Search Results (172)

Accurate modeling of wind turbine power curves is essential for performance assessment, energy forecasting, condition monitoring, and operational optimization in wind energy systems. This study presents a SCADA-based comparative assessment of established power curve modeling approaches through a single-site case study conducted on a low-power Vertical Axis Wind Turbine (VAWT) operating under real environmental conditions at the University of the Isthmus, located in the Isthmus of Tehuantepec, Oaxaca, Mexico. The evaluated methods included the maximum power curve, an aerodynamic model based on Blade Element Momentum Theory (BEMT), parametric approaches using polynomial and logistic regressions, and non-parametric data-driven methods based on Random Forest (RF), Gaussian Process Regression (GPR), and Kernel Density Estimation (KDE). One year of SCADA data, including wind speed and generated power measurements, was analyzed, while model performance was assessed using RMSE, MAE, and R² metrics. The results showed that the machine learning approaches achieved the lowest prediction errors among the evaluated models, with RF providing the best overall performance (RMSE = 7.4%, MAE = 4.8%, R² = 0.9814), followed by GPR and KDE under the investigated operating conditions. Additionally, Weibull analysis yielded parameters of k = 1.887 and c = 7.875 m/s, while the largest prediction errors were observed within the partial-load operating region (approximately 4–10 m/s) and as the turbine approached the rated operating condition (approximately 10–12 m/s). These findings indicate that, for the investigated low-power VAWT operating at the experimental site, non-parametric approaches provided the most accurate representation of the power curve among the evaluated models, highlighting the potential of SCADA-based data-driven techniques for comparative model assessment under similar operating conditions. Full article

Understanding wind direction (WD) variability and vertical directional shear is critical for aviation safety, particularly during landing when aircraft are highly sensitive to abrupt wind changes. This study analyzes the temporal behavior of WD and low-level directional shear over Baghdad International Airport during representative months of 2024 (January, April, July, and October) using hourly ERA5 u and v wind components at 10 and 540 m. Circular statistical measures quantify WD variability, while directional shear roses and absolute directional shear change rates characterize vertical wind alignment and shear intensity relevant to aviation operations. The results reveal pronounced seasonal contrasts. Summer (July) shows the most stable wind regime with strong directional persistence and weak shear (<~0.1–0.15 °/m), winter (January) is generally stable but punctuated by synoptic shear peaks up to ~0.85 °/m, spring (April) exhibits moderate and more variable shear (~0.1–0.45 °/m), and autumn (October) is the most dynamically unstable, with reduced persistence and frequent shear maxima approaching ~0.9–0.95 °/m. Circular standard deviation (σ_dir), is highest in winter–spring (mea n≈ 38° in January and ≈34° in April), weakest in summer (≈16–17° in July), and increases again in autumn (≈28–33° in October), reflecting seasonal changes in atmospheric forcing. σ_dir exhibits a clear seasonal cycle, with highest values during winter and spring and the most stable conditions in summer. These results show that integrating ERA5-based circular wind statistics with directional shear diagnostics effectively identifies aviation-critical wind regimes, while future multi-year and aircraft-based analyses can further refine operational low-level wind shear risk thresholds. Full article

High-sided vehicle safety on exposed long-span bridges depends on local wind direction as well as wind speed. Using a previously validated full-scale computational fluid dynamics model of the Queensferry Crossing Bridge, this study presents a two-stage site-specific assessment for double-decker buses. First, 50-year return period hourly means and one-second gust wind conditions from twelve directions are used to screen for the most adverse aerodynamic response. Within the present modelling framework, directions of 210°, 240°, 270° and 300° produce the largest changes in side force, lift force and rolling moment and are selected for further analysis. Second, a threshold-based operational assessment is carried out for these directions using the existing 60 mph gust closure threshold for double-decker buses and a moving-bus URANS (Unsteady Reynolds-Averaged Navier–Stokes) model. Safety factors for sideslip and overturning are evaluated to assess whether direction-dependent local wind effects create different levels of vulnerability under the same threshold. Additional simulations varying gust speed and bus speed examine the trade-off between local wind effects and vehicle speed in the most adverse direction. The framework links directional screening with operational safety assessment and shows that the same 60 mph gust threshold can correspond to materially different risk levels and mechanisms depending on wind direction. Full article

In order to reduce aviation’s negative environmental effects and support international efforts to battle climate change, the International Civil Aviation Organization (ICAO) seeks to cut greenhouse gas (GHG) emissions. About 2–3% of the world’s CO₂ emissions come from aviation, and at high altitudes, the fraction of other GHGs that significantly alter the atmosphere is considerably greater. In this study, hourly wind speed data at 100 m height from ECMWF’s fifth-generation reanalysis (ERA-5) were used over a period of 40 years (1985–2025). Hourly assessments of wind speeds at 40 m and 80 m heights are conducted in ERA-5, with biases at specific ground locations rectified via the Global Wind Atlas (GWA). This research estimates and analyzes many factors, including Weibull statistical parameters, daily and monthly wind speed variations, cumulative distribution function (CDF), and atmospheric turbulence intensity. The energy generation from several wind turbine types at different elevations was assessed. The findings indicate that the examined location revealed fair potential for the construction of large-capacity wind energy units at heights equal to or above 80 m. Turbines that are less than 50 m tall are spread out at least 10 km around the airport runway. While turbines that are less than 150 m tall are spread out at least 15 km away from the airport runway. Full article

The success of wind energy projects depends on reliable site selection and cost-effective operation. Existing studies largely focus on either resource potential or standalone economic feasibility, while a unified wind power rating framework for site prioritization across India remains lacking. This study proposes a multi-criteria wind power assessment framework and investigates the spatial and scale-dependent variability of wind speed (WS) and wind power density (WPD) over six major regions of India. Hourly WS data were at diurnal, monthly and annual scales to capture atmospheric and seasonal influences. The results reveal significant temporal variabilities in WS and WPD, especially over the southern and western coastal and high-altitude regions during the monsoon months (June–August). The spatial analysis revealed a non-linearly increasing trend for WS with altitude, contrary to the simplifying assumptions. Regions such as the Southern Peninsular States (SPSs) and western middle states (WMSs) show high suitability for large-scale deployment, whereas the Northeastern States (NESs) and parts of northern border states (NBS) exhibit lower potential. The site suitability is further evaluated using wind variability indices such as the wind variability index (WVI) and Windy Site Identifier (WSI), along with the plant capacity factor (PCF), cost of energy (COE), and greenhouse gas (GHG) emissions, enabling a comprehensive and decision-oriented framework for wind energy planning. Full article

This study presents the results of a one-month validation campaign focused on wind field reconstruction using a dual-scanning LiDAR configuration. The measurement campaign was conducted inland, approximately 6 km from the Thracian Sea coast in northeastern Greece, and involved the deployment of two scanning LiDAR units alongside a reference meteorological mast. Wind conditions were measured at 82 m above ground level, enabling spatially resolved reconstruction of horizontal wind speed and direction. To investigate the sensitivity of wind field reconstruction to probe volume effects, two range gate length configurations—100 m and 200 m—were systematically alternated during the campaign. This alternating strategy enabled a direct comparison under identical atmospheric conditions. The reconstructed wind speed and direction data exhibited excellent agreement with the reference measurements, achieving a coefficient of determination greater than 0.99, and showed negligible systematic bias. Analysis of turbulence characteristics revealed that the dual-scanning LiDAR system underestimated turbulence intensity compared to the reference meteorological mast. These findings underscore the effectiveness of this technology as a cost-efficient and accurate method for coastal wind characterization. Owing to its ability to reliably reconstruct wind fields over long distances, the system holds strong potential for near-shore and offshore applications. Full article

With the advancement of China’s “dual carbon” targets, Desert–Gobi–Wilderness (DGW) regions have become strategic areas for large-scale renewable energy deployment. However, the intermittency and variability of wind and solar resources pose challenges to power system stability, necessitating systematic evaluation of their characteristics and complementarity. This study uses ERA5 reanalysis data (2013–2023) to assess wind and solar resources in the Badain Jaran and Kumtag Deserts. A multi-dimensional framework is developed, incorporating availability, intermittency, variability, and complementarity, and a GIS-based multi-criteria decision-making method is applied for site selection. Results show that the Badain Jaran Desert is characterised by strong wind resources (average wind power density: 235.16 W/m²) and is suitable for wind-dominated development, whereas the Kumtag Desert exhibits superior solar resources (221.08 W/m²), favouring photovoltaic deployment. Significant wind–solar complementarity is identified, particularly in the central-western Badain Jaran and northeastern Kumtag regions. Three high-suitability sites were identified, including two in the Badain Jaran Desert and one in the Kumtag Desert, all characterised by favourable topographic conditions and high engineering feasibility. This study provides a scientific basis and a methodological framework for the planning of wind–solar hybrid systems and coordinated ecological development in DGW regions. Full article

Offshore wind is expanding globally, driven by energy security and decarbonization goals. Brazil’s world-class potential for this resource is challenged by its unique context: an already clean electricity matrix and abundant, low-cost onshore alternatives, which reduce the immediate urgency for deployment. This paper starts with a global offshore wind market analysis, understanding why the main countries pursue this technology, in contrast with Brazil’s already high share of renewable generation. The following examination focuses on Brazil’s recently approved new offshore wind framework and the governance-related issues, revealing that the legislative process was distorted by unrelated riders mandating costly, non-competitive energy procurement. These riders threatened to absorb future market growth, undermining competition and jeopardizing the emergence of the entire offshore wind industry. While presidential vetoes of these riders were essential to preserve this opportunity, remaining market distortions still favor mature technologies. The study concludes that Brazil’s primary barrier to offshore wind is not technical or resource-based but institutional: the need for stable, transparent governance to foster a truly competitive and predictable policy environment. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

This paper introduces a new combination of hybrid energy storage in a split-shaft wind energy conversion system based on a hydraulic transmission system. In the hybrid energy storage, a flywheel, supercapacitor, and battery are integrated into the wind energy conversion system with minimal additional supporting hardware. The split-shaft configuration allows the direct connection of the flywheel to the doubly fed induction generator (DFIG) shaft without a power electronic converter. The principal operation and minimization of this hybrid storage, as well as the energy management strategy, are explained. The goal is to smooth out output power fluctuations using the response surface method. A 1.5 MW hydraulic wind turbine is simulated in Matlab 23, and the hybrid storage is configured and optimized. The direct connection of the flywheel facilitates reaching a suitable level of smoothness at a reasonable cost. The proposed configuration is compared with conventional storage, and the results demonstrate that the integrated hybrid energy storage reduces the annualized storage cost by 71%. Full article

Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re), and their laminar separation bubbles (LSB) significantly complicate aerodynamic predictions. No prior study has experimentally validated XFOIL, k-ω SST, and γ-Re_θ models against simultaneous lift, drag, and chord-wise pressure coefficient (Cp) measurements for the customized high-camber airfoil at Re = 68,000 (68k), 118,000 (118k), and 159,000 (159k). Lift, drag, and Cp distributions were measured experimentally. The γ-Re_θ model demonstrated superior performance, achieving a lift maximum absolute percent error of 1.6–3.4%, near-zero bias, and a coefficient of determination >0.99. It accurately captured the LSB pressure plateau at mid-chord, with mean gross-averaged Cp percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. The k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL is unreliable specifically for separated transitional flows at Re < 118k, but improves at Re = 159k. The experimental dataset and validated transition-sensitive RANS approach provide a foundation for low-Re airfoil and DAWT diffuser design. Future work should extend measurements below Re = 50k and above 200k, including post-stall conditions, and system-level design of DAWT. Full article

Energy plays an essential role in economic advancement for any nation. However, escalating worldwide energy demands coupled with environmental and climate change issues resulting from the excessive consumption of conventional energy sources highlight the importance of identifying sustainable energy resource alternatives. Jordan, with its very limited fossil-fuel resources, is actively expanding its energy mix by investing in renewable sources, particularly wind energy. Therefore, the current work provides an evaluation of the wind power potential of Gharandal town within Tafila governorate, in southern Jordan, using hourly wind data recorded at 90 m elevation within a one-year monitoring period. The investigation reveals that the Weibull distribution more accurately models the wind speed in Tafila compared to the Rayleigh distribution based on parameters estimated through the maximum likelihood approach. The investigation at 90 m also shows that the annual wind power is 296 W/m², indicating that Tafila has marginal suitability for wind potential (Class 2) under the Pacific Northwest Laboratory classification system and has fairly good and suitable conditions for installing a wind farm per the European Wind Energy Association classification system. Most of the time, the prevailing winds at Tafila originate from the west direction (i.e., 270°), accounting for 23% of all occurrences. Finaly, the Tafila region contains promising areas for wind energy generation, particularly with the implementation of modern wind turbine technologies. Full article

Synchronous machines used in wind turbines typically use rare earth permanent magnets (PMs) due to the possibility of high power densities and efficiencies. However, alternative non-PM topologies are gaining popularity due to the cost and supply volatility of PMs. Wound-field flux-switching machines (WFFSMs), although boasting high torque densities and being PM-free, have lower power densities than PM machines. However, a double-stator wound-field flux-switching machine (DSWFFSM) exemplifies even greater power density. This study investigates the application of DSWFFSMs for direct-drive wind applications. Furthermore, the performance of an optimized 3 MW DSWFFSM design is compared with a single-stator WFFSM design. Both designs are based on the volume of a single-stator PM flux-switching machine from the literature. Although the torque per weight for the DSWFFSM and the single-stator WFFSM are similar, the torque per volume for the DSWFFSM is shown to be significantly exceptional. The torque ripple in the DSWFFSM is also smaller, but the efficiency is slightly lower than the single-stator WFFSM. The DSWFFSM design, which is shown to be comparable to PM-based topologies in terms of power density, highlights a low-cost, sustainable, clean energy generator topology. Full article

Accurate simulation of landfalling typhoons is essential for urban resilience in the densely populated Pearl River Delta. Using Super Typhoon Mangkhut (2018) as a case study, this paper evaluates the Weather Research and Forecasting (WRF) model through a contribution analysis designed to disentangle the roles of surface layer, planetary boundary layer (PBL), urban canopy model (UCM), and eddy-coefficient/diffusion closure parameterizations in wind-hazard prediction. Model results are validated against observations at the Hong Kong Observatory headquarters (HKO) and King’s Park (KP) stations, demonstrating that the hierarchy of physical controls is strongly metric-dependent. Substantial and structured spread is found among the tested configurations. Controlled comparisons show that PBL selection is the primary driver of variability in peak timing and high-wind persistence, whereas surface-layer formulation and diffusion closure exert secondary but systematic influences by shifting distributional centers and reshaping variability and upper tails. Urban canopy effects are comparatively weaker in aggregate but become more apparent during the impact and recovery phases. Overall, the results confirm that no single parameterization is consistently optimal across all metrics and motivate a multi-objective physics-selection strategy, in which multi-physics ensembles are used to better represent uncertainty in wind-event duration and associated loading risks in complex urban environments. Full article

Effective fault diagnosis of wind turbine blades and rotating machinery is critical for ensuring operational reliability and reducing maintenance costs. This study introduces a healthy-reference modeling framework that combines physics-informed neural network (PINN) with deviation-based dynamic regime features for systematic fault detection. At first, healthy and faulty data are normalized, then PINN is trained solely on healthy data, creating a reference model that predicts normal behavior. Deviations between measured signals and the healthy-reference predictions are then analyzed to extract key dynamic regime features, including energy, stability, drift, intermittency, and persistence, capturing subtle variations caused by faults. An interpretable Support Vector Machine (SVM) classifier uses these features to identify fault types such as ball, inner race, outer race, crack, erosion, and unbalance. Classification is performed using dynamic feature combinations while energy is often used as the base feature. The result shows energy with persistence combination performance is better than other feature combinations, and fused features achieved higher accuracy for both datasets. The approach is validated on both bearing data and an experimental blade dataset, demonstrating strong performance across different mechanical systems. Comparative evaluation with three different approaches, including Cross-load Scalogram-based CNN, Spectrogram-based CNN, and Hybrid SVM, highlights that the proposed healthy reference framework offers a data-efficient, interpretable, and robust solution for fault detection. This work highlights the importance of modeling healthy dynamics before classification, capturing both how strong a fault is and how it behaves over time, which offers a practical approach for wind turbine condition monitoring with limited data. Full article