Wind Turbine Loads Research Articles

Effects of Inflow Turbulent Coherent Structures on a Small-Scale Wind Turbine in the Atmospheric Boundary Layer of Long Corridor Terrains: An Example Study in the Hexi Corridor

Considering its Venturi effect, long corridor terrain is considered as one of the outstanding wind-energy resources, but has not been developed due to unclear inflow turbulence effects on wind turbines. To advance the wind-power technology in long corridors and reveal the physics behind it, we conducted a series of data-fusion analyses combining field measurements in the Hexi Corridor, Gansu, China, and numerical simulations and summarize the effort here. In this study, the classical spectrum for the atmospheric boundary layer for wind-energy research, the IEC Kaimal spectrum (also known as IECKwind, International Elector technical Commission 61400-1) cannot describe the wind profile in corridors. Here, we proposed a new spectrum to describe the wind in the Hexi Corridor by modifying the IEC Kaimal spectrum, and we call it the IECK-HX spectrum. The results demonstrate that it preferentially resides at lower frequencies (larger spatial scales) compared with IECKwind. Large-scale motions (LSMs) and very large-scale motions (VLSMs) coexist in the Hexi Corridor atmospheric boundary layer, which can be well described by the IECK-HX spectrum, and these two dominate the large fluctuations in the power, thrust, and blade root flapwise moment of wind turbines, while the high-frequency small-scale coherent structures mainly cause high-frequency fluctuations in wind-turbine load. Furthermore, IEC Kaimal spectrum can not describe the VLSMs, and the generated flow presents significant fluctuations and thereby reduces the power output in high wind-speed regions. This study provides fundamental support for wind-energy scientists and engineers who are interested in wind energy in corridor terrains. Published by the American Physical Society 2024

Identification of equivalent wind and wave loads for monopile-supported offshore wind turbines in operating condition

Wind and wave loads are critical to monopile-supported offshore wind turbines (OWTs) in determining whether OWTs are in safe conditions to generate electricity. However, these random and complex loads vary with time and location, and are difficult to measure directly. Theoretically, the loads can be identified based on structural responses. Nevertheless, it is challenging due to the complexity of external loads and the existence of harmonic loads generated by rotor rotation. This study proposes a new load identification framework using a limited amount of monitoring data to estimate the equivalent wind and wave loads of OWTs simultaneously. Both loads are modeled as Gaussian processes with exponential covariance function and incorporated into the augmented state-space model. Kalman filter is employed to identify the equivalent loads. The effectiveness and accuracy of the proposed method were validated based on a numerical OWT model and a scaled OWT test model. The results demonstrate that the framework can successfully identify equivalent wind and wave loads of OWTs in both parked and operating conditions. The probability distribution of identified load data is consistent with actual load data, with an error of less than 9.1 %. The proposed framework can find vast applications in operational management for OWTs.

Effects of multi-scale turbulent motions on aerodynamic performance of wind turbine under sand-laden conditions

Wind turbine installation in the desert and Gobi regions offers a promising approach for meeting long-term energy demands. However, the effect of multi-scale characteristics in sand-laden atmosphere flows on wind turbine aerodynamic performance has not been evaluated. In this study, wind velocity data collected from the Qingtu Lake Observation Array (QLOA) were employed to address this gap. Results show that up to 58% of the total turbulent kinetic energy (TKE) is accounted for by very large-scale motions (VLSMs), which make up a considerable portion of the TKE. The contributions of the large-scale motions (LSMs) and the small-scale motions (SSMs) to TKE are 36% and 6%, respectively. The contribution of multi-scale turbulent motions to the aerodynamic loads of wind turbine under sand-laden conditions has been quantified for the first time. The comparison demonstrates that while LSMs and SSMs exhibit a rapid drop in their contributions to wind turbine loads with height, VLSMs show a rapid increase. Wavelet analysis revealed a strong correlation between VLSMs and power, thrust, and blade root flapwise moment at periods ranging from 256 to 1024 s. This correlation weakens as the streamwise length scale of the turbulent coherent structure decreases. This study provides essential insights for optimizing wind turbine design and site selection in sand-laden environments.

Compensation Algorithm for Wind Turbine Main Shaft Loads Based on LSTM

Abstract For wind turbine development, load size is the core and critical factor. Currently, most companies employ Bladed software for load calculations, which enjoys widespread use in engineering applications due to its efficiency and ease of use. However, constrained by the limitations of blade element theory and momentum theory, there still exist discrepancies between the simulation results and actual conditions. This paper aims to combine simulation algorithms with the application of an LSTM (Long Short-Term Memory) neural network to develop a compensation algorithm for the main shaft loads of wind turbines. By doing so, it endeavors to realize real-time monitoring of the main shaft loads, thereby providing a foundation for achieving high-precision control in wind turbine systems.

Aerodynamic characteristics of wind turbines considering the inhomogeneity and periodic incentive of wake effects

In wind farms, the wake can lead to a loss of output power, and the uneven characteristics of wake can also exacerbate the fatigue load of downstream wind turbine (WT), affecting their operating life. To explore the impact of wake effects on downstream WT, this article proposes a new method for calculating the load of WT wake wind conditions and verifies it. Establishing a 3DJG wake model coupled with improved BEM for wind turbine aerodynamic performance calculation method. The influence of downstream WT operation phase angle and positions of 3D wake on the time-varying aerodynamic characteristics of downstream WTs was discussed from three perspectives: Blade elements on the spanwise, single blade and wind rotor. Results shown that: (1) For the blade element, calculated the shear force in the flapwise direction and the edgewise direction on each blade segment. The overall shear force of blade element in the vertical upward direction of WT blades is significantly greater than that in other directions. The non-uniformity of the wake is not sufficient to change the position of the maximum shear force on the blade. The position of the maximum shear force at each wake position is in the rear segments of the blade; (2) For the single blade, the most severe load fluctuation occurs when it is in the partial wake condition. The period of this fluctuation is 360°. When in full wake condition, the load fluctuation is relatively small, and due to the symmetry of the wake, the load fluctuation period is only 180°. The load standard deviation (SD) of the partial wake condition increases by 51 % compared to the full wake condition. (3) For the wind rotor, different wake positions have a weighty impact on the power loss of the WT rotor. Compared to the incoming shear wind, the power loss at the wake center can reach up to 54.22 %. At the wake boundary, the power loss of the WT is only 5.6 %. The non-uniformity of wake also has a periodic impact on the power output of the entire WT rotor, with the fluctuation period is 120°, and the fluctuation amplitude of no more than 2 %. This article provides a more intuitive quantitative analysis of the impact of wake on the time-varying aerodynamic characteristics of downstream WTs. It has certain reference value for the design strength verification of WTs, as well as the yaw strategy and layout design of wind farms.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Loads and fatigue characteristics assessment of wind farm based on dynamic wake meandering model

As the scale of wind farms continues to expand, the impact of the wake on the power generation and load of wind turbines is becoming increasingly prominent. This study delves into the wake characteristics, power generation, load, and fatigue behavior of an ideal layout wind farm using a dynamic wake meandering model, accounting for diverse spacing configurations, inflow conditions, and yaw angles. The findings reveal that increasing the axial spacing facilitates superior wake recovery, promoting a more evenly distributed load and reducing fatigue damage throughout the wind farm. Moreover, higher inflow wind speed or turbulence intensity exacerbates fatigue damage, especially at elevated wind, where the damage accumulates exponentially. Under yaw conditions, the wake deviates, reducing its adverse impact on downstream turbines and subsequently boosting the overall power generation of the wind farm. However, this deflection simultaneously introduces intricate and detrimental effects on the fatigue load. Notably, optimal yaw achieves a 7.9 % improvement in power generation, also resulting in the most significant fatigue damage at the blade root and tower base. This study provides theoretical and technical underpinnings for the layout optimization and control strategies of wind farms or wind farm clusters.

Two-Stage Robust Optimization of Integrated Energy Systems Considering Uncertainty in Carbon Source Load

Integrated Energy Systems (IESs) interconnect various energy networks to achieve coordinated planning and optimized operation among heterogeneous energy subsystems, making them a hot topic in current energy research. However, with the high integration of renewable energy sources, their fluctuation characteristics introduce uncertainties to the entire system, including the corresponding indirect carbon emissions from electricity. To address these issues, this paper constructs a two-stage, three-layer robust optimization operation model for IESs from day-ahead to intra-day. The model analyzes the uncertainties in carbon emission intensity at grid-connected nodes, as well as the uncertainty characteristics of photovoltaic, wind turbine, and cooling, heating, and electricity loads, expressed using polyhedral uncertainty sets. It standardizes the modeling of internal equipment in the IES, introduces carbon emission trading mechanisms, and constructs a low-carbon economic model, transforming the objective function and constraints into a compact form. The column-and-constraint generation algorithm is applied to transform the three-layer model into a single-layer main problem and a two-layer subproblem for iterative solution. The Karush–Kuhn–Tucker (KKT) condition is used to convert the two-layer subproblem into a linear programming model. A case study conducted on a park shows that while the introduction of uncertainty optimization increases system costs and carbon emissions compared to deterministic optimization, the scheduling strategy is more stable, significantly reducing the impact of uncertainties on the system. Moreover, the proposed strategy reduces total costs by 5.03% and carbon emissions by 1.25% compared to scenarios considering only source load uncertainty, fully verifying that the proposed method improves the economic and low-carbon performance of the system.

Anti-tropical cyclone load reduction control of wind turbines based on deep neural network yaw algorithm

Rapid changes in the wind field of tropical cyclones can cause excessive loads and threaten the safety of offshore wind turbines. This paper designs a wind turbine yaw optimization strategy based on the deep neural network to reduce the structural loads of wind turbines caused by tropical cyclones. Firstly, the high-precision tropical cyclone data is used to analyze the characteristics of the wind field. Then, a pseudo-Monte Carlo experiment is designed to compensate for the incompleteness of the observed data. Furthermore, a robust nonlinear coupling model between the wind characteristics and the loads of the wind turbine is constructed by a deep neural network, and the optimal yaw angle is searched in real time based on this model. The simulation results show that the proposed deep neural network model based on pseudo-Monte Carlo scenario generation can robustly calculate the structural loads of wind turbines with a calculation error of less than 8 %. After applying this model to the real-time optimization control loop, the corresponding optimized yaw angle can be obtained according to the operating data of the wind turbine under tropical cyclone conditions so that the structural loads of the wind turbine are reduced. Compared with the no-yaw and traditional yaw strategies, the load suppression effect is 1 %–9 % under different working conditions. The proposed data-driven structural load model and load suppression algorithm will effectively improve the operating safety of wind turbines under tropical cyclone conditions.

Effects of turbulence on the blade root load of the tandem wind turbine in neutral atmospheric boundary layer

Abstract A model characterizing a turbulent wind field, consistent with neutral atmospheric boundary layer attributes, was developed for a field-based experimental wind turbine using stochastic processes combined with shear flow profiles. By incorporating interactions of vertical thermal exchange, terrain-induced surface roughness, and Coriolis forces, the model simulates environmental impacts. A sequence of wind turbines situated within the atmospheric wind field underwent computational analysis leveraging Large Eddy Simulation (LES) alongside an Actuator Line Model (ALM) to examine atmospheric turbulence alterations both preceding and following the turbines. Analysis of the power spectra concerning wind velocity, turbulence intensity, and components of Reynolds stress upwind and downwind demonstrated the turbines’ blade effects on surrounding turbulence patterns. The research demonstrated that turbulence kinetic energy experienced enhancement in downwind turbines as a result of the upwind turbine’s impact, particularly evident in the lower-frequency spectrum. Augmenting the distance between turbines initially aids in the wake’s turbulent structure restoration, with a marked effect on tip vortices that hold greater amounts of high-frequency energy.

Analysis of performance improvement methods for offshore wind turbine blades affected by leading edge erosion

Leading edge erosion (LEE) poses greater challenges to the blade design of offshore wind turbines. This paper analyzes the influence of LEE on blade aerodynamic performance and wind turbine load based on high-power offshore wind turbines through the GH-Bladed computing platform. The study found that LEE severely weakens the blade aerodynamic performance, resulting in a lag in the wind turbine reaching its rated power. LEE expands the range of blade surface flow separation, leading to an increase in blade stall risk and a decrease in wind turbine load stability. The promotion of blade aerodynamic performance will cause an increase in blade force, which will strengthen the load level of both the blade and the wind turbine. The optimization of twist angle is an effective method to avoid the loss of power generation of wind turbines under LEE interference, but it is inevitable to cause a decrease in the load stability of wind turbines. Therefore, when the load safety margin of wind turbines is sufficient, adjusting the twist angle is an efficient technique to improve the blade aerodynamic performance and promote the stability of power output, which provides a novel solution for blade design of high-power offshore wind turbines.

Computational Fluid Dynamics in Wind Energy: Modeling and Optimization for Turbine Design

Renewable energy sources are hard to come by, but wind power has become one of the most popular options because it can make a lot of clean electricity. For wind energy to be used properly, however, turbines need to be designed in a way that takes into account the surrounding environment. To model and improve wind turbine systems, computational fluid dynamics (CFD) has become very important. It lets engineers simulate complicated fluid flow processes and improve turbine performance. When it comes to wind energy, this study mainly talks about how CFD can be used to model and improve rotor designs. Experts can learn more about the complicated fluid dynamics around wind turbines by using CFD models. For example, they can learn how the rotor blades interact with the wind flow moving in. Calculative fluid dynamics (CFD) makes it easier to guess things like power output, efficiency, and structure loads for wind turbines by accurately modeling turbulence, boundary layers, and other airflow effects. An important other topic this paper covers is how to make wind machine designs work better. To find the best setups for capturing energy while using the least amount of materials and spending the least amount of money on upkeep, engineers can combine computational fluid dynamics (CFD) with optimization methods. Blade shape, tower height, turning angle, and rotor speed are just some of the design factors that this optimization process looks at. We can make very efficient and cost-effective wind turbine systems that are perfect for each spot by using CFD-driven optimization methods and running models and analyses over and over again. For better confidence and accuracy in turbine design predictions, this study talks about how to combine CFD models with actual confirmation. Engineers can make sure that CFD models work in a wide range of situations by comparing modeling results with readings taken in the real world. It also helps to improve the models. This paper emphasizes how important CFD is to the progress of wind energy technology by giving a thorough look at its uses in designing turbines and their optimization, proof, and development. Potential for improving the efficiency and long-term viability of wind power generation stays high as long as more study and new ideas are put into CFD-driven methods.

Simulating low-frequency wind fluctuations

Abstract. Large-scale flow structures are vital in influencing the dynamic response of floating wind turbines and wake meandering behind large offshore wind turbines. It is imperative that we develop an inflow wind turbulence model capable of replicating the large-scale and low-frequency wind fluctuations occurring in the marine atmosphere since the current turbulence models do not account well for this phenomenon. Here, we present a method to simulate low-frequency wind fluctuations. This method employs the two-dimensional (2D) spectral tensor for low-frequency, anisotropic wind fluctuations presented by Syed and Mann (2024) to generate stochastic wind fields. The simulation method generates large-scale 2D spatial wind fields for the longitudinal u and lateral v wind components, which can be converted into a frequency domain using Taylor's frozen turbulence hypothesis. The low-frequency wind turbulence is assumed to be independent of the high-frequency turbulence; thus, a broad spectral representation can be obtained just by superposing the two turbulent wind fields. The method is tested by comparing the simulated and theoretical spectra and co-coherences of the combined low- and high-frequency fluctuations. Furthermore, the low-frequency wind fluctuations can also be subjected to anisotropy. The resulting wind fields from this method can be used to analyze the impact of low-frequency wind fluctuations on wind turbine loads and dynamic response and to study the wake meandering behind large offshore wind farms.

Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

Integrating COAWST and OpenFAST for wind turbine loading

Modern research endeavors in wind energy have been increasingly focused on achieving accurate representations of wind turbine loading across diverse atmospheric conditions. Recent advancements in numerical weather prediction techniques make it possible to downscale weather conditions for operational use, underscoring the importance of including air-sea interactions using models such as the Coupled-Ocean-Atmosphere-Wave-Sediment Transport model to improve wind energy predictions. Nevertheless, challenges of high computational costs, the elusive ”gray zone” in simulations, and creating accurate wind turbine digital twins for predictive modeling remain.The current research addresses this challenge by proposing a novel modeling framework integrating the COAWST model with the OpenFAST aeroelastic solver. The current research strives to bridge the gap between the different dynamic regimes involved under diverse atmospheric conditions to achieve real-world representative wind turbine loading. Therefore, a scaled wind turbine model is implemented in the OpenFAST aeroelastic solver. Further, this research tests and discusses the proposed model framework’s capabilities and limitations under extreme weather phenomena.

Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.

Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling

In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.

Wind Turbine Loads in the Near Wake

This work is analyzing the the engineering wake models Frandsen and TNO for small inter-turbine distances. Therefore, loads generated by actuator line LES (AL-LES) wind fields will be compared with the loads generated by the mentioned engineering models. The results are checked for plausibility by comparing the current results with the available literature. Load comparisons for non-dimensional distances x/D ranging from 1.75 to 4 and for seven different wind speeds in order to consider the influence of the thrust coefficient were made. For each speed a low and a high turbulence intensity case is considered. Two latest generation turbines are investigated (one with a diameter of 138 m and one with a diameter of 175 m).Our studies reveal that the fatigue loads computed with the Frandsen and TNO model are conservative compared to the loads generated by AL-LES wind fields. The Wöhler averaged (m=10 and a sector of 120°) flap wise blade root bending moments are at least 150% higher for the TNO and Frandsen model compared to the loads generated by AL-LES wind fields for distances lower than 2.3D and C T values higher than 0.7. This indicates that current sector management rules applied by means of the results of the Frandsen and TNO model are too restrictive. A possible way to decrease the conservatism without posing at risk the structural integrity of the turbines is the following: The wake added turbulence intensity can be kept constant for regions in the parameter space with very high conservatism (e.g. for distances smaller than 2.3D and C T values higher than 0.7).

Impact of dynamic stall model tailoring on wind turbine loads and performance prediction

The future of wind turbines will be characterised by long, slender blades subject to dynamic inflow and aeroelastic deflections. This makes the next generation of blades more prone to encounter dynamic stall effects, in which significant forces and loads fluctuations can be expected. Dynamic stall models can be tailored to suit the aerodynamics of different airfoils. Although different dynamic stall models exist, the impact of the choice of model, its implementation and calibration on the overall wind turbine performance remains to be assessed. In this work, we gathered an experimental dynamic dataset for a representative airfoil, the FFA-W3-211, to define the semi-empirical time constants for the Beddoes-Leishman dynamic stall model. An important differentiation is made between stall regions for positive and negative angles of attack, and the impact of tailored coefficients is assessed at airfoil scale. The difference between the tailored and untailored model is quantified for power performance and loads of the IEA 15 MW reference wind turbine. The results highlight a significant load over-prediction from the untailored Beddoes-Leishman model, whereas changes in power performance are negligible.

Impact of low-frequency fluctuations on loads of a fixed-bottom offshore reference wind turbine

Marine atmospheric turbulence is predominantly characterized by large-scale structures associated with low-frequency wind turbulence. However, conventional turbulence models for assessing wind turbine loads often fall short of adequately integrating these low-frequency wind fluctuations. In this study, we used a model for low-frequency wind fluctuations to generate wind fields representative of offshore wind conditions in the North Sea. The IEA 15 MW reference wind turbine was subjected to three distinct incoming wind fields: high-frequency (3D) turbulence, low-frequency (2D) turbulence combined with high-frequency (3D) turbulence, and 3D turbulence scaled by target or measured standard deviation of wind components. We found that increased damage equivalent loads by incorporating the low-frequency wind fields are only significant for the tower base fore-aft and blade root fore-aft moments. The impact of low-frequency wind turbulence on the damage equivalent loads was more pronounced for below-rated wind speeds. The scaled 3D turbulence yielded the highest lifetime damage equivalent loads, i.e., in the range of 8-18% more than the unscaled 3D turbulence and combined 2D +3D turbulence. This method leads to an overestimation of loads and, hence, more expensive turbines. Incorporating 2D+3D turbulence in wind fields resulted in a 3.8% increase in lifetime damage equivalent loads for the tower base fore-aft and blade root flapwise moments compared to unscaled 3D turbulence. These findings underscore the importance of considering low-frequency wind fluctuations in the turbine design process, especially for a more accurate estimation of loads.