format and a comparative discussion is drawn on the prediction results. The results indicate that the proposed method obtains the same or better prediction accuracy while significantly reducing computational time and data volume.
Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7‐year‐long data set of hourly, plant‐level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1‐year of records (2019) to evaluate an operational, high‐resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.
This paper reports results from a 2‐month validation campaign of near‐shore wind measurements taken by a dual scanning light detection and ranging (LiDAR) system at a coastal site in Japan. A meteorological mast and a vertical profiling LiDAR device were deployed at an offshore research station approximately 1.5 km from the coast. Offshore winds at heights of 66, 120, and 180 m above sea level were observed by the scanning LiDAR system. Comparisons with a sonic anemometer found that the radial velocities had a coefficient of determination of more than 0.99 without unreasonable bias. The accuracy of 10‐min mean wind speeds and directions was then evaluated. The 10‐min values from the dual scanning LiDAR system were accurate enough to satisfy the acceptance criteria used for floating LiDAR systems. In addition, the vertical velocity and direction profiles from the dual scanning LiDAR system were compared with those from a vertical profiling LiDAR device. The performance of turbulence intensity (TI) measurements was also evaluated. Although the TIs from the dual scanning LiDAR system were slightly lower than those from the sonic anemometer, they were equivalent to those from the cup anemometers. This investigation concluded that the near‐shore wind measurements using the dual scanning LiDAR system are beneficial for reducing near‐shore wind measurement costs, because the system can measure not only 10‐min wind speed and direction, but also parameters associated with assessing site‐specific conditions without installing offshore meteorological masts.
Wind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.
Wind energy often plays a major role in meeting renewable energy policy objectives; however, increased deployment can raise concerns regarding the impacts of wind plants on certain wildlife. Particularly, estimates suggest hundreds of thousands of bat fatalities occur annually at wind plants across North America, with potential implications for the viability of several bat species. One approach to reducing bat fatalities is shutting down (or curtailing) turbines when bats are most at risk, such as at night during relatively low wind speed periods throughout summer and early autumn. While curtailment has consistently been shown to reduce bat fatalities, the lost power production reduces revenues for wind plants. This study conducted simulations with a range of curtailment scenarios across the contiguous United States to examine sensitivities of annual energy production (AEP) loss and potential impacts on economic metrics for future wind energy deployment. We found that AEP reduction can vary across the country from less than 1% to more than 10% for different curtailment scenarios. From an estimated 2891 gigawatts (GW) of simulated economically viable wind capacity (measured by a positive net present value), we found the mid curtailment scenario (6.0 m/s wind speed cut‐in from July 1 through October 31) reduced the quantity of economic wind capacity by 274 GW or 9.5%. Our results indicate that high levels of curtailment could substantially reduce the future footprint of financially viable wind energy. In this context, future work that illuminates cost‐effective strategies to minimize curtailment while reducing bat fatalities would be of value.
Recently, floating wind has enjoyed much public and governmental attention and is commonly identified as a crucial next step on the route to net zero targets for many countries. However, there is currently a large gap between the scale and readiness‐level of devices in the water and the end‐of‐ the‐decade aspirational targets of many governments. To facilitate the expansion of the industry, a wider acceptance and agreement of the realistic potential of floating wind and the barriers to the sector's development is required, particularly over this crucial 10‐year period, something currently scarce in published literature. In this work, a stakeholder engagement was performed, with subsequent analysis of the results to quantify industry expectations and identify the predominant technical and financial concerns of key stakeholders. The presented results of the work serve to both provide a point of comparison and updated account of industry‐based expert views in the near term, with further scientific analysis investigating distinctions between different stakeholder types. Secondarily, the key concerns of industry were analysed and compared with a literature review of recent research effort within floating wind, providing a high‐level gap analysis. A knowledge of the gaps can inform future areas for academic enquiry, ensuring the future needs of industry and the focus of research is aligned.
The input for fatigue analyses of offshore wind turbines is typically chosen based on design values provided by design standards. While this provides a straightforward design methodology, the contribution of different input parameters to the uncertainty in the fatigue damage estimates is usually unknown. This knowledge is important to have when improving current designs and methodologies, and the parameters governing the uncertainty is typically found through a sensitivity analysis. Several sensitivity studies have been performed for monopile‐based offshore wind turbines, typically focusing on specific turbines and engineering disciplines. This paper performs a sensitivity study for three monopile‐based offshore wind turbines (5 MW, 10 MW, 15 MW) using parameters from several engineering disciplines. The results show that the fatigue utilization is primarily governed by the uncertainty in the SN curves and fatigue capacity. Following this, the uncertainty in the environmental conditions is the dominating uncertainty, with wind loads becoming increasingly important as turbine size increases. Additionally, the effect of modelling uncertainties is investigated. The wind‐related model uncertainties dominate in the tower top, while uncertainties in the wave and soil models dominate in the tower base and monopile. Designers wanting to reduce the uncertainty in a design are recommended to focus on the environmental conditions, and using as accurate models as possible. All modelling uncertainties are significant, but research should particularly be focused on wave directionality and soil models.
The Global Atlas for Siting Parameters project compiles a suite of models into a complex modeling system, uses up‐to‐date global datasets, and creates global atlases of siting parameters at a spatial resolution of 275 m. These parameters include the 50‐year wind, turbulence, and turbine class recommendations based on relevant generic turbines. The suite of models includes the microscale Linear Computational Model (LINCOM), a statistical, spectral correction method here revised for strong convective areas and tropical cyclone affected areas, two turbulence models with four setups, and load models. To this complexity, an uncertainty model was developed to classify the various sources of uncertainties for both the extreme wind and the turbulence calculations, and accordingly, atlases of uncertainty classification were created. Preliminary validation of the global calculations of the 50‐year wind and turbulence is done through comparisons with measurements, and the results are promising. This is the first time the siting parameters are obtained with such a high spatial resolution and shared on open data portal. It is expected to benefit the global wind energy planning and development.
Ramp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.
This work aims at assessing the loads and the fatigue estimates computed using an advanced actuator disk (AD) method coupled to large eddy simulation, at a resolution appropriate for large wind farm calculations. In order to compute pertinent fatigue loads, blade trajectories are replicated through the disk, and the AD aerodynamic forces are interpolated onto these “virtual blades” at each time step. This approach, denoted AD‐B, is verified against a Vortex‐Particle Mesh (VPM) method coupled to immersed lifting lines at a fine resolution, through simulations of an isolated rotor in a turbulent wind. Two different methods are used to evaluate the fatigue damages: the widely used Rainflow counting (RFC) procedure and the spectral Dirlik's approach (DK), both combined with a Palgrem–Miner rule. In the present work, the DK counting method is considered to further investigate the potential of extrapolating some loading data in the high frequencies, uncaptured by the AD‐B model at a coarse resolution. The results show that the fatigue estimates computed using the RFC procedure are very similar for the VPM and the AD‐B methods, thus without any modeling of the unresolved scales for the disk. Indeed, the AD‐B model captures the loads of middle and large amplitudes that contribute the most to the rotor damage. The use of extrapolated loading data makes the AD‐B fatigue estimates closer to the VPM ones, but the DK counting method globally leads to results that are quite different from those obtained using the RFC procedure. Further investigations are thus required for the combination of extrapolated loading information and spectral counting methods.
This paper presents a new method for approximately modeling 2‐D ideal steady fluid flows with finite vorticity induced by actuator curves of arbitrary shape. An actuator curve is an infinitesimally thin region containing a body force density acting on the flow, while the rest of the flow is force‐free. The approach can be used for any flow satisfying this description, but, like other actuator methods, it is naturally suited for modeling turbines or propellers. We derive a weak formulation of the governing equations in the Lagrange streamfunction ψ$$ \psi $$ and solve it using finite elements. Compared to related methods such as the actuator cylinder (AC) approach, our formulation is uniquely suited for computations involving wake–wake or wake–turbine interactions. We validate the method by computing the flow through a single Darrieus vertical‐axis wind turbine (VAWT) and comparing with previous work. To demonstrate the ability to simulate interacting actuators of arbitrary configuration, we simulate a three‐VAWT array. The turbines are modeled in a freestream, and the loading is chosen to represent ideal airfoils. The standard VAWT results are consistent with previous work, validating the method. The three‐VAWT array demonstrates a higher efficiency than the single VAWT (0.56 vs. 0.52), with differing optmal tip speed ratios for the upwind and downwind turbines (upwind: 3.9, downwind: 3.1. The optimum for a single turbine is 3.6). The flow field of the three‐VAWT array shows expected features such as an acceleration of flow between the two counter‐rotating upwind turbines.
The frequency dependant active current injection (FDACI) as proposed in Göksu et al.³
Modified‐frequency dependant active current injection (FDACI) proposed in this work
the implemented PLL in Göksu et al.³
A simple system consisting of a wind turbine (WT) connected to an AC grid with a three phase fault with negligible impedance occurring near the grid transformer (trgrid)
Response of wind turbine (WT) to a severe fault with (a) frequency dependant active current injection (FDACI) with normalisation in phase‐lock‐loop (PLL) and with the dead‐zone (which is the original implementation of FDACI), (b) FDACI without normalisation in PLL and with the dead‐zone, and (c) FDACI without normalisation in PLL and with logical circuit replacing the dead‐zone
Grid codes mandate that wind turbines (WTs) remain connected even during severe faults. This requirement can lead to WTs' loss of synchronisation (LoS) with the AC grid under severe symmetrical faults. In this paper, the LoS is illustrated by the power balance principle. Then, a solution with improved performance for avoiding LoS during severe faults is proposed, implemented and verified by simulations. The results show that the solution eliminates the steady‐state frequency deviation during faults, resulting in a smoother WT transient during and post‐fault.
The manuscript presents a novel numerical investigation on the impact of the wake of a floating IEA wind 15‐MW reference wind turbine (RWT) on downstream machines using a state‐of‐the‐art vortex solver coupled to a multi‐body code. The coupling enables to account for the flexibility of the different turbine components as well as to include the effect of the controller and the dynamics of the floating support structure. First, the turbine is mounted on the WindCrete spar‐buoy platform, and the wake impact on a second turbine positioned at different downstream positions is investigated and compared with the impact of the wake generated by a bottom‐fixed machine. It is found that the faster breakdown of the vortex structures triggered by the motion of the floater in the upstream turbine increases the power production of a downstream machine, as well as its mean thrust level relative to normal operation downstream a bottom‐fixed machine. It is demonstrated that this effect is drastically reduced with the increase of the turbulence intensity (TI). Further, simulations with a prescribed harmonic motion of the upstream turbine in surge and pitch under different turbulence levels are presented. It is found that the motion of the floater has a strong impact in the generated wake and consequently in the operation of downstream machines. In particular, downstream turbines experienced considerably higher blade loading that led to an increase of the aerodynamic power. Finally, aero‐hydro‐servo‐elastic simulations of five turbines in a row have shown that the interaction between multiple floating machines is more dynamic than between bottom‐fixed turbines. This has been mainly observed at high wind speeds, where the pitch and surge motions of the floater in turbines located deep inside the farm can be resonantly excited by the interaction with the wind farm flow. In practice, this means that the power and thrust variations increase with the turbine location depth inside the farm. Overall, the study highlights the importance of an accurate flow and wake modeling for the prediction of turbine‐to‐turbine interaction in a floating context.
Wind power technology has changed rapidly in recent years. Technology innovation, evolving power markets, and competing land and ocean uses continue to influence the design and operation of wind turbines and plants. Anticipating these trends and their impact on future facilities can inform commercial strategies and research priorities. Drawing from a recent survey of 140 of the world's foremost wind experts, we identify expectations of future wind plant design in 2035, both for onshore and offshore wind. Experts anticipate continued growth in turbine size, to 5.5 (onshore) and 17 MW (offshore), with plants located in increasingly less favorable wind and siting regimes. They expect plant sizes of 1,100 MW for fixed‐bottom and 600 MW for floating offshore wind. Experts forecast enhanced grid‐system value from wind through significant to widespread use of larger rotors, hybrid projects with batteries and hydrogen production, and more. To explain experts' perspectives on future plant design and operation, we identify five mechanisms: economies of unit, plant, and resource scale; grid‐system value economies; and production efficiencies. We characterize learning effects as a moderating influence on the strength of these mechanisms. In combination, experts predict that these design choices support levelized cost of energy reductions of 27% (onshore) and 17%–35% (floating and fixed‐bottom offshore) by 2035 compared to today, while enhancing wind energy's grid service offerings. Our findings provide a much‐needed benchmark for representing future wind technologies in power sector models and address a critical research gap by explaining the economics behind wind energy design choices.
Wind speed prediction has an important impact on the planning, economic operation and safe maintenance of wind power systems. However, the high volatility and intermittency of wind speed make it difficult to predict accurately. To improve the prediction accuracy, we developed a hybrid multistep wind speed prediction model named EWP‐CS‐RELM. In this model, a secondary decomposition technique of ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) is used, and it is called the EWP decomposition technique. This decomposition technique can achieve an adaptive processing of the data and accurately extract the characteristic components of the signal, avoiding the occurrence of pattern overlap and reducing the mutual interference between components. At the same time, the high and low‐frequency parts of the complex signal (component) can be decomposed into different frequency bands, and the corresponding frequency band can be selected adaptively to match the signal spectrum. The subsequence obtained after EWP decomposition is then predicted using a regularised extreme learning machine (RELM) optimised by the cuckoo search (CS) algorithm with strong global search ability to obtain the results. The hybrid prediction model is validated using four seasons of wind speed data from two wind farms in Shandong, China, and compared with seven other prediction models. Simulation results illustrate that the EWP‐CS‐RELM model outperforms the other seven models with the smallest statistical errors.
Wind turbines have shown significant advancements in efficiency and power output in the last 20 years. However, during the same time period, parallel advances in the manufacturing of wind blades have not happened due to the reluctance of wind blade manufacturers to make significant capital investments into unproven automation technologies. This reluctance is due in part to the lack of access to a robust techno‐economic model that can give feedback on if and how these investments will impact the overall cost to make a blade. In the current research, existing costing methods are reviewed and a new techno‐economic model for estimating the number of man‐hours required to manufacture a wind turbine blade is proposed. A set of standardized input parameters for the proposed model is developed from time studies completed at three wind blade manufacturing facilities. The proposed model uses 77 discrete processing steps. The credibility of the proposed model is validated by its ability the predict the required man‐hours for the manufacture of previously produced blades by the blade manufacturers. Additionally, validation is done against models of publicly available designs. The proposed model delivers more accurate and realistic estimates over prior models reflecting the application of current production techniques and detailed design information.
Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post‐process the ensembles. This post‐processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post‐processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post‐processing method and evaluate four possible strategies: only using the raw ensembles without post‐processing, a one‐step strategy where only the weather ensembles are post‐processed, a one‐step strategy where we only post‐process the power ensembles and a two‐step strategy where we post‐process both the weather and power ensembles. Results show that post‐processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post‐processing the weather ensembles does not necessarily lead to increased forecast performance.
Individual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignment, or wake impingement. This work presents a novel controller structure that relies on the separation of low‐level control tasks and high‐level ones. It is based on a neural network that modulates basic periodic pitch angle signals. This neural network is trained with reinforcement learning, a trial and error way of acquiring skills, in a low‐fidelity environment exempt from turbulence. The trained controller is further deployed in large eddy simulations to assess its performances in turbulent and waked flows. Results show that the method enables the neural network to learn how to reduce fatigue loads and to exploit that knowledge to complex turbulent flows. When compared to a state‐of‐the‐art individual pitch controller, the one introduced here presents similar load alleviation capacities at reasonable turbulence intensity levels, while displaying very smooth pitching commands by nature.
Wind turbines are exposed to the turbulent wind of the atmospheric boundary layer. Consequently, the aerodynamic forces acting on the rotor blades are highly complex. To improve the understanding, a common practice is the experimental or numerical investigation of 2d (wind turbine) blade sections. In these investigations, the flow around the 2d blade section is assumed to be two‐dimensional; however, 3d effects are known to occur. Therefore, we combine 2d CFD simulations and experimental investigations in a wind tunnel with a 2d wind turbine rotor blade section at full‐scale (i.e., chord length c=1.25m$$ c=1.25\kern0.1em \mathrm{m} $$ and chord‐based Reynolds number of Rec=4.7·106$$ R{e}_c=4.7\cdotp 1{0}^6 $$). In the wind tunnel, the inflow turbulence intensity is TI≈1.5%$$ TI\approx 1.5\% $$. To avoid wall effects biasing the results, the profile does not span the whole test section. The profile was equipped with two rows of pressure taps around the airfoil, close to the center, to monitor the time‐resolved aerodynamic response as well as the flow around the airfoil. The normal force, cp$$ {c}_p $$ curves, and the separation point are analyzed. While 2d simulations and experiments match well, in the experiments, we find natural instabilities, that is, local and temporal variations of the flow separation point at angles of attack close to the maximum lift that are not triggered externally, for example, by inflow variations.
As wind farms become larger, there is scope for improved operation via wind farm control. Further development of wind farm control would be facilitated by more flexible operation of wind farms and so by more flexible operation of wind turbines. A novel approach to wind farm control is proposed that provides full flexibility of both. It consists of a wind farm controller architecture and an interface to individual turbines. The design of a specific realisation of the interface, the Power Adjusting Controller, is presented that requires little information on the turbine dynamics or controller and does not compromise the operation of the wind turbine controller or the turbine's safety. Results from a DNV Bladed simulation of a 5MW wind turbine are presented to illustrate the behaviour of the Power Adjusting Controller and to confirm that it meets the requirements to enable fully flexible operation of wind turbines and, so of wind farms.
The wind‐turbine wake growth is crucial for wake assessment. At present, it can only be determined empirically, which is the primary source of prediction errors in the analytical wake model, and a physically‐based method is urgently needed. Recently, the plume model is proposed for wake width prediction in the neutral boundary layer based on Taylor's diffusion theory. However, this model is not applicable for high‐roughness neutral and strongly convective conditions, which is mainly related to the fact that the specified far wake point in the plume model is too close to the virtual wake origin. In this condition, the wake width prediction has evident convex function characteristics, which is inconsistent with the actual linear expansion of wake width. To this end, we propose a coupled model of the plume model and the traditional wake model (CPT model) to calculate the wake growth rate iteratively, thereby obtaining the wake width and velocity deficits in the far‐wake region. The average wake width prediction error decreases from 11.75% to 3.1% in these conditions. Since the wind‐turbine‐induced‐turbulence contribution is dominant in the wake recovery in the very stable boundary layer, both models have low but engineering acceptable prediction accuracy. Except for the above conditions, both the plume and CPT models can predict the wake width well, and their average wake width prediction errors are 2.5% and 1.9%, respectively. This implies that the proposed CPT model has higher prediction accuracy and a broader application range.
Marine operations required to transfer technicians and equipment represent a significant proportion of the total cost of offshore wind. The profile of sites being considered for floating offshore wind farms (FOWFs), e.g., further from the shore and in harsher environments, indicates that these costs need to be assessed by taking into account the maintenance requirements and restricted weather windows. There is an immediate need to investigate the potential use of robotic systems in the wind farm's operations and maintenance (O&M) activities, to reduce the need for costly manned visits. The use of robotic systems can be critical, not only to replace repetitive activities and bring down the levelised cost of energy but also to reduce the health and safety risks by supporting human operators in performing the desired inspections. This paper provides a review of the state of the art in the applications of robotics for O&M of FOWFs. Emerging technology trends and associated challenges and opportunities are highlighted, followed by an outline of the agenda for future research in this domain.
Global dynamic response models used for the design of wind turbines are largely based on neutral stability, which is not representative of real atmospheric conditions. Offshore wind farms, for example, have been seen to experience predominantly unstable conditions, especially at lower wind speeds. In the current work, we use four wind generation models under stable, neutral and unstable atmospheric stability conditions to study the low‐frequency content of the global responses of a semisubmersible floating wind turbine (FWT). To represent the wind fields, we use the Kaimal Spectrum and Exponential Coherence Model (Kaimal), the Mann Spectral Tensor Model (Mann), a point measurement based model (TIMESR) and large‐eddy simulation (LES). At the low‐frequency range, both atmospheric stability and the turbulence wind model significantly affect the response of the FWT. In all the cases studied throughout this paper, the structural response under unstable conditions is higher than under stable or neutral conditions. The TIMESR and the Kaimal models fitted to the FINO‐1 offshore meteorological mast measurements show more similar responses than the Mann model; surge and pitch are higher for the TIMESR and Kaimal models, and yaw is lower. When fitted to LES, TIMESR and Kaimal predict surge and pitch responses closer to LES, but they underestimate the responses related to yaw, opposite to what the Mann model does. The responses are directly related to turbulence intensity and coherence, which are affected by atmospheric stability. Therefore, based on the analyses carried out through this study, the structural analysis of FWT should account for the effect of atmospheric stability.
In wind turbine optimization, the standard power regulation strategy follows a constrained trajectory based on the maximum power coefficient. It can be updated automatically during the optimization process by solving a nested maximization problem at each iteration. We argue that this model does not take advantage of the load alleviation potential of the regulation strategy and additionally requires significant computational effort. An alternative approach is proposed, where the rotational speed and pitch angle control points for the entire operation range are set as design variables, changing the problem formulation from nested to one‐level. The nested and one‐level formulations are theoretically and numerically compared on different aerodynamic blade design optimization problems for AEP maximization. The aerodynamics are calculated with a steady‐state blade element momentum method. The one‐level approach increases the design freedom of the problem and allows introducing a secondary objective in the design of the regulation strategy. Numerical results indicate that a standard regulation strategy can still emerge from a one‐level optimization. Second, we illustrate that novel optimal regulation strategies can emerge from the one‐level optimization approach. This is demonstrated by adding a thrust penalty term and a constraint on the maximum thrust. A region of minimal thrust tracking and a peak‐shaving strategy appear automatically in the optimal design.
The wind resource assessment community has long had the goal of reducing the bias between wind plant pre‐construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long‐term corrected operational AEP (hereafter OA AEP) and is known as the P50 bias. The industry has critically lacked an independent analysis of bias investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants in North America and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project‐to‐project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants' estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.
Modern wind turbine blades experience tip speeds that can exceed 110 m/s. At such speeds, water droplet impacts can cause erosion of the leading edge, which can have a detrimental effect on the performance of the wind turbine blade. More specifically, rain erosion is leading to both reduced efficiency and increased repair costs. The industry is using polymeric coatings—leading‐edge protection (LEP) materials—to protect the blades but those are also prone to rain erosion. In this work, LEP materials that are currently used by the industry for the protection of wind turbine blades were selected and their performance assessed. The LEP materials were characterised in terms of mechanical properties by using different experimental methods, and they were also assessed in terms of durability by performing rain erosion testing (RET). Finally, the damage and failure mechanisms observed were further investigated using CT scanning. This paper provides an insight to the properties of LEP materials, their durability, and the damage and failure mechanisms they experienced during rain erosion.
All of the presented datasets can be grouped into one of five different categories. The two supergroups are wind power data and wind‐based data. The wind power datasets can be divided into the subgroups turbine‐level and aggregated data. Turbine‐level datasets contain measurements on turbine level and aggregated data are spatially aggregated on different levels from farm to country level. All of the turbine‐level datasets include wind and wind power measurements, SCADA data contain more variables than turbine‐level SCADA‐subset data. The wind‐based datasets contain either wind measurements or synthetic power data that is derived from numerical weather prediction models
Descriptive statistics of the presented datasets. The numbers in the brackets display the absolute size of each group respectively. In (A)–(D), all of the five groups are contained, (E) does not take the wind datasets into account
Country coverage of the datasets. Synthetic data can be simulated for all countries and locations given publicly available NWP data. For several countries turbine‐level data or aggregated data are accessible. Countries where at least one dataset of aggregated and turbine‐level data are accessible are colored in stripes
Overview of the spatial aggregation and information contained in the different datasets. The diameter of each circle scales linearly with the number of datasets associated with the respective category. The y‐axis is ordered with increasing information content, for example, datasets that contain control data always contain wind power, wind and weather data as well. While none of the aggregated datasets contains weather data, six of them contain location information that allows to incorporate weather variables for example from NWPs
Scatterplot that shows wind power on the y‐axis and wind speed on the axis. The plots show data from the first turbine of the dataset in Beberibe and Pedra do Sal³⁶ respectively. It can be seen that the mapping from wind speed to wind power is not deterministic
Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open‐source datasets, as many currently used datasets are non‐disclosed and make reproducibility of research impossible. This unavailability of open‐source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper, we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up‐to‐date overview of existing open‐source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open‐source datasets and compare their methods on them.
The cover image is based on the Research Article Virtual shaft‐based synchronous analysis for bearing damage detection and its application in wind turbines by Baoqiang Zhang et al., https://doi.org/10.1002/we.2727.
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Top-cited authors
R. J. Barthelmie
  • Cornell University
Niels Nørmark Sørensen
  • Technical University of Denmark
Jens Sørensen
  • Technical University of Denmark
Ervin Bossanyi
  • Det Norske Veritas
Kurt Schaldemose Hansen
  • Technical University of Denmark