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.

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.

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%.

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.

Vortex generators (VGs) have been widely applied to wind turbines thanks to their potential to increase aerodynamic performance. Due to the complex inflow perceived by a rotor and the proneness to flow separation, VGs on wind turbines usually experience highly unsteady flow. While there are models that exist to simulate the steady effects of VGs, we lack a fast and efficient tool to model the unsteady performance of airfoils equipped with VGs. This paper adopts an unsteady double-wake panel model with viscous-inviscid interaction developed to simulate a vertical axis turbine in dynamic stall, adding the capability of predicting the dynamic aerodynamic performance of VG-equipped airfoils. The results of a series of steady and unsteady cases of an airfoil with different VG configurations in various pitch motions in free and forced transition are verified against experimental data. Results show that the double wake model offers sufficient accuracy results compared to experimental data to claim the model’s validity in a preliminary evaluation of an airfoil’s capability to prevent stall with VGs. While a few limitations are still identified for improvement, the model’s accuracy in predicting the transition location, separation and reattachment, and drag forces into future development.