A main bearing (MB) preforms the critical role of supporting the rotor and whole drive train in a wind turbine. It endures very heavy and complex load coming from the rotor, meanwhile, high reliability in whole operation life is required for the high cost of MB replacement. Taking a spherical roller bearing (SRB) MB as an example, the paper analyzed complex load characters of a SRB MB configuration and discussed how to simplify the complex load in MB design works, then the key aspects influencing the MB performance were discussed, including raceway performance and shrink fit. Further more, thresholds of raceway contact pressure occurrence, fitting contact pressure and hoop stress were presented based on the state of art experiences. The paper gives an overview of SRB MB design for field operation performance.

Computationally efficient frequency-domain models can play a very important role in facilitating conceptual design optimization of floating wind turbines (FWTs). However, achieving sufficient accuracy in such models is challenging due to the nonlinear variation of the aerodynamic loads, particularly the interaction between the floating platform motions and the controller. Building on previously proposed approaches from the literature, this work implements and improves upon three methods to evaluate the influence of rotor dynamics on FWTs dynamics in frequency domain. The investigated methods rely on: coupled fixed-nacelle simulations in turbulent wind; decay tests in steady wind; and linearized analytical expressions of the steady state aerodynamic loads. The main objective is to assess the suitability of these methods for future optimization of the floating platform and the mooring system. The various techniques are compared through a case study of three semi-submersible FWTs with increasing rotor size. While all approaches have good accuracy below-rated wind speed, only the decay test approach provide good estimates of the wind-induced global responses across all tested conditions.

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.

Low level jets (LLJs) describe conditions in which the wind speed reaches a local maximum with respect to altitude near the surface, and have been observed intermittently in the US Mid-Atlantic offshore environment. LLJs pose unique operating conditions for future wind turbines operating in the region, presenting negative shear and locally strong veer, but they are not typically considered in existing turbine standards. This work builds upon recent research that explains the formation and evolution of U.S. Mid-Atlantic LLJs through a simple analytical governing equation. We generate several LLJ inflow conditions with varying jet characteristics based on this analytical model and create monotonically-sheared (MS) analogues with constant veer in order to assess the impacts of the LLJ on turbine performance and loading. Using aeroelastic simulations with these inflow conditions on the IEA 15MW reference turbine, we find that the LLJ leads to a greater range of tower top pitching and yawing moments, which could contribute to larger accumulated structural fatigue in components compared to monotonically-sheared inflow. These preliminary results demonstrate a path toward a unified set of test cases for low-level wind maxima that can inform International Electrotechnical Commission standards related to offshore wind turbine design.

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.

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.

One of key factors in simulating offshore wind field is con- sidering the effects of waves on momentum and energy ex- changes between the atmosphere and ocean. However, most NWP models traditionally overlook the effects of waves, due to the lack of suitable roughness length parameterization. To address this issue, coupling atmospheric and wave mod- els has emerged which handles the wind-wave interaction through the exchange of a few quantities between two mod- els. On the other hand, integrating atmospheric and wave models requires substantial computational resources and as- sociated expenses. In this study, we developed an offline wave coupling method in the WRF to account for wave ef- fects on wind field simulations. With this method, the WRF is modified to read wave parameters and bathymetry data, and to parameterize roughness length using these data. Our study aims to evaluate the offline method by comparing it to an online coupling system that uses the coupled atmosphere and wave models, as well as observation data. We found that the offline method utilizes almost 17% fewer computational resources and 19% less computational time compared to the online method, while maintaining identical forecasting accu- racy. Evaluation of the results during dominant swell con- dition suggested that both methods are able to reduce the forecast uncertainty compared to LiDAR measurement spe- cially in the lower part of atmosphere.

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.

Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. Due to the intermittency of wind power and its rapidly increasing share in the total energy mix, accurate forecasting of wind farm power production becomes increasingly important. This paper presents a data-driven methodology for forecasting power production and wake losses of wind farms, taking the dynamics of weather conditions into account. A modular approach is adopted by integrating multiple deep neural networks, resulting in a digital twin of the wind farm that can be interfaced with weather forecasts of different meteorological service providers. Another key advantage of the employed data-driven approach is its high prediction speed compared to physics-based methods, such that it can be employed for applications where real-time power forecasting is required. The methodology has been applied to two large offshore wind farms located within the Belgian-Dutch wind farm cluster in the North Sea.

Satellite SAR provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of Synthetic Aperture Radar (SAR) satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed surplus is generated, measured in 48% of the 67 images available. The results compare well to Sentinel-1 observations, showing a 34% surplus rate during several years based on 1171 images. Three wind speed surplus cases have been studied in detail using the mesoscale Weather, Research, and Forecasting model (WRF) model with two wind farm parametrizations. The SAR-based observations and WRF model compare for most cases, though only when Turbulent Kinetic Energy (TKE) is included in the wind farm parametrization. The TKE mixes higher momentum downwards in a stable atmosphere, causing surface wind speed surplus near the surface.

Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes and a lack of lifetime reserve have made continuous fatigue life monitoring become an operational concern. In this contribution we discuss a data-driven methodology for farm-wide tower-transition piece fatigue load estimation. We specifically tackle the employment of this methodology in a real-world farm-wide setting and the implications of continuous monitoring. With reliable nacelle-installed accelerometer data at all locations, along with the customary ten-minute SCADA statistics and three strain gauge-instrumented ’fleet-leaders’ we discuss the value of two distinct approaches: use of fleet-leader or population-based data for training a physics-guided neural network model with a built-in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data is missing, then its fatigue loads should be modelled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of nine months, with monthly accumulated errors always kept below ±5% . A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of ten-minute statistics, but mitigation strategies have been identified. Finally, the farm-wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behaviour we correlated with the operational conditions. Finally, the continuous data-driven, population-based approach here presented can serve as a springboard for further lifetime-based decision-making.

Nacelle-mounted, forward-facing LIDAR technology is able to deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines when assisting with blade pitch control in above-rated wind speed conditions. Large-scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor-swept area. These loading fluctuations can be mitigated through the use of individual blade pitch control (IPC). This paper presents a novel LIDAR-assisted feedforward IPC approach that uses each blade’s rotor azimuth position to allocate an appropriate individual pitch command from a multi-beam LIDAR. In this computational study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR-assisted control. The LIDAR simulation modifications made are present in the latest OpenFAST release, v3.5. Simulations were performed on a single 15 MW floating offshore wind turbine across the above-rated wind spectrum and using multiple randomly generated wind profiles. Under a turbulent wind field with an average wind speed of 17 ms -1, the LIDAR-assisted feedforward IPC delivered root mean squared error and standard deviation reductions to key turbine and substructure parameters by up to 35%. Feedforward IPC delivered enhancements of up to 10% over feedforward collective pitch control, which instead provided the same feedforward command to all blades, compared to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimisation of the tower, while the reductions in the variations in the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.

High-fidelity numerical simulations of wind turbines are often performed through a computational fluid dynamics (CFD) model in which the wind turbine is parametrized using an actuator line model, as fully blade-resolved simulations are computationally expensive at the large Reynolds numbers involved. When applying such an actuator line approach to the modeling of wind turbines, the two main issues to consider are the free-stream velocity sampling and the choice of the regularization kernel used to project the computed body forces onto the domain. In this work, a novel velocity sampling method - the so-called effective velocity model (EVM) - is implemented in the CFD software SOWFA and assessed against pre-existing approaches. Results show superior method robustness with respect to the regularization kernel width choice while preserving acceptable accuracy.

Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models, of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared to normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re-energizing the wake. Measurements indicate that the breakdown of tip vortices, and the resulting destabilization of the wake is enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two turbine setup by as much as 15%.

This study presents a data-driven approach to extrapolate and predict Reynolds number effects on wind turbine airfoil polars. For this purpose a database is created using experimentally obtained aerodynamic coefficients from open literature for airfoils with thickness-to-chord ratio (t/c) values in the range of 15% to 30% in order to be more relevant to wind turbine blade design applications. All available airfoil geometries are parameterized using PARSEC methodology, and a Pareto analysis is performed to understand the sensitivity of maximum lift coefficient ( C l max ) and minimum drag coefficient ( C d min ) variations to geometrical inputs as well as to the Reynolds number using statistical tools. Based on this analysis, response surfaces are generated to predict C l max and C d min of a given airfoil operating at a given Reynolds number. These predicted values are then utilized in a power-law based estimation methodology to obtain predictions for full polars, which are then compared to experimental data for selected airfoil test cases. The results show that the response surfaces generated through the current data-driven approach as well as the full polar prediction methodology show better agreement with experimental results compared to those obtained using numerical simulations-based extrapolation schemes. For airfoil types that might be under-represented in the database (such as symmetrical airfoils), the prediction results can be erroneous, especially at very high Reynolds numbers that are not in the constructed database. In the power-law based polar prediction methodology, utilizing a reference Reynolds number that is closer to the target value provides more accurate predictions for both lift and drag polars.

Foundation design for offshore wind turbines (OWTs) is an iterative process due to load iterations. Foundation stiffness is required for aero-servo-hydro simulations that generate the turbine loads to be experienced by the foundation. Furthermore, foundation stiffness is dependent on its size and needs to be estimated prior to the simulations where simplified methods are often used for load estimations. This paper compares the foundation loads calculated for OWTs at mudline level using a simplified approach and OpenFAST simulations. Three OWTs, NREL 5MW, DTU 10MW and IEA 15MW are used as reference models. Extreme Turbulence Model wind load at rated wind speed combined with the 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load combined with the 1-year maximum wave height are used as the load combinations in this study. OpenFAST simulations are performed as an approach with higher fidelity, where the results are then compared with a simplified load estimation approach previously proposed. It is shown that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. Further, the pile penetration lengths calculated using the mudline loads using two methods are compared. The results show that the simplified approach can be effectively used during initial phases of the monopile foundation design by using factor as 1.5 and 2 for shear force and bending moment, respectively.

A scanning lidar measures a projection of the actual wind speed to the line-of-sight. Reconstruction of the original field is possible via various retrieval algorithms, especially if several lidars are deployed in the area. However, this may not be the case due to financial constraints. Additionally, conventional algorithms like Volume Velocity Processing (VVP) do not perform well for non-homogeneous flow fields, e.g., smooth wind turbine wakes in a retrieved field. A 2D-VAR algorithm using VVP as an intermediate step was suggested allowing the retrieval for consecutive scans at a low elevation angle. We validate this algorithm for scans containing prominent wakes under elevation angles within 5° using FINO1 mast and supervisory control and data acquisition (SCADA) data. We show that prominent wakes scanned at the hub height introduce substantial disturbances in the VVP retrieval output and affect the retrieved field. The unwanted disturbance may be overcome by masking the wakes. In point measurements, the wind speed magnitude is retrieved with an acceptable accuracy, but the wind direction appears to be sensitive to the weights and initial guess chosen. According to our sensitivity analysis, the weight assigned to the radial velocity residuals affects the outcome most, while other weights act as corrections.

Bolt looseness may occur on wind turbine (WT）blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing various blade sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts looseness, the response data of blade sensors were then obtained under different bolt looseness degrees. Afterwards, by analyzing the sensitivity of the response signal to the looseness of blade root bolts, the types and number of sensors that can judge the looseness of blade root bolts were determined. Finally, the multi-domain sensitive features of response signals were fused to construct a hybrid domain feature set of bolt looseness. The LightGBM classification algorithm was applied to identify different bolt loosening states for this hybrid domain feature data. The identification results of experimental data showed that the proposed method can accurately determine the loosening state of single or multiple WT blade root bolts.