Search Results (105)

Strategies to reduce global warming by limiting the consumption of fossil fuels are expected in part to include the integration of various renewable energies in residential buildings, which emit a considerable volume of greenhouse gases. Wind turbines are an important technology for moving toward this goal, as they are especially effective in isolated or rural areas for small-grid power systems. However, the approach requires that they be miniaturized, and that the production of wind blades be more convenient. The present study proposes a maple seed-shaped blade mimicking the practical shape and performance of a toy, in which the blade is so simple it can just be bent, eliminating the cumbersome production process of current propeller-type blades. We demonstrate with visualization, experiments of the flow characteristics, and numerical analysis that the power coefficient C_p of the wind turbine with micrometer-scale maple seed blades (7.5 cm long) was superior to that of the one using a propeller-type blade. A better C_p was observed with maple seed-shaped blades than with rectangular plates. This was found to be due to the behavior of the vortex near the blade. The performance of the maple seed wind turbine provides perspective on the development of microscale wind turbine-inspired dual-mode possessing characteristics of both lift and drag wind turbines. Full article

Waste from the wind power and textile industries poses major environmental challenges. While the textile industry is a significant global contributor to waste, producing around 92 million tons of waste annually, and greenhouse gas emissions, wind power, although one of the cleanest energy sources during operation, still generates waste and associated CO₂ emissions, particularly associated with the end-of-life decommissioning of turbine blades. This waste can be reused, combined with bio-based binders, to reduce the construction sector’s long-term environmental impact. The present work identifies research trends and gaps in the use of these waste materials, either individually or combined, for the development of thermal and acoustic insulation solutions for the construction sector, by means of a combined bibliometric and content analysis of Scopus and Web of Science documents from 2014 to 2025. The study focuses on bibliometric indicators and reports on physical properties (thermal conductivity, density, mechanical strength, and acoustic performance) of the resulting composites, including those produced with bio-binders. Additionally, a qualitative review of life cycle assessment studies indicates that bio-based and waste-derived insulation materials can significantly reduce environmental impacts compared with conventional mineral or petrochemical insulators. Results reveal growing scientific interest in this subject, highlighting an annual publication growth of 5.09%. They emphasize the performance of natural textile fibers in thermal and acoustic insulation, the mechanical capacity of synthetic fibers, and the semi-structural potential of fiberglass composites. Meanwhile, bio-binders improve the upcycling of textile waste; however, they reveal a significant research gap in the integration of wind turbine blade waste into insulation composites. No indexed studies were found that simultaneously combine textile waste, blade-derived fibers, and bio-based binders in a single insulation system, despite projected cumulative blade waste of 43 million tons by 2050. These findings advocate hybrid innovations and standardized assessments to drive circular economy and low-carbon building solutions. Full article

The increasing demand for sustainable energy in residential buildings and public concerns on greenhouse gas (GHG) emissions has driven the integration of smart homes with hybrid renewable energy systems (HRESs). This research proposes an optimal scheduling strategy for home energy consumption in a grid-connected HRES that comprises a grid, wind turbines, photovoltaics and battery storage systems. The objective of the study is to reduce the net energy cost, scheduling inconvenience cost (SIC), GHG cost and battery degradation cost. An ant colony optimization algorithm is utilized in the MATLAB environment, with load profiles and meteorological data of Upington, South Africa, obtained from NASA and a residential consumption dataset to accomplish the objectives of the study. The outcomes of the study show that case study 3 is the most feasible configuration based on a net energy revenue cost of $9.8382, GHG cost of $0.0627, battery degradation cost of $0.461 and SIC of $0.66. Simulation results demonstrate that energy purchased from the grid has been reduced by 98% and 48% relative to case studies 1 and 2. The results of the study can assist households to improve the sustainability and resilience of the power system in residential environments where the grid supply is unstable and electricity costs are high. Full article

The depletion of fossil fuel resources and the growing need for sustainable energy solutions have increased interest in vertical axis wind turbines (VAWTs), which offer advantages in urban and variable-wind environments but often exhibit limited performance at low tip speed ratios (TSRs). This study optimizes VAWT aerodynamic behavior across a wide TSR range by varying three geometric parameters: maximum thickness position ($a/b$), relative thickness (m), and pitch angle ($\beta$). A two-dimensional computational fluid dynamics (CFD) framework, combined with the Metamodel of Optimal Prognosis (MOP), was used to build surrogate models, perform sensitivity analyses, and identify optimal profiles through gradient-based optimization of the integrated $C_p$–$\lambda$ curve. The Joukowsky transformation was employed for efficient geometric parameterization while maintaining aerodynamic adaptability. The optimized airfoils consistently outperformed the baseline NACA 0021, yielding up to a 14.4% improvement at $\lambda =2.64$ and an average increase of 10.7% across all evaluated TSRs. Flow-field analysis confirmed reduced separation, smoother pressure gradients, and enhanced torque generation. Overall, the proposed methodology provides a robust and computationally efficient framework for multi-TSR optimization, integrating Joukowsky-based parameterization with surrogate modeling to improve VAWT performance under diverse operating conditions. Full article

To address the scientific gap concerning optimal urban wind turbine morphology, this work presents an experimental performance comparison between two small-scale wind turbine designs: a conventional three-blade horizontal-axis wind turbine (HAWT) and a duct-equipped Ayanz-inspired screw-blade turbine. Both configurations were tested in a controlled wind tunnel under steady and transient wind conditions, including synthetic gusts designed to emulate urban wind patterns. The analysis focuses on power output, aerodynamic efficiency (via the power coefficient $C_P$), dynamic responsiveness, and integration suitability. A key novelty of this study lies in the full-scale experimental comparison between a non-conventional Ayanz screw-blade turbine and a standard three-blade turbine, since experimental data contrasting these two geometries under both steady and gusty urban wind conditions are extremely scarce in the literature. Results show that while the three-blade turbine achieves a higher $C_P$ peak and greater efficiency near its optimal operating point, the Ayanz turbine exhibits a broader performance plateau and better self-starting behavior under low and fluctuating wind conditions. The Ayanz model also demonstrated smoother power build-up and higher energy capture under specific gust scenarios, especially when wind speed offsets were low. Furthermore, a methodological contribution is made by comparing the $C_P$ vs. tip speed ratio λ curves at multiple wind speeds, providing a novel framework (plateau width analysis) for realistically assessing turbine adaptability and robustness to off-design conditions. These findings provide practical insights for selecting turbine types in variable or urban wind environments and contribute to the design of robust small wind energy systems for deployments in cities. Full article

Offshore wind energy is a key enabler of the global net-zero transition. As nearshore fixed-bottom projects reach maturity, floating offshore wind turbines (FOWTs) are becoming the next major focus for large scale deployment. To accelerate this development and reduce construction costs, it is essential to optimize mooring systems through a systematic and performance driven framework. This study focuses on the mooring assessment of the Taiwan-developed DeltaFloat semi-submersible platform supporting a 15 MW turbine at a 70 m water depth offshore Hsinchu, Taiwan. A full-chain catenary mooring system was designed based on site specific metocean conditions. The proposed framework integrates ANSYS AQWA (version 2024 R1) and Orcina OrcaFlex (version 11.5) simulations with sensitivity analyses and performance-based fitness metrics including offset, inclination, and line tension to identify key parameters governing mooring behavior. Additionally, an analysis of variance (ANOVA) was conducted to quantitatively evaluate the statistical significance of each design parameter. Results indicate that mooring line length is the most influential factor affecting system performance, followed by line angle and diameter. Optimizing these parameters significantly improves platform stability and reduces tension loads without excessive material use. Building on the optimized symmetric configuration, an asymmetric mooring concept with unequal line lengths is proposed. The asymmetric layout achieves performance comparable to traditional 3 × 1 and 3 × 2 systems under extreme environmental conditions while demonstrating potential reductions in material use and overall cost. Nevertheless, the unbalanced load distribution highlights the need for multi-scenario validation and fatigue assessment to ensure long-term reliability. Overall, the study establishes a comprehensive and sensitivity-based evaluation framework for floating wind mooring systems. The findings provide a balanced and practical reference for the cost-efficient design of floating offshore wind farms in the Taiwan Strait and other shallow-water regions. Full article

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. 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

Wind power is a major source of renewable energy, yet turbine performance is strongly influenced by atmospheric conditions and surrounding terrain. Several meteorological phenomena can hinder energy production, disrupt operations, and accelerate structural deterioration. This paper reviews three key atmospheric hazards affecting wind turbine systems: lightning, icing, and rain. For each phenomenon, the formation mechanisms, operational effects, and mitigation approaches are examined, with offshore-specific processes and conditions integrated directly into each hazard discussion. Building on this foundation, the review then analyses interactions between the hazards, their combined implications for turbine performance and maintenance, and the associated economic impacts. Comparisons of material behaviour across lightning, icing, and rain-erosion conditions are also incorporated. Finally, future research directions are proposed. Full article

The rapid expansion of offshore wind energy requires exploring alternative turbine architectures capable of operating efficiently in deep waters. While horizontal-axis wind turbines (HAWTs) dominate the current market, vertical-axis wind turbines (VAWTs) offer potential advantages in wake recovery, structural integration, and scalability on floating platforms. This work proposes a methodological framework to enable a fair and reproducible comparison between the two concepts. The approach begins with site selection through spatial exclusion criteria, followed by acquisition and validation of wind data over at least one year, including long-term correction with reanalysis datasets. Technical specifications of both HAWTs and VAWTs (power curves, thrust coefficients, and rotor geometries) are compiled to build consistent turbine models. Wind resource characterization is carried out using sectoral Weibull distributions, energy roses, and vertical wind profiles. Annual energy production (AEP) for HAWTs is estimated with WAsP, while VAWT performance requires geometric normalization to a common top-tip height and subsequent correction factors for air density, turbulence sensitivity, and wake recovery. Case studies demonstrate that corrected AEP values for VAWTs may exceed baseline WAsP estimates by 6–20%, narrowing the performance gap with HAWTs. The framework highlights uncertainties in wake modeling and calls for dedicated computational fluid dynamics (CFD) validation and pilot projects to confirm large-scale VAWT viability. Full article

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article

To study the bearing capacity of large width-to-height ratio (LWHR) wind power spread foundations on onshore sandy soils, this study takes a 2 MW unit foundation of a specific wind farm as the object, conducts its sustainable design optimization, structural design and bearing capacity analysis, and explores internal force variation patterns of the structure and foundation. First, it investigates interactions among foundation soil stiffness, foundation deformation, and width-to-height ratio, and proposes the ratio can be slightly over 2.5 under specific geological conditions. Then, it verifies the foundation’s design and bearing capacity via code-based methods and uses ABAQUS to build an integrated finite element model (foundation, reinforcement, foundation ring, soil) for analyzing the mechanical behavior of the overall structure and key components. Finally, it focuses on bending moments of the foundation’s top/bottom slab reinforcement and reaction force distribution, and compares results from code formulas, commercial software, and finite element analysis. The results show that foundation deformation relates to the outer cantilever’s width-to-height ratio, reaction force magnitude, and soil stiffness. Soil stiffness impacts the linear distribution of reaction forces more significantly than the ratio—when soil stiffness is smaller, reaction forces still meet the linear assumption even if the ratio exceeds 2.5 within a range; when larger, they differ greatly. Under specific sandy soils (bearing stratum capacity < 300 kPa), the LWHR foundation meets wind turbine requirements, and its foundation ring’s punching shear resistance complies with standards (considering uplift-resistant reinforcement). For coarse sand bearing strata, the foundation’s bottom pressure follows linear distribution per code, and code formulas apply. However, the LWHR scheme is not fully suitable for complex geology or other foundation types, whose applicability needs comprehensive analysis. Full article

The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs wind energy systems’ stability and sustainability. This research introduces a novel spatio-temporal hybrid model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and graph convolutional networks (GCN) to extract temporal patterns and meteorological dynamics (wind speed, direction, temperature) across 134 wind turbines. Building upon conventional methods, our architecture captures turbine spatio-temporal correlations while assimilating multivariate meteorological characteristics. Addressing data integrity compromises from equipment failures and extreme weather-which undermine data-driven models-we implement Wasserstein GAN (WGAN) for generative missing-value interpolation. Validation across severe data loss scenarios (30–90% missing values) demonstrates the model’s enhanced predictive capacity. Rigorous benchmarking confirms significant accuracy improvements and reduced forecasting errors. Full article

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

Water and energy are strongly intertwined, especially in wastewater treatment plants (WWTPs) whose electrical loads can strain local grids. This work evaluates the technical, economic, and environmental feasibility of powering the WWTP attached to the smart building of Ibn Tofail University (Morocco) with building-integrated photovoltaics (PV) and a complementary wind turbine. Using the HOMER Pro optimizer, two configurations were compared: (i) stand-alone PV and (ii) a hybrid PV/wind system. The hybrid design raises the renewable energy fraction from 8.5% to 17.9%, cutting annual grid purchases by 8% and avoiding 47.9 t CO₂ yr⁻¹. The levelized cost of electricity decreases from 1.08 to 0.97 MAD kWh⁻¹ (≈0.11 to 0.10 USD kWh⁻¹), while the net present cost drops by 6%. Sensitivity analyses confirm robustness under grid electricity tariff and load-growth uncertainties. These results demonstrate that modest wind additions can double the renewable share and improve economics, offering a replicable pathway for WWTPs and smart buildings across the MENA region. Full article