Search Results (6,643)

Wind turbine blade defect detection requires accurate identification of small and irregular defects while maintaining low computational cost for practical inspection scenarios. However, lightweight detectors often suffer from insufficient local feature extraction, limited multiscale feature fusion, and weak responses to critical defect regions. To address these issues, this study proposes a Receptive-Field-Enhanced You Only Look Once model (RFE-YOLO), a lightweight defect detection model based on You Only Look Once version 10 nano (YOLOv10n).The proposed model introduces three task-oriented improvements. First, C2f-RFAConv is embedded into the backbone to enhance receptive field aware local feature representation for fine grained defects. Second, a Compact Cross-scale Feature Fusion Module, termed CCFM, is designed in the neck to improve the integration of low-level detail information and high-level semantic features with reduced computational complexity. Third, an Efficient Local Attention module is inserted before the detection head to strengthen defect-related spatial responses after feature fusion. Experiments were conducted on a wind turbine blade defect dataset containing three categories, namely Crack, Oil leakage, and Peel. The results show that RFE-YOLO achieves 89.9% mean Average Precision at an Intersection over Union threshold of 0.5, namely mAP@0.5, and 64.73% mAP@0.5:0.95. Compared with YOLOv10n, RFE-YOLO improves mAP@0.5 by 2.8 percentage points while reducing the number of parameters from 2.70M to 1.91M and giga floating point operations from 8.4 to 5.3. The inference speed reaches 88.8 frames per second on an NVIDIA GeForce RTX 3090 GPU. These results indicate that RFE-YOLO achieves a favorable balance between detection accuracy and model efficiency under the current experimental setting. Full article

The increasing demand for sustainable and lightweight structural systems has motivated the development of alternative materials for wind turbine tower applications, where conventional steel structures are associated with high material consumption and environmental impact. In this study, a novel steel-reinforced recycled thermoplastic composite system is proposed as an alternative structural solution. To enable the design and practical application of such composite systems, the mechanical properties of the recycled thermoplastic matrix were experimentally characterized. Compression and tensile tests revealed average yield strengths of approximately 32 MPa in compression and 7.8 MPa in tension. To account for the environmental conditions encountered in field applications, the temperature-dependent mechanical behavior of the material was investigated. Since the critical mechanical response of the thermoplastic matrix in the composite system is governed by compression rather than tension, the study was limited to compression tests under elevated temperatures. The results show that the compressive yield strength decreases to approximately 31 MPa at 55 °C. An analytical model based on the transformed-section approach was also developed to predict the flexural behavior of the composite section and was validated through three-point bending tests, with an analytically predicted yield load of approximately 31.5 kN showing good agreement with experimental results. To assess structural applicability at a larger scale, a full-scale composite wind turbine tower was designed and manufactured, and its dynamic performance was evaluated through field measurements under natural wind loading conditions. The results indicate that the composite tower exhibits comparable dynamic behavior to a conventional steel tower, with a first natural frequency of approximately 3.08 Hz compared to 2.89 Hz for the steel tower, along with enhanced damping characteristics. These findings demonstrate that steel-reinforced recycled thermoplastic composites offer a promising and sustainable alternative for wind turbine tower applications, with potential for broader use in structural systems. Full article

This paper investigates whether event-level pitch-bearing fatigue damage can be estimated directly from turbine measurements, and whether these mechanical damage metrics leave measurable fingerprints in the generator DC-link voltage and current. To achieve this, a case study was performed using SCADA and structural load data from the 45 kW Chalmers (Björkö) research turbine. This data was segmented into 223 park-run-park pitch events. For each event, blade-root flapwise and edgewise bending moments were converted into radial and axial loads at the pitch bearing; an equivalent dynamic bearing load $P_{\mathrm{eq}}\left(t\right)$ was reconstructed using SKF and DG03 formulations; and rainflow counting with an S–N curve and Palmgren–Miner’s rule was used to compute event-level damage indices compatible with the International Standard Organization basic rating life concepts. In parallel, DC-link voltage and current were summarized into time-domain features, combined with operating-condition descriptors, and clustered using PCA-based k-means. The resulting clusters captured distinct electrical regimes that, across several event batches, corresponded to different levels of accumulated fatigue damage: regimes with sustained high DC-link voltage and longer duration tended to exhibit higher mean damage indices than lower, steadier DC regimes, indicating an electromechanical link. The results show that physics-based lifetime estimation and unsupervised analysis of existing electrical traces can be combined into a hybrid workflow for pitch-bearing condition assessment without additional sensors. Full article

This study investigates whether aerodynamic interaction effects in an interlocked (negative-gap) counter-rotating dual Savonius rotor configuration can improve the efficiency of drag-based vertical-axis wind turbines in urban wind conditions. Two-dimensional Computational Fluid Dynamics (CFD) simulations were performed in ANSYS Fluent 2025 R2 using both steady and unsteady RANS approaches, including dynamic meshing to enable collision-free rotation in the interlocked overlap region. The numerical setup was first validated for a single two-bucket reference rotor against published experimental data of torque and power coefficients and subsequently applied to dual-rotor configurations with negative gap distances. The results show that the dual-rotor arrangement redistributes torque production over the azimuth angle and yields a smoother and consistently positive mean static torque coefficient, indicating improved self-starting behavior compared to the single rotor. Under transient operation, the dual-rotor configuration yields higher power coefficient values across the entire investigated tip-speed ratio range. The highest performance gain is observed at a tip-speed ratio of $\lambda \approx 1.0$, where the peak power coefficient increases from $c_p\approx 0.25$ (single-rotor) to $c_p\approx 0.32$ (dual-rotor), corresponding to an improvement of the power coefficient of about $\Delta c_p/c_{p0}\approx 28\%$. Full article

With the large-scale integration of offshore wind farms (OWFs), harmonic issues caused by the interaction between high-frequency switching of converters and complex network impedances pose severe challenges to power quality. Traditional harmonic monitoring heavily relies on post-event fixed-threshold alarm mechanisms, which struggle to achieve early warning during the low-distortion sub-health operation stage and lack the capability for multi-dimensional tracing of harmonic degradation sources. To address these limitations, this paper proposes a deep warning network for grid harmonics combining multi-step regression and multi-dimensional tracing within a unified multi-task learning (MTL) architecture. First, a deep shared feature encoder, integrating a bi-directional long short-term memory (Bi-LSTM) network with a multi-head self-attention (MHSA) mechanism, is utilized to extract high-order temporal coupling features between meteorological evolution and multi-node electrical states. Subsequently, the main task branch executes a k-step-ahead multivariate time-series regression to accurately predict the evolution trend of total harmonic distortion (THD) at both the point of common coupling (PCC) and the turbine terminal. Simultaneously, the auxiliary task branch performs multi-label micro-state classification based on relative degradation thresholds, achieving fine-grained multi-dimensional tracing covering spatial nodes, electrical attributes, and their joint micro-states. Experimental results on real-world OWF operational data demonstrate that through the joint optimization of regression and tracing tasks, the proposed MultiDimKStepMTL model significantly improves time-series prediction accuracy, achieving a $10.3\%$ relative improvement over single-task baselines, while substantially reducing computational overhead. This research successfully advances grid harmonic monitoring from passive response to proactive micro-state early warning, providing a solid, highly interpretable data-driven foundation for active filter control of offshore wind clusters. Full article

Electric vehicles (EVs) are playing a crucial role in decarbonising the transportation industry by cutting down on toxic emissions from vehicles. Increasing the range of EVs is still a major hurdle in the widespread adoption of such vehicles, and serious efforts are underway across the globe in order to address this issue. A potential solution to this is the integration of small wind turbines with EVs to extract wind power and help charge the batteries. However, serious efforts in this regard are severely lacking in the published literature. This study aims to bridge this gap through systematic numerical investigations on a drag-based vertical-axis wind turbine (VAWT) installed on top of an EV. Utilising Computational Fluid Dynamic (CFD)-based solvers, the flow fields associated with the turbine are analysed in detail. Instantaneous and average power produced by the turbine have been critically evaluated over its entire operational range and at different vehicle speeds. The results obtained show that the VAWT has a peak power coefficient (Cp) of 0.46 at a tip speed ratio (λ) of 0.55. The average power produced by the VAWT at 30 mph, 50 mph, and 70 mph is about 160 W, 700 W, and 2 kW, respectively. Full article

Offshore wind and wave energy have emerged as promising alternatives due to their abundant availability and substantial energy potential. This research explores a V-shaped semi-submersible platform designed to support both wind turbines and wave energy converters (WECs). The V-shaped configuration is selected for its ability to enhance hydrodynamic performance by reducing wave-induced loads and improving motion characteristics, while also providing increased structural stability through a wider effective footprint. In addition, the geometry creates a favourable layout for integrating WECs between the pontoons, enabling efficient wave energy capture without significantly interfering with the aerodynamic performance of the wind turbine. The study assesses the performance of different V-shaped platform configurations, ensuring their motion responses meet the operational limits required for wind turbines. It also examines whether interactions between the platform and coexisting WECs can lead to an improvement in wave energy absorption efficiency. Numerical hydrodynamic diffraction was conducted using the boundary element method in ANSYS AQWA, based on 3D potential flow theory and considering viscous damping effects, to calculate platform motion and the wave power output of WECs with a linear power take-off system. Preliminary analyses revealed that optimising the placement of WECs on a V-shaped semi-submersible can significantly improve energy generation while maintaining acceptable platform motion. This research demonstrates the additional potential of integrated wind-wave energy systems in delivering efficient and sustainable offshore energy solutions. The study also highlights the advantages of a turret mooring system for passive alignment with environmental forces, prolonging platform structure longevity and enhancing energy efficiency. Full article

The growing integration of solar, wind and battery energy storage (BES) of the microgrids (MGs) has increased the necessity of real-time energy management, especially in the multi-microgrid (multi-MG) setting, where the generation and the load change stochastically. This paper presents a Java Agent DEvelopment (JADE)-based Multi-Agent System (MAS) for real-time energy management of a low-voltage hybrid multi-MG system incorporating solar photovoltaic (PV), wind generation, and battery energy storage (BES). The proposed framework’s novelty lies in its physical campus-scale hardware deployment—validated across four operating scenarios (single MG off-grid, single MG on-grid, dual MG off-grid, and dual MG on-grid)—combined with autonomous inter-MG power sharing, which distinguishes it from existing simulation-only MAS-based microgrid studies. The suggested framework facilitates decentralized communication between interconnected MGs and the utility AC grid to facilitate the proper management of power flow, its exchange, and the reliability of the system. The intelligent agents are used to coordinate solar, wind, BES, and load changes in order to adjust to changing demand conditions. The system is physically implemented on a campus rooftop with two 1 kW solar PV arrays and two 1.5 kW wind turbine generators, each paired with a 24 V, 150 Ah battery bank, operating on a 24 V DC bus. Results across 24 h real operational profiles demonstrate effective power balance maintenance, renewable energy maximization, and constraint-compliant battery operation (SOC is bounded within 20–90%). A direct comparison with a conventional centralized JavaScript-based EMS confirms equivalent dispatch accuracy while demonstrating superior scalability, fault tolerance, and modularity of the proposed JADE MAS architecture. Full article

Loading fluctuations and fatigue-related structural demand under turbulent wind conditions are important factors that limit the reliability of small wind turbines. This study investigates the separate effects of turbulence intensity and pitch angle on a 5 kW distributed variable-pitch wind turbine prototype using an OpenFAST-based aeroelastic model validated against field measurements. Under the adopted simulation setup and selected operating conditions, increasing turbulence intensity from 5% to 20% leads to a pronounced increase in the extreme blade-root flapwise bending moment and a substantial reduction in the estimated comparative fatigue life. The analysis also reveals a clear trade-off between aerodynamic efficiency and structural durability: among the tested pitch settings, the 6° case yields the highest power output, but also exhibits the largest load fluctuations and the shortest estimated comparative fatigue life. Adjusting the pitch angle to 0° or 12°, while reducing power to some extent, alleviates fatigue-related structural demand and increases the estimated comparative fatigue life. Overall, the results provide a validated prototype-level comparative assessment of how turbulence intensity and pitch angle influence aerodynamic performance, structural response, and fatigue-related demand in the studied turbine. Because the present work focuses on one prototype and does not include cross-turbine comparison or a full stochastic convergence study, the reported quantitative results should not be interpreted as directly generalizable to other turbine configurations. These findings may nevertheless provide a useful basis for future studies on load-aware pitch regulation under turbulent inflow. Full article

As offshore wind energy advances into deeper waters, the dynamic response and safety assessment of tension leg platform (TLP) wind turbines under complex marine conditions have become focal research points. This study investigates a 15 MW TLP wind turbine, acquiring data on motion responses, mooring tensions, and tower-base loads through time-domain analysis, with extreme value estimation conducted using the mean up-crossing rate method. The results indicate that under normal operating conditions, second-order wave forces significantly influence extreme response estimation. At an exceedance probability of 0.01, the second-order sum-frequency force increases the extreme tower base shear by 4.28% and the bending moment by 10.11% compared to the first-order-only case, while the difference-frequency force has a minor effect. Different wind-wave incidence angles cause distinct variations in turbine motion, with head-on incidence exciting the largest wave-frequency responses and lateral incidence producing relatively weaker excitation effects. Furthermore, the coupling effect between incident direction and second-order wave forces further amplifies extreme response risks. Therefore, it is essential to fully assess the prevailing wind-wave directions in the target sea area and consider the effects of second-order wave forces, especially the sum-frequency component, to ensure the long-term safe operation of TLP wind turbines under complex sea conditions. Full article

Wind turbines operating under highly variable wind conditions require effective pitch-angle control to ensure maximum energy capture and structural protection. This study examines the performance of a 2.5 MW GEWE-B2.5-100 horizontal-axis wind turbine by quantifying how pitch-angle regulation affects power limitation, rotor-speed stability, and mechanical loading. Using an aerodynamic model, the maximum power point (MPP) was identified at an optimal mechanical angular speed of ω_OPTIM = 240.45 rad/s for V = 10 m/s, and the corresponding pitch-angle adjustments were determined for wind speeds up to 26 m/s, where β increases from 9.28° to 29.06° to maintain safe operation. Three dynamic case studies were conducted. Under sinusoidal wind variations between 10 and 14 m/s, PI-based pitch control limited rotor-speed oscillations to below 0.1%, ensuring stable operation. For exponential wind increases to 24 m/s and 34 m/s, the pitch angle rose to 28.48°, with rotor-speed overshoot remaining minimal at 0.004% and 0.006%, respectively. As stated in the manuscript, “dynamic pitch angle control significantly reduces rotor speed oscillations and mitigates excessive which indirectly contributes to alleviating potential structural stresses”. These results show that pitch-angle control is a key factor in turbine performance, enabling precise power capping at 2.178 MW and ensuring structural safety under extreme wind conditions. The proposed strategy supports reliable integration of large wind turbines into modern power systems. Full article

Large-eddy simulation (LES) of wind farms is often limited by the computational cost required to represent many turbine rows and to obtain statistically converged wake and power statistics. Here, we present LES of an extended wind-farm configuration using the PALM model system, where cyclic lateral boundary conditions are employed to emulate interior-farm interaction in an idealized neutral boundary layer. The setup consists of nine identical horizontal-axis wind turbines arranged in a staggered array within the computational domain. Time-averaged hub-height fields show coherent wake corridors with a mean inflow-speed reduction of $23.7$ % (array-mean across turbines) relative to an undisturbed background wind speed, and peak wake deficits reaching $71.4$ % in the near-wake region. Turbulence levels increase markedly in the wake shear layers, with hub-height turbulence intensity enhanced by $32.2$ % in the rotor region compared to background conditions; correspondingly, the peak hub-height SGS-TKE increases by a factor of 6.74 relative to background. Vertically averaged profiles indicate a momentum deficit within the turbine layer and gradual recovery aloft; the streamwise turbulent momentum flux remains predominantly negative, demonstrating the downward transport of higher-momentum air from above as a key recovery mechanism. Turbine rotor-power statistics show an initial adjustment followed by a quasi-stationary regime, with a farm-mean rotor power of $1.93$ MW and persistent inter-turbine variability characterized by a mean coefficient of variation of $61.2$ %. Overall, the results demonstrate that the proposed extended-farm LES approach enables computationally efficient quantification of wake dynamics, vertical momentum transport, and their impact on power variability under idealized neutral wind-farm conditions. Full article

The growing energy demand in rural areas such as the ejido Boca del Cerro, located in Tenosique, Tabasco (Mexico), near the Usumacinta River, calls for sustainable energy solutions such as microgrids. This study proposes an energy management system combining renewable energy forecasting and fuzzy control for a simulated small autonomous rural microgrid scenario designed to supply a fixed priority load of 5 kW and a variable flexible load ranging from 1 to 10 kW. Three LSTM architectures (vanilla, stacked, and bidirectional) are compared for predicting solar irradiance, wind speed, and river flow. The vanilla model is optimized using Hyperband to improve prediction accuracy, particularly for flow rate, which is rarely addressed in similar studies. Forecasts feed into models of photovoltaic, wind, and hydro systems within the microgrid. Energy dispatch is managed through fuzzy logic control. The fuzzy controller supports load prioritization, battery charge/discharge management, and surplus energy redirection to an absorbing load. The final vanilla LSTM achieved RMSE values of 25.741, 0.302, and 12.644 for solar irradiance, wind speed, and river flow, respectively, with NSE values above 0.949 in all cases. These results indicate high forecasting accuracy for solar irradiance and river flow, with limited improvement for wind speed. Overall, the proposed EMS enables effective energy flow management, while the integration of hydrokinetic turbines with AI-based forecasting represents a novel contribution. Full article

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process. Full article

Turbulent wind conditions pose significant challenges to the blade structural reliability of small wind turbines. Different from the authors’ previous work, which mainly focused on the output characteristics of the same 5 kW prototype under variable inflow conditions, this study combines field-test observations with numerical simulations to further investigate the blade structural dynamic responses of a 5 kW variable-pitch wind turbine under both uniform inflow and extreme wind conditions. Owing to the unique pitch-regulation mechanism of the proposed turbine, two pitch-control modes, namely conventional power-limited pitch control and active stall pitch control, are comparatively analyzed to clarify their effects on blade load, stress, and displacement responses. The results indicate that, under uniform inflow conditions, stresses are concentrated near the leading edge of the blade mid-span, while the maximum displacement occurs at the blade tip. Both stress and displacement decrease with increasing conventional pitch angle. Under extreme wind conditions, increasing gust intensity causes a nonlinear growth in blade loads and aggravates blade structural response. During active stall pitch control, the load distribution pattern is generally consistent with that under conventional pitch control, whereas the blade structural response first decreases and then increases as the pitch angle is adjusted toward negative values. Under uniform inflow at the rated wind speed of 11 m/s, the blade-tip maximum displacement decreased from 56.51 mm under the +6° power-limited/reference pitch condition to 48.42 mm under the −6° active-stall-related pitch condition, corresponding to a reduction of approximately 14.3%. These results provide a useful reference for the blade structural design and control optimization of distributed small wind turbines under complex inflow conditions. Full article