A model of the evolution of the onshore wind turbine blade mass installed in Denmark is proposed described by a Weibull distribution and the age of the blades is estimated from decommissioning data to t50 %,onshore = 29 years when half of the blade mass of an installation year has been decommissioned. This is considerable longer than the 20 year design life time of onshore turbines, which is often assumed to be an estimate of the End-of-Life of turbine blades. Thus blade waste predictions using the simple assumption may predict that installed blade masses are entering recycling processes about 9 years sooner that what is observed in Denmark. The blade mass for decommissioning in Denmark is estimate to peak at 2000 ton/year and 5000 ton/year in 2028 and 2045 using the Weibull model.

Leading edge erosion of wind turbine blades is one of the most critical issues in wind energy production, resulting in lower efficiency, as well as increased maintenance costs and downtime. Erosion is initiated by impacts from rain droplets and other atmospheric particles, so to protect the blades special protective coatings are applied to increase their lifetime without adding significantly to the weight or friction of the blade. These coatings should ideally absorb and distribute the force away from the point of impact, however, microscopic defects, such as bubbles, reduce the mechanical performance of the coating, leading to cracks and eventually erosion. In this work, Optical Coherence Tomography (OCT) is investigated for non-destructive, contactless inspection of coated glass-fiber composite samples to identify subsurface coating defects. The samples were tested using rubber projectiles to simulate rain droplet and particle impacts. The samples were subsequently imaged using both OCT, optical microscopy, and X-ray tomography. OCT scanning revealed both bubbles and cracks below the surface, which would not have been detected using ultrasonic or similar non-destructive methods. In this way, OCT can complement the existing quality control in turbine blade manufacturing, help improve the blade lifetime, and reduce the environmental impact from erosion.

The Norwegian government recently agreed on the goal 30by40 which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040 (Regjeringen, 2022).We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi-criteria decision analysis framework (MCDA), including an analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno-economic aspects, social acceptance, environmental considerations, and met-ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criteria importance are pair-vise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criteria importance we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent actors with distinct preferences for siting of wind farms: the investor, the environmentalist, and the fisherman. The results show that the southern part of the NEZ is the most suitable region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near-coastal areas outside mid-Norway are also well suited regions, but these are more sensitive to the choice of criteria importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.

Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made. Results indicate that CEMPC-IPC can achieve better reduction in rotating blade loads, as well as similar performance in the mitigation of shaft and yaw bearing loads, with the added advantage of convenient economic objectives trade-off tuning.

The filtered lifting line theory is an analytical approach to solving the equations of flow subjected to body forces with a Gaussian distribution, such as used in the actuator line model. In the original formulation 1, the changes in chord length along the blade were assumed to be small. This assumption can lead to errors in the induced velocities predicted by the theory compared to full solutions of the equations. In this work, we revisit the original derivation and provide a more general formulation, that can account for significant changes in chord along the blade. The revised formulation allows for applications to wings with significant changes in chord along the span, such as wind turbine blades.

In this paper, 3D reconstruction of the leading edge of wind turbine blades from images, also known as photogrammetry, is investigated. The technique was applied to a decommissioned blade with an eroded leading edge, with images captured by a smartphone. The reconstructed 3D surface was accurate, when compared to the captured images, despite the small number of images used. A solid model was then created and imported into a finite element modelling software for rain-droplet impact simulations. Photogrammetry is relatively cheap, when compared to other surface scanning techniques, and provides means of monitoring erosion damage when combined with drone imaging. Further investigations of the parameters which control the accuracy of the reconstructed surface must be performed, to fully explore the potential of photogrammetry for 3D reconstruction of leading edge erosion damage.

Accurate assessment of wind energy potential can provide important implication regarding the optimalization of micro-siting of wind turbines and increase of wind power generation. It is, however, noteworthy that most previous studies on wind energy resource assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In the current study, a copula-based method is proposed to better characterize the direction-related wind energy potential at six typical sites in Hong Kong. In the first step, several widely used statistical models are adopted to fit the marginal distributions of wind speed and direction. The joint probability density function (JPDF) of wind speed and wind direction is therewith constructed by various copula models. The goodness-of-fit evaluation indicates that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites, while Gumbel copula outperforms other models at downtown sites. More importantly, the derived JPDFs are applied to estimate the direction-related wind power density at each of the considered sites, finding a maximum value of wind energy potential of 506.4 W/m2 at a hilltop site. In addition, site-to-site variability is also identified regarding the prevailing wind resource directions. The outcome of this study is expected to be useful for the site selection of wind turbines, as well as the strategic development of wind energy in Hong Kong. Notably, the proposed copula-based method can also be applied to characterize the direction-related wind energy potential somewhere other than Hong Kong.

Bearing failure in wind turbine gearboxes is one of the significant sources of downtime. While it is well known that bearing failures cause the largest downtime, the failure cause(s) is often elusive. The bearings are designed to satisfy their Rolling Contact Fatigue (RCF) life. However, they often undergo sudden and rapid failure within a few years of operation. It is well known that these premature failures are attributed to different types of surface damage. In that regard, transient torque reversals (TTRs) in the drivetrain have emerged as one of the primary triggers of surface damage, as explained in this paper. The risk associated with TTRs motivates the need to mitigate TTRs arising in the drivetrain due to various transient events. This paper investigates three TTR mitigation methods. First, two existing devices, namely, the torsional tuned mass damper and the asymmetric torque limiter, are studied. Then, a novel idea of open-loop high-speed shaft mechanical brake control is proposed. The results show that while the torsional tuned mass damper and the asymmetric torque limiter can improve the torsional vibration characteristics of the drivetrain, they cannot mitigate TTRs in terms of eliminating the bearing slip risk associated with TTRs. However, the novel approach proposed here can mitigate TTRs both in terms of improving the torque characteristic in the high-speed shaft and reducing the risk of bearing slip. Furthermore, the control method is capable of mitigating TTRs with the mechanical limitations of a pneumatic actuator in terms of bandwidth and initial dead time.

Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine; and (2) by changing the overall flow resistance in the farm and thus the so-called farm blockage effect. To better predict these effects with low computational costs, we develop data-driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient CT* as a function of turbine layout. We train the model using a multi-fidelity Gaussian Process (GP) regression with a combination of low (engineering wake model) and high-fidelity (Large-Eddy Simulations) simulations of farms with different layouts and wind directions. A large set of low-fidelity data speeds up the learning process and the high-fidelity data ensures a high accuracy. The trained multi-fidelity GP model is shown to give more accurate predictions of CT* compared to a standard (single-fidelity) GP regression applied only to a limited set of high-fidelity data. We also use the multi-fidelity GP model of CT* with the two-scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimisation in the future.

Small-scale horizontal axis wind-turbines (SHAWTs) are acquiring relevance within the regulatory policies of the wind sector aiming at net-zero-emissions, while reducing visual and environmental impact by means of distributed grids. SHAWTs operate transitionally, at Reynolds numbers that fall between 10^5 < Re < 5·10^5. Furthermore, environmental turbulence and roughness affect the energetic outcome of the turbines. In this study, the combined effect of turbulence and roughness is analysed via wind tunnel experiments upon a transitionally-operating NACA0021 airfoil. The combined effects cause a negative synergy, inducing higher drops in lift and efficiency values than when considering the perturbing agents individually. Besides, such losses are Reynolds-dependent, with higher numbers increasing the difference between clean and real configurations, reaching efficiency decrements above 60% in the worst-case scenario. Thus, these experimental measurements are employed for obtaining the power curves and estimating the annual energy production (AEP) of a 7.8 kW-rated SHAWT design by means of a BEM code. The simulations show a worst-case scenario in which the AEP reduces above 70% when compared to the baseline configuration, with such a loss getting attenuated when a pitch-regulated control is assumed. These results highlight the relevance of performing tests that consider the joint effect of turbulence and roughness.

For many design applications in offshore engineering, including offshore wind turbine foundations, engineers need accurate statistics for kinematic and dynamic quantities, such as hydrodynamic forces, whose statistics depend on the stochastic sea surface elevation. Nonlinear phenomena in the wave--structure interaction require high-fidelity simulations to be analyzed accurately. However, accurate quantification of statistics requires a massive number of simulations, and the computational cost is prohibitively expensive. To avoid that cost, this study presents a machine learning framework to develop a reliable surrogate model that minimizes the need for computationally expensive numerical simulations, which is implemented for the monopile foundation of an offshore wind turbine. This framework consists of two parts. The first focuses on dimensionality reduction of stochastic irregular wave episodes and the resulting hydrodynamic force time series. The second of the framework focuses on the development of a Gaussian process regression surrogate model which learns a mapping between the wave episode and the force-on-structure. This surrogate uses a Bayesian active learning method that sequentially samples the wave episodes likely to contribute to the accurate prediction of extreme hydrodynamic forces in order to design subsequent CFD numerical simulations. Additionally, the study implements a spectrum transfer technique to combine CFD results from quiescent and extreme waves. The principal advantage of this framework is that the trained surrogate model is orders of magnitude faster to evaluate than the classical modeling methods, while built-in uncertainty quantification capabilities allows for efficient sampling of the parameter using with the CFD tools traditionally employed.

{The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre-computed quasi-steady aerodynamic mapping, which describes the relationship between pitch angle and tip-speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterised in terms of axial induction factors. Based on this insight and standard turbine measurements, real-time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine drive-train model and an extended Kalman filter. In addition, the actuator disc model can be updated easily over time by calibrating solely two correction factors. Thus, the proposed algorithm presents an alternative for estimating the rotor effective wind speed, which is valuable for numerous applications, for example, LiDAR-assisted control and coherence studies.

Short-term wind speed prediction is essential for economical wind power utilization. The real-world wind speed data is typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multi-step wind speed prediction, namely LR-FFT-RP-MLP/LSTM (Linear Fast Fourier Transformation Rank Pooling Multiple-Layer Perception/Long Short-Term Memory). Our hybrid model processes the local and global input features simultaneously. We leverage Rank Pooling (RP) for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit Fast Fourier Transformation (FFT) to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are respectively integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared to state-of-the-art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.