Small wind turbines face significant challenges in achieving commercial viability due to lower efficiency and higher energy costs compared to utility-scale systems and competing renewable technologies. Counter-rotating dual-rotor wind turbines (CR-DRWTs) with dual-rotational armature configurations offer a potential pathway for efficiency improvements through doubled direct-drive power and minimal mechanical complexity, suitable for urban applications. This study presents a detailed experimental investigation of a 1.6 m diameter CR-DRWT through wind tunnel testing at the Centre Scientifique et Technique du Bâtiment (CSTB) in Nantes, France, conducted at wind speeds between 4 and 15 m s−1. An improved turbine design is tested with enhanced instrumentation, including independent measurements of rotor rotational speed and blade pitch angle, enabling a detailed characterization of rotor interaction, operating behaviour, and power performance. The turbine achieved a maximum electrical power output of 1014 W and a peak measured power coefficient of 0.33 while demonstrating reliable self-starting at wind speeds as low as 3.5 m s−1. To interpret and generalize the experimental findings, an existing blade element momentum (BEM) model for dual-rotor systems is extended to explicitly represent torque balance and power production in a dual-rotational armature configuration. The extended model shows good agreement with experimental trends and is further applied in a sweep optimization. The optimized configuration predicts a theoretical maximum power coefficient of 0.56, highlighting substantial remaining performance potential. By combining wind tunnel measurements with a validated BEM model extension, this work provides unique reference data and supports the development of mechanically simple CR-DRWTs for future small-scale wind energy systems.
Model predictive control (MPC) for wind turbines offers several interesting advantages over simpler techniques, as for example the direct optimization of a goal function, the inclusion of constraints, non-linear coupled dynamics, and wind preview (when available). To enable real-time execution, MPC uses a reduced-order model (ROM) that approximates the dynamics of the controlled system using only a limited number of degrees of freedom. As a result, the accuracy of the ROM is often the main limit to the performance of MPC. To address this problem, an adaptive controller-internal model can reduce plant-model mismatches, potentially leading to improved performance.This work proposes an adaptive economic non-linear MPC (ENMPC) for wind turbines. The controller maximizes profit by optimally balancing fatigue damage cost with revenue due to power generation. The cyclic fatigue cost is formulated directly in the controller using the novel parametric online rain flow counting (PORFC) approach. PORFC provides a rigorous continuous expression of the discontinuous cyclic fatigue cost using time-varying parameters. Adaptivity is obtained by a controller-internal gray-box model that combines reduced-order physical dynamics with data-driven correction terms. These are implemented via a neural network that is trained offline. Additionally, system state and disturbance estimators are included in the closed-loop controller.The improvement in state predictions due to model adaptation is first assessed and compared with the non-adapted baseline ROM in open loop. The performance of the adaptive ENMPC and the impact of a reduced plant-model mismatch is then assessed in closed loop for a reference multi-MW onshore wind turbine in a realistic simulation environment. Results show that the adaptive ENMPC yields higher economic profits at significantly lower pitch and torque travels compared to the baseline non-adaptive ENMPC. While the enhanced closed-loop performance and economic gains of the proposed model adaptation are significant, they come at the cost of a slight increase in the computational burden of the controller.
Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.
Power converters are among the most frequently failing subsystems of onshore and offshore wind turbines. In order to minimize the resulting downtime and production losses, the time to repair should be as low as possible. In practice, however, it is not uncommon for several turbine visits to be necessary, as information about the failure mode and the spare parts required can often only be determined on site. This paper presents a data-driven, interpretable workflow for the remote diagnosis of power-converter-related turbine shutdowns using converter control system data from an offshore wind farm. The study uses converter fault events and three data sources: high-resolution fast logs (4.5 kHz, −350 to +200 msmin operating data, and fault flags derived from event log data. From an initial 864 engineered features we remove low-variance and highly correlated features, apply a subsampled decision-tree inclusion-rate filter to retain 34 features, and estimate diagnostic impact via subsampled logistic regression. Results show that fast-log features and converter fault flags contain the most predictive information for classifying standstill severity after a fault-induced shutdown, while low-resolution operating data contribute little. Using four of the derived features yields the best cross-validated performance in a decision tree, with an accuracy of 0.89 and an F1 score of 0.86. The proposed approach is practical for industry use and offers the potential to provide explainable decision support for improving the first-time fix rate.
This study experimentally investigates the performance of vortex generators (VGs) designed for steady stall control in preventing unsteady trailing-edge flow separation and dynamic stall during pitch oscillations occurring on inboard and midboard wind turbine blade sections. Surface pressure measurements are conducted in the TU Delft low-speed wind tunnel on a DU-97-W-300 airfoil undergoing pitch oscillations while equipped with VGs of various vane sizes and shapes. In steady conditions, vanes with heights smaller than the local boundary layer thickness optimally balance delaying stall following trailing-edge separation with achieving maximum lift-to-drag ratio among the tested triangular vane VGs. However, these same VGs with vane heights smaller than or equal to the steady local boundary layer thickness are insufficient to suppress the onset and upstream progression of a trailing-edge separation front in all pitching cycles. VGs whose vane height exceeds the local boundary layer thickness for a larger part of the pitch cycle prevent the onset and upstream progression of the trailing-edge separation front for a larger percentage of cycles. Contrary to past literature, rectangular vanes yield a higher steady aerodynamic efficiency than triangular vanes. Rectangular vanes also suppress trailing-edge flow separation in all pitching cycles at all tested reduced frequencies, indicating more effective boundary layer energization than triangular vanes, thus proving to be a better VG shape for steady and unsteady stall suppression on thick airfoils.
The Third Wind Forecast Improvement Project (WFIP3) is a multi-institutional field campaign designed to advance the understanding and prediction of the offshore atmospheric boundary layer along the US east coast. Extending from February 2024 through August 2025, WFIP3 combines long-term coastal and offshore measurements with targeted modeling and forecasting efforts. This data paper presents the WFIP3 event log, a curated record of 578 d
wakes<b>Benchmarking of three DWM-based wake models at below-rated wind speeds</b><br> Øyvind Waage Hanssen-Bauer, Paula Doubrawa, Helge A. Madsen, Henrik Asmuth, Jason Jonkman, Gunner C. Larsen, Stefan Ivanell, and Roy Stenbro<br> Wind Energ. Sci., 11, 1913–1948, https://doi.org/10.5194/wes-11-1913-2026, 2026<br> Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.
Copernicus Electronic Production Support Office 2026-05-26T21:15:30+02:00 2026-05-26T21:15:30+02:00
Accurate estimation of extreme wind speeds from tropical cyclones is a significant challenge in regions prone to tropical cyclones. This study presents a method to estimate the 50-year return wind speed at heights relevant to wind turbines. The International Best Track Archive for Climate Stewardship data are combined with the Holland parametric model and the Gumbel distribution to assess extreme winds in three tropical-cyclone-affected regions in the Northern Hemisphere. These regions are Taiwan, Japan, and the east coast of the US. To assess the uncertainty in the results from differing input parameters, Monte Carlo simulations are used. The method aligns with previous studies through the spatial representation of wind speeds and maximum 50-year return wind speeds in Taiwan and Japan that can be attributed to the large sample size of data points located in a limited spatial area. The east coast of the US exhibits spatial fragmentation and only partially aligns to the spatial representation of 50-year return wind speeds from previous studies, which, conversely, is due to the smaller sample size and wider spatial region of which they cover. This study shows that combining International Best Track Archive for Climate Stewardship data with parametric and statistical models provides a practical approach to estimate extreme wind speeds while highlighting the need for an understanding of regional characteristics to ensure reliability of the results.
The helix approach has shown potential in increasing wind farm power production through enhancing wake mixing. By applying periodic blade pitch signals to upstream turbines, a helical wake is generated, which reduces velocity deficits for downstream turbines and mitigates the wake effect. While promising, the closed-loop implementation of the helix approach remains largely unexplored, which could enable handling uncertainties and model errors in wind farm applications. This work presents a framework that integrates lidar-based wake measurements to enable such closed-loop control. First, a downwind-facing continuous-wave lidar is used to extract the hub vortex as the controlled variable. Second, we developed a control algorithm that regulates the hub vortex position in the helix frame, thereby controlling the helical wake. Simulations in QBlade show that the framework enables a real-time, flow-informed closed-loop wake mixing approach. Compared with the open-loop cases, the framework corrects the shear-induced steady-state wake bias and enables measurement-informed, dynamic pitch adjustments under turbulence. In shear, bias correction increases downstream power but raises structural loads on both turbines; under turbulence, dynamic pitch control delivers a modest farm-level power gain with only minor load increases. These outcomes highlight the promise of flow-informed, closed-loop wake-mixing control and motivate further investigation.
This study delivers a method and datasets for a global offshore atlas for turbulence intensity (TI) from 10 to 200 m. The method includes both surface-driven three-dimensional boundary-layer turbulence and large-scale two-dimensional turbulence. This systematically includes the effect of large-scale eddies, particularly at weak wind conditions, and hence significantly improves TI in weak to moderate wind conditions. This method describes water roughness length through a dependence on wave age and wind speed, which is suitable for moderate to strong wind conditions. The method also includes stability dependence through the Obukhov length. Based on theories and measurements in literature, algorithms for TI have been calibrated for heights up to 200 m. We use the ERA5 atmospheric and wave data to demonstrate the use of the method and create a global dataset. The results show satisfactory agreement with measurements and data from the literature.
Accurately assessing wind turbine performance in large offshore wind farms requires a nuanced understanding of how inflow parameters, turbulence intensity (TI), wind shear, and wind veer are associated with power production across different turbine rows. In this study, we analyze 1310 min operational data from more than 40The results indicate that the relationships between wind shear/veer and power output depend strongly on turbine location: upwind shear and veer correlate negatively with active power deviation in the front row but show positive correlations in the middle and rear rows. In addition, TI in the wake region has a distinct influence on power production, particularly at lower wind speeds, relative to TI observed in the front row. Finally, the rear section of the wind farm exhibits approximately 30 % lower variability in active power relative to the front section. These location-specific changes underscore the evolving nature of inflow conditions within large wind farms. Furthermore, IEC-based REWS may not fully capture the effects of shear and veer in large-scale offshore wind farms. Overall, the findings indicate that turbines operating in waked conditions may require additional inflow-characterization parameters beyond standard IEC norms to enable more accurate performance evaluations and support farm-level efficiency improvements.To our knowledge, this study provides one of the first empirical assessments spanning the front, middle, and rear sections of a modern offshore wind farm to evaluate IEC-based REWS and TI normalizations, revealing location- and regime-dependent limitations and motivating complementary inflow descriptors for wake-affected operation.
This study investigates how the motion of a floating wind turbine affects the aerodynamic performance and dynamic loading of a downstream turbine operating in its wake. Wind tunnel experiments were conducted using a two-turbine setup, where the upstream turbine was subjected to controlled platform motions (both sinusoidal and wave driven), while the downstream turbine remained fixed and was tested in multiple relative positions. Results show that large-amplitude, low-frequency sinusoidal motions of the upstream turbine, especially in crosswind and yaw directions, can increase the power output of the downstream turbine under low-turbulence conditions and at short turbine spacing (3–5 rotor diameters). The largest relative gain reached 26 % over the fixed case, although the absolute increase remained moderate because the highly persistent wake, driven by low turbulence and strong thrust of the upstream turbine, resulted in very low baseline power of the downstream turbine. The gains obtained under idealized sinusoidal motions were replicated in cases with realistic wave-driven motions when wind and waves were aligned but not when wind–wave misalignment introduced crosswind movements of the upstream wind turbine. In parallel, motion of the upstream turbine increased the dynamic loading on the waked turbine. Load increments varied with turbine spacing and alignment, and were more pronounced in sinusoidal motion cases than with wave-induced motions, which also produced increased dynamic loading but with smaller amplitudes. The loads resulting from wave-induced motions exhibited a broad spectral distribution, consistent with the wide frequency content of the wave excitation. Overall, these findings underscore the fact that platform-induced wake dynamics are not a secondary effect but a key driver of wake recovery, downstream turbine performance, and dynamic loading, and must be considered in the design and operation of floating wind farms.
Maintaining wind turbines in efficient and optimal working conditions is crucial to maximize energy production and reduce unexpected downtime, especially in remote or offshore installations. Pitch misalignment is one of the most common issues affecting wind turbine performance. Our previous studies addressed the automatic detection of such a fault using signals from mechanical moments collected from the fixed and rotating reference frames. Specifically, the introduced approaches involve applying machine learning techniques to ad-hoc-designed physics-based indicators, extracted from the mentioned signals, to detect the misalignment and localize the fault. Despite the fact that these approaches work effectively in the case of both single and multiple blades misaligned simultaneously, conditions in which all blades are misaligned by the same quantity have not been taken into account. Unlike individual blade misalignments, this fault presents unique challenges in its detection due to the symmetrical nature of the fault, which minimizes immediate operational disruptions but gradually impacts turbine performance and energy efficiency. To also account for this condition, in this paper, we present an innovative methodology to identify and classify uniform pitch misalignment across all wind turbine blades. This issue has been scarcely explored in existing literature, leaving a critical gap in the understanding and diagnosis of uniform pitch misalignment. Extensive results conducted with linear and turbulent wind conditions prove the effectiveness of our approach at identifying and quantifying the entity of the misalignment, thus paving the way for more efficient and reliable wind turbine diagnostics.