Search Results (2,607)

This study presents the first comprehensive techno-economic assessment of wind-based green hydrogen production across Senegal, a country highly dependent on fossil fuel imports. Using a novel integrated approach combining 30 years of ERA5 reanalysis data (1993–2023), turbine performance modeling and electrolyzer comparison, it fills an important gap for renewable hydrogen development in West Africa. Wind resources were analyzed at multiple altitudes, revealing strong potential in both coastal and northeastern regions, particularly during the dry season, with higher wind speeds at higher turbine heights. Four turbines (Vestas_150, Goldwind_155, Vestas_126 and Nordex_N100) and two electrolyzer types (alkaline and PEM) were evaluated. The alkaline system performed best. Vestas_150 and Goldwind_155 achieved the highest hydrogen yields of 241 and 183 tons/year and CO₂ reductions of 2951 and 2241 tons/year, generating carbon credits of 0.118 M$ and 0.089 M$, respectively. Their levelized cost of electricity remained low (0.042 and 0.039 $/kWh), while smaller turbines showed higher costs. Vestas_150 also had the shortest payback period of 2.16 years, making it the most competitive option. Sensitivity analyses showed that longer system lifespans and high-performance turbines significantly reduce the levelized cost of hydrogen. Priority investment zones include Saint-Louis, Matam, Louga and Tambacounda, with levelized cost of hydrogen values as low as 3.4 $/kg. Full article

The frictional resistance of impeller machinery blades such as aircraft engines, gas turbines, and wind turbines has a decisive impact on their efficiency and energy consumption. Inspired by the micro-tooth structure on the surface of shark skin, microstructural drag reduction technology has become a cutting-edge research direction for improving aerodynamic performance and a continuous focus of researchers over the past 20 years. However, the significant difficulty in fabricating microstructures on three-dimensional curved surfaces has led to the limited widespread application of this technology in engineering. Addressing the issue of drag reduction and efficiency improvement for small axial flow fans (local Reynolds number range: (36,327–40,330), this paper employs Design of Experiments (DOE) combined with high-precision numerical simulation to clarify the drag reduction law of bionic microgroove surfaces and determine the dimensions of bionic microstructures on fan blade surfaces. The steady-state calculation uses the standard k-ω model and simpleFoam solver, while the unsteady Large Eddy Simulation (LES) employs the pimpleFoam solver and WALE subgrid-scale model. The dimensionless height (h⁺) and width (s⁺) of microgrooves are in the range of 8.50–29.75, and the micro-grooved structure achieves effective drag reduction. The microstructured surface is fabricated on the suction surface of the blade via a spray coating process, and the dimensions of the microstructures are determined according to the drag reduction law of grooved flat plates. Aerodynamic performance tests indicate that the shaft power consumed by the bionic fan blades during the tests is significantly reduced. The maximum static pressure efficiency of the bionic fan with micro-dimples is increased by 2.33%, while that of the bionic fan with micro-grooves is increased by 3.46%. The fabrication method of the bionic microstructured surface proposed in this paper is expected to promote the engineering application of bionic drag reduction technology. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

The large-scale integration of doubly fed wind turbines reduces the inertia level of power systems and increases the risk of frequency instability. This paper analyzes the performance characteristics and application ranges of different types of energy storage technologies and addresses the limitations of conventional control methods, which cannot adjust energy storage power output in real time according to frequency variations and may hinder frequency recovery during the restoration stage. Based on a grid-forming doubly fed wind turbine model, this study adopts a hybrid ultracapacitor energy storage system as the auxiliary storage device. The hybrid configuration increases energy density and extends the effective support duration of the storage system, thereby meeting the requirements of longer-term frequency regulation. Furthermore, the paper proposes an adaptive inertia control strategy that combines an improved variable-K droop control with adaptive virtual inertia control to enhance the stability of doubly fed wind turbines under load fluctuations. Simulation studies conducted in MATLAB 2022/Simulink demonstrate that the proposed method significantly improves frequency stability in load disturbance scenarios. The strategy not only strengthens the frequency support capability of grid-connected wind turbine units but also accelerates frequency recovery, which plays an important role in maintaining power system frequency stability. Full article

Establishing a reduced-order model (ROM) of the wind turbine generator system (WTGS) preserving transient characteristics is a fundamental requirement for the transient stability analysis of power systems. This study introduces a novel dimension reduction framework based on trajectory eigenvalues, integrated with virtual inertia control (VIC). The framework facilitates multi-timescale state variable partitioning through a reversible mapping, which is derived from eigenvalue dominance and participation metrics. Based on this, dimension reduction is performed using singular perturbation theory (SPT). Taking a direct-drive wind turbine generator as an example, this paper establishes a ROM of the WTGS with VIC preserving transient characteristics, based on the proposed reduction method. Comprehensive time-domain simulations in MATLAB/Simulink validate the model’s accuracy and computational efficacy. Full article

This study presents a compact, 3D-printed Savonius wind turbine rotor incorporating pointed deflectors to enhance concave-side airflow and mitigate blade-edge vortex formation. The prototype, fabricated from ABS plastic, was experimentally evaluated in an Eiffel-type wind tunnel under low-speed wind conditions (3, 4, and 5 m/s), with blockage effects taken into account. Flow visualization revealed improved airflow attachment and pressure concentration on the concave blade surfaces, increasing drag asymmetry and torque generation. Corresponding power coefficients with applied blockage ratio were observed to be 0.181, 0.185 and 0.186, while torque coefficients with applied blockage ratio were observed to be 0.385, 0.374 and 0.375 at each wind speed and optimal tip-speed ratio, respectively, and were compared with previously reported computational results. The optimal operating tip-speed ratios identified for the torque and power coefficients were remarkably close, enabling efficient torque and power generation during operation. The experimental findings validate earlier numerical predictions and underscore the importance of physical testing in assessing turbine performance. Observed deviations between predicted and experimental coefficients suggest that fabrication parameters may influence prototype performance and warrant further investigation. Overall, the results demonstrate the technical viability of 3D-printed Savonius turbines for small-scale urban energy harvesting applications in the Philippines. Full article

This study presents a frequency-domain analysis of a finite-element (FEM)-based rotor–nacelle model for wind turbines, validated against the open-source time-domain tool OpenFAST. The analysis was carried out using METHOD, an in-house computational framework implemented in Python. While time-domain models remain standard for nonlinear aeroelastic simulations, frequency-domain approaches offer advantages in early-stage design, control development, and system identification due to their efficiency, transparency, and suitability for parametric studies. The FEM model includes flexible blades, hub, and nacelle dynamics and includes tower and fixed or floating platform components with rotor–tower frequency interactions. In this work, a fixed tower is considered to isolate rotor behaviour. Beam-element formulation enables the computation of natural frequencies, mode shapes, and frequency response functions, and an equivalent rotor model is implemented in OpenFAST for consistent benchmarking. Validation results show close correspondence between the two modelling approaches. Key operational parameters agree within 3%, while structural responses, including flap-wise deflection, bending moments, and resultant quantities, typically fall within an overall accuracy range of 5–15%, consistent with expected differences arising from reference-frame conventions and modelling assumptions. Discrepancies are discussed in terms of numerical damping, model assumptions (differences in the axis system), and the influence of structural simplifications. Overall, the FEM model captures the dominant dynamic behaviour with satisfactory accuracy and a consistent orientation of global response. Computational efficiency results further highlight the advantages of the METHOD framework. Wind-field generation is completed roughly an order of magnitude faster, and long-duration aeroelastic simulations achieve substantial speed-ups, reaching more than one order of magnitude for multi-hour cases, demonstrating strong scalability relative to OpenFAST. Overall, the results confirm that a well-constructed yet still simplified frequency-domain FEM rotor model can provide a robust and computationally efficient alternative to conventional time-domain solvers. Moreover, the computational performance presented here represents a lower bound, as further improvements are readily achievable through parallelisation and solver-level optimisation. Future papers will present the full-system aero-hydro-elastic coupling for fixed and floating offshore wind turbine applications. Full article

Wind power forecasting is critical to renewable energy generation, as accurate predictions are essential for the efficient and reliable operation of power systems. However, wind power output is inherently unstable and is strongly affected by meteorological factors such as wind speed, wind direction, and atmospheric pressure. Weather conditions and wind power data are recorded by sensors installed in wind turbines, which may be damaged or malfunction during extreme or sudden weather events. Such failures can lead to inaccurate, incomplete, or missing data, thereby degrading data quality and, consequently, forecasting performance. To address these challenges, we propose a method that integrates a pre-trained large-scale language model (LLM) with the spatiotemporal characteristics of wind power networks, aiming to capture both meteorological variability and the complexity of wind farm terrain. Specifically, we design a spatiotemporal graph neural network based on multi-view maps as an encoder. The resulting embedded spatiotemporal map sequences are aligned with textual representations, concatenated with prompt embeddings, and then fed into a frozen LLM to predict future wind turbine power generation sequences. In addition, to mitigate anomalies and missing values caused by sensor malfunctions, we introduce a novel frequency-domain learning-based interpolation method that enhances data correlations and effectively reconstructs missing observations. Experiments conducted on real-world wind power datasets demonstrate that the proposed approach outperforms state-of-the-art methods, achieving root mean square errors of 17.776 kW and 50.029 kW for 24-h and 48-h forecasts, respectively. These results indicate substantial improvements in both accuracy and robustness, highlighting the strong practical potential of the proposed method for wind power forecasting in the renewable energy industry. Full article

Electrical strain gauges are essential for monitoring wind turbine tower loads and fatigue, but accurate load measurements from these sensors require calibration over time to correct the zero-drift found in long-term measured signals. Calibration is often performed using nacelle rotation events for cable untwisting, where the tower mechanical load is known; however, non-uniform solar heating during these events can introduce thermal stresses that are misinterpreted as drift, causing systematic errors. This study evaluates six preprocessing methods for correcting zero-drift and thermal stresses in strain gauges, using measurements from two tower cross-sections—one with temperature sensors and one without. Performance is quantified using the scatter of the 10 min mean bending moments in the fore–aft and side-to-side directions and the cumulative fatigue damage over the monitoring periods. Results show that modelling the thermal stresses using a linear regression model with temperature measurements as inputs yields the most physically consistent load curves. If temperature measurements are unavailable, the effects of thermal stresses can be partly mitigated by restricting calibration to nighttime events or using solar-position variables in a regression model (instead of temperatures). As expected, the choice of preprocessing method significantly impacts load curves, but its influence on fatigue damage estimates is limited. Full article

The maintenance operations in the wind sector (particularly offshore) are extremely costly, and thus technologies for optimization of maintenance need to take the high risks related to these costs into account. On the topic of predictive maintenance, any false prediction can lead to high cost incursions, and even a few false predictions can invalidate the predictive maintenance strategy. The costs and benefits of correct and false predictions are not uniform or symmetric, leading the conventional performance metrics to misalign the perceived model performance with the actual business impact. This problem leads us to introduce a cost-aware deep learning framework that accounts for the costs and benefits of predictions. This framework is achieved by loss functions designed to adjust the parameters according to the cost–benefit and not to optimize model accuracy. For the case study of wind turbine generator gearbox failure prediction, several cost-aware loss functions are tested, along with a novel fine-tuning approach that combines them. The case study shows that using cost-aware expected-value loss functions improves cost–benefit performance by an average of 30–57% compared with models trained with conventional loss functions. Full article

Robust condition monitoring of wind turbine blades is essential for reducing downtime and maintenance costs, particularly under variable operating conditions. While recent studies suggest that combining trend monitoring (TM) with change point detection (CPD) can improve diagnostic performance, it remains unclear whether such integration is beneficial for all fault types. This study experimentally evaluates the integration of TM and CPD using vibration data from a laboratory-scale wind turbine for two representative blade faults: leading-edge erosion and twist misalignment. For the erosion case, discrete wavelet transform (DWT) energy features exhibit a clear and persistent increase in mid-frequency content, with energy deviations of approximately 34–45% relative to the healthy state. However, Bayesian Online Change Point Detection (BOCPD) does not reveal distinct change points, indicating that CPD provides limited additional value for gradual, steady-state degradation. In contrast, for twist misalignment, the short-time Fast Fourier Transform (FFT) features reveal dynamic spectral redistribution, and CPD applied to spectral centroid trends produces a sharp, localized detection signature. These results demonstrate that integrating TM with CPD significantly enhances fault detectability for dynamic, instability-driven faults, while TM alone is sufficient for smooth, steady-state degradation. This study provides an evidence-based guideline for selectively integrating CPD into wind turbine blade condition monitoring systems based on fault physics. Full article

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of $H_s$ = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height ($H_{s100}$ = 4.66 m, $T_{p100}$ = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, $H_{s100}$ = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia. Full article

Vertical-axis wind turbines (VAWTs) have received increasing research interest due to their structurally simple design and superior adaptability to gusty, multidirectional, and highly turbulent wind conditions. However, their relatively low efficiency of wind utilization remains a significant limitation, necessitating extensive research into design optimization and performance enhancement strategies. As we show, efficiency can be achieved by arranging the blades not evenly around the circumference, as in a traditional VAWT, but in groups called “blocks”, which extracts more energy from the air flow using aerodynamic and thermodynamic phenomena. The experimental results of a 20 kW VAWT in an independent certified laboratory strengthen the theoretical study and prove that the efficiency of the proposed system is 1.7 times higher than that of known VAWTs, as well as horizontal-axis wind turbines (HAWTs). Full article

Floating offshore wind turbines (FOWTs) are essential for meeting global renewable energy goals, yet their viability depends strongly on platform motion in harsh marine environments and the resulting influence on structural loading and the levelized cost of energy. This study examines the dynamic response of a 15 MW semi-submersible FOWT based on the IEA-15-240-RWT developed by NREL. The baseline UMaine VolturnUS-S platform is evaluated alongside two newly proposed variants, KSNU-1 15 MW and KSNU-2 15 MW, each equipped with distinct heave-plate configurations designed to enhance hydrodynamic damping while maintaining equal surface area for fair comparison. Hydrodynamic coefficients are obtained through potential-flow analysis using Ansys Aqwa, and fully coupled aero-hydro-servo-elastic simulations are conducted with OpenFAST. The performance of all platforms is assessed under two design load cases (DLCs): the fatigue limit state (FLS) and the ultimate limit state (ULS). The results show that both KSNU platforms achieve slight reductions in surge, sway, and heave motions, with KSNU-2 providing the most consistent improvement in vertical and horizontal stability. Rotational responses increase modestly but remain within acceptable limits. Overall, the KSNU-2 design demonstrates improved motion control without compromising energy output, offering a promising configuration for large-scale floating wind applications. Full article

Energy deficits have been a major challenge in Sub-Saharan Africa (SSA), particularly in Nigeria. Consequently, the integration of renewable energy (RE) is a crucial strategy for achieving energy transition goals and addressing climate change issues. Therefore, this article investigates the technical, energy, economic, and environmental impact of PV/Wind/BS/Converter, a standalone hybrid energy mix for electrifying a single-family residential building prototype in multi-regional parts of Nigeria. This study aims to examine the renewable energy potential of three locations using HOMER Pro. The results indicate that Kano exhibits the lowest economic performance indices, with a net present cost (NPC) of USD 32,212.52 and a cost of energy (COE) of USD 0.6072/kWh, followed by Anambra (NPC: USD 45,671.68; COE: USD 0.8609/kWh) and Lagos (NPC: USD 47,184.62; COE: USD 0.8706/kWh). Technically, this study shows that the higher the renewable potential of a site, the lower the energy cost and vice versa. The sensitivity cases of key energy parameters—including solar PV cost, wind turbine cost, wind speed, solar radiation, and inflation rate—were considered to compare multiple scenarios and assess renewable energy potential variability under certain decision-making conditions. Economically, the Kano system shows the feasible capital cost of the energy produced, replacement cost, and operation and maintenance cost (O&M) for wind turbines, compared to the nil cost for Anambra and Lagos. Environmentally, the energy systems revealed 100% renewable fractions (RFs) with zero emissions at the three sites under study, which can enhance Nigeria’s energy transition plan and help in achieving the Sustainable Development Goals. Integrating RE supports the successful implementation of the recommended energy policy strategies for Nigeria. Full article