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.

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.

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.

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 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.

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.

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.