Quantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors’ previous theoretical and numerical studies showed that compared to damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore-aft (FA) and side-side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and different turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5 MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady-state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice.

The airfoil DU91-W2-150 was investigated in the Low Speed Low Turbulence Tunnel at the Delft University of Technology to study unsteady aerodynamics. This experimental study tested the airfoil under a wide range of angles of attack (AoA) from 0 ◦ to 3 1 0 ◦ at three Reynolds numbers ( Re) from 2 × 1 0 5 to 8 × 1 0 5 . Pressure on the airfoil surface was measured and Particle Image Velocimetry (PIV) measurements were conducted to capture the flow field in the wake. By examining the force coefficient and comparing the wake contours, it shows that an upwind concave surface provides a higher load compared to a convex surface upwind case, highlighting the critical role of surface shape in aerodynamics. When comparing separation at specific locations along the chord for all three Res, it is observed that as Re increases, separation tends to occur at lower angles of attack, both for positive stall and negative stall. The examination of the aerodynamic force variation indicates that, during reverse flow, fluctuations are more pronounced compared to forward flow. This is owing to separation occurring at the aerodynamic leading edge (geometric trailing edge) in reverse flow. In terms of vortex shedding frequency, the study found a nearly constant normalized Strouhal number ( St) of 0.16 across various Res and AoAs in fully separated regions, indicating a consistent pattern under these conditions. However, a slight increase in St, between 0.16 and 0.20, was observed for AoAs exceeding 180 degrees, possibly due to the convex curvature of the airfoil in the upwind direction. In conclusion, this research not only corroborates previous findings for small AoAs, but also adds new data on the aerodynamic behavior of the DU91-W2-150 airfoil under large AoAs, offering various perspectives on the effects of surface curvature, Re, and flow conditions on key aerodynamic parameters.

The monitoring of wind turbine blade root load (BRL) has always been a valuable yet highly challenging problem. BRL monitoring is often measured directly using strain sensors, which is expensive to install and poses safety risks. Thus, it is necessary to explore an indirect measurement method that establishes a mapping relationship between BRL and easily measurable data. This paper presents a non-intrusive BRL monitoring method based on the vibrational signals at bearings (including main bearing, gearbox bearing, and generator bearing) and a new proposed AE-LSTM-Attention neural network. The proposed neural network incorporates autoencoder (AE) pre-training process, long short-term memory (LSTM) neural network, and channel attention mechanism to overcome difficulties in extracting and fusing long-term, high-frequency, and multi-channel signal features. Additionally, considering the wind turbine transmission mechanism, an optimal LSTM step number selection criterion is proposed. The wind turbine’s physical feature is innovatively bridged with the neural network training parameters, e.g., setting the transmission ratio of the gearbox as the number of LSTM steps, by which the most cost-effective prediction performance can be achieved. Experiments were conducted using data from actual operating wind turbines, and the results show that the proposed method can successfully achieve non-intrusive monitoring of BRL characteristics and outperform traditional methods in terms of predictive performance. The mapping relationship between wind turbine vibration signals and BRL characteristics can be established efficiently and accurately.

not-yet-known not-yet-known not-yet-known unknown With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.

A coupled medium-fidelity drivetrain model is developed and implemented in OpenFAST for a 10-MW land-based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost, useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is extended to a monopile offshore wind turbine and used to investigate sensitivity of predicted main bearing rolling contact fatigue to different synthetic turbulence models. Large eddy simulations (LES) intentionally targeting stable, neutral, and unstable atmospheric conditions at below-, near- and above-rated wind speeds, were used as a reference. The turbulence models recommended by IEC, the Mann spectral tensor model and the Kaimal spectral model with exponential coherence, were fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb, based on LES data, was applied in the aero-hydro-servo-elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10 and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue with up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

A versatile and widely used tool in atmospheric research and operational forecasting is the Weather Research and Forecasting (WRF) model. The WRF model utilizes advanced algorithms and physics parameterizations to simulate complicated atmospheric phenomena. However, this model relies on a configuration that encompasses spin-up time, different Planetary Boundary Layer (PBL), and Land Surface Model (LSM) schemes to describe the physics near the surface. This work aims to optimize the configuration of the WRF model by evaluating the impact of the spin-up period, PBL, and LSM schemes to obtain the best representation of the wind field to calculate the power production of a wind farm located in the complex terrain of La Rumorosa, Baja California, Mexico. This site was chosen because of the availability of data, the challenging conditions of the terrain complexity, and severe changes in the wind direction. Results indicate that one day spin-up is the best option to predict the wind speed for one month or even one year. Moreover, the best statistical metrics are obtained when the Mellor-Yamada-Nakanishi–Niino Level 2.5 (MYNN2) PBL scheme is coupled with Noah-Multiphysics LSM. The best set-up for the WRF model was used to simulate the performance of a wind turbine in the farm with excellent results.

Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR-assisted pitch control. This approach uses wind velocity measurements from a nacelle-mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs), and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC+FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR-assisted pitch control may allow for an up to 5% reduction in OpEx, increasing up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR-assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR-assisted pitch control.

Wind energy is one of the main renewable energy sources in the current energy transition. Due to ever more and ever larger wind turbines (WT), the requirements for WT operation become more complex. Model predictive control (MPC) for WTs shows the potential to handle these requirements and conflicting control objectives in a single optimization-based controller. Recent research has widely investigated MPC for WT in simulation, but mostly lacks experimental validation. This work aims to experimentally validate MPC on a full-scale WT under real conditions. To this end, we combine an Extendend Kalman Filter for nonlinear state estimation with robust linear time-varying MPC. We evaluate the proposed control algorithm in terms of time-domain performance and power curve in simulation. However, the main contribution of this work is the experimental validation on a 3MW WT in Northern Germany with a total duration of 3h continuous full access of the controller. We were able to demonstrate stable operation of the proposed MPC in the upper partial load regime, transition regime and lower full load regime, at measured wind speeds between 4.76m/s and 13.06m/s, inside and outside the wake shadow of another WT. The power curve determined in simulation shows comparable results to a reference feedback controller. The MPC formulation combines several control objectives in a single optimization problem, yet the tuning effort still remains complex. In future work, we plan to reduce the complexity of the control loop based on this experimentally validated MPC. We provide our experimental data at https://github.com/rwth-irt/MPC_WT_experiment.

This study presents results from a long-term measurement campaign on a research wind turbine in the field. Pressure measurements are conducted at 25% blade radius over several months. Together with inflow measurements provided by a LiDAR system, they form an extensive dataset which is used in the validation of numerical aerodynamic models.  The model validation is conducted based on both ten-minute average data as well as time-resolved unsteady data. Initially, it is investigated how representative ten-minute average pressure measurements are of the underlying unsteady aerodynamics. Binned ten-minute average pressure distributions are then analysed together with their numerical counterpart, consisting of a combination of rotor and airfoil level aerodynamic/aeroelastic simulation results using average environmental and operating conditions as input. Finally, time-resolved measurements and simulation results are compared, validating the aeroelastic tools’ capability to reproduce unsteady aerodynamics.  Overall, reasonable agreement is found between numerical simulations and field experiment data showcasing two aspects: Low-fidelity numerical tools remain relevant for simulating modern multi-megawatt wind turbines and long-term pressure measurements provide invaluable means for validating such tools.

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition’s expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in non-dimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. We find that FLOWERS predicts AEP within about 10% of the baseline AEP models in less than 10% of the computational time. Furthermore, we illustrate how the FLOWERS design space at relatively low resolution is qualitatively similar to the reference and yields comparable optimal layouts.

As a commodity, wind energy is typically traded in ex-ante time frames and is dependent on forecasts to secure physical positions up to 36 hours after gate closure for trading in Day-Ahead markets. Wind energy is traded in discrete quantities, however it is generated from an intermittent and variable resource. Deterministic forecasts are preferred for energy trading as the most compatible solution to provide a defined forecast quantity. However, deterministic forecasts cannot capture the stochastic nature of the underlying power source and are therefore sub-optimal. Ensemble based forecasts have the potential to reduce forecast error by accounting for uncertainties not captured in deterministic models. However, ensemble forecasts are not always available at the vertical levels at which wind turbines operate. Therefore, a method is needed to apply ensemble information to turbine hub heights for energy forecasting purposes. This paper presents a novel machine learning based method that translates the perturbations from a localised Numerical Weather Prediction model’s 10m wind speed component to an ensemble energy forecast at 100m. The extrapolated ensemble based forecast has improved the forecast accuracy by 9% when compared to the deterministic output. The findings will have important implications for future energy trading, transmission system operation and meteorological forecasting.

The Additively-Manufactured, System-Integrated Tip (AMSIT) project is leveraging the flexibility of 3D printing to integrate several technologies in a wind turbine blade tip, while reducing the levelized cost of electricity (LCOE) produced. The design integration is demonstrated for a 200 kilowatt-scale turbine with 13-meter blades, with the outer 15% of the blade replaced with a 3D-printed design. Aerodynamic performance is enhanced through inclusion of a winglet and surface texturing, both challenging for traditional manufacturing. Longevity and durability is improved through integrated lightning and leading edge erosion protection. Increased power, reduced repair frequency, and ease of repair through blade modularity all contribute to reduced LCOE. Cost models are are extended to modern megawatt-scale designs to estimate the impact of the technology at scale, demonstrating the potential to reduce LCOE very significantly for modern onshore turbines, with even higher potential savings offshore.

In efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: firstly, a set of non-intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Secondly, a physics-based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an Augmented Extended Kalman Filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a Normalized Mean Absolute Error (NMAE) between 3 .41% to 7 .47% , depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Condition monitoring and evaluation of wind turbine drivetrain hold great importance. However, the implementation of real-time monitoring often faces challenges in efficiency and accuracy, as the drivetrain typically operates under harsh conditions. In order to resolve this, this paper proposes a vibration-based damage monitoring digital twin (VBDM-DT) that enables the online intelligent evaluation of wind turbine drivetrain. The VBDM-DT integrates a random wind load model, a high-fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM-DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. And the fatigue damage model takes the real-time dynamic load estimated by the high-fidelity dynamics model as input to realize real-time fatigue damage monitoring of key components of the drivetrain. The VBDM-DT model is applied to a 2 MW wind turbine drivetrain to verify the effectiveness of the proposed method. In addition, a visualization platform is developed to vividly and intuitively display the real-time operating information, dynamic loads, and damage levels of the key components of wind turbine.

Recent studies have revealed the large potential of vertical-axis wind turbines (VAWTs) for high-energy-density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof-of-concept for the wake recovery mechanism of the novel X-Rotor VAWT. The phase-locked flowfield is measured at several streamwise locations along the X-Rotor’s wake using stereoscopic particle image velocimetry (PIV) with fixed-pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X-Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H-type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip-vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes.

The actuator line method (ALM) is a widely-used tool for the modelling of horizontal axis turbines and wind and tidal farm flows. The method uses a virtual blade representation to simulate the dynamics of wind turbines without the computational expense associated with resolving the blade geometry. Within the ALM the flow is first sampled at each blade section, allowing for the calculation of the sectional lift and drag forces that are in turn imposed on the flow domain using a smearing kernel. In this work the effect of the flow sampling method on the robustness of the ALM is discussed. Implementations of two widely-used types of methods for horizontal axis wind turbines are tested: point sampling, where the flow is sampled at or near the collocation point, and volume average sampling where flow field information from multiple points centered at the collocation point is averaged into a representative blade-local velocity vector. A third method, line average sampling, that was initially proposed for sampling flows from blade resolved simulations is adapted for the ALM framework. This method samples the flow symmetrically around a control element in order to eliminate the interference of the bound circulation, thus allowing the inflow velocity at the aerofoil to be determined. When evaluating power and thrust coefficients as well as the flow field at the rotor plane predicted by the ALM, the line average sampling approach is demonstrated to be more robust and converges faster with time step for two different rotors: a small wind rotor and a high solidity tidal rotor. This suggests that carefully selecting the sampling method could be key in alleviating the very strict time step restriction imposed by the ALM, a widely acknowledged limitation of the method. Such an advancement could contribute towards improving the computational efficiency and tractability of the method.

The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing nearest neighbors imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.

Previous numerical studies suggested the motions of Floating Offshore Wind Turbines (FOWTs) may enhance their wake recovery rates due to having different modes of wake dynamics from the bottom-mounted counterparts. However, the majority of previous research were conducted with models having relatively low fidelities and/or focusing on laminar inflow conditions. Models with lower fidelities are not able to capture the dynamics of tip-vorticies reliably while inflow conditions without turbulence are unrealistic out in the fields. In light of this, this paper performed high fidelity numerical simulations (large eddy simulation with actuator line technique) using full scale surging (prescribed and harmonic) FOWT rotor with different inflow turbulence intensities and multiple surging settings systematically to better understand the wake dynamics of FOWT. The results showed that the differences of wake structures between fixed and (harmonic) surging rotors were pronounced when under laminar inflow conditions, where the Surging Induced Periodic Coherent Structures (SIPCS) could be detected straightforwardly; while the differences were much less significant when under inflow conditions with realistic turbulence intensities, and the SIPCS were clearly revealed only after phase-locked averaging. Moreover, when under laminar inflow conditions, the values of mean disk-averaged streamwise velocity at x/D=8 could be above 30% larger for the surging cases than the fixed case, while the increases were down to around 0 .5∼2% when under inflow conditions with realistic turbulence intensities.