Search Results (229)

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

The inspection of power equipment is vital for maintaining the safe and reliable operation of power systems. Among various inspection tasks, the detection of defects in insulators and wind turbine blades holds particular importance. However, existing detection methods often suffer from limited accuracy, largely due to substantial scale variations among defect targets and the loss of features associated with small objects. To address these challenges, this paper proposes Inspection of Power Equipment-YOLO (IPE-YOLO), an enhanced defect detection algorithm based on the YOLOv8n framework. First, a Cross Stage Partial Multi-Scale Edge Information Enhancement (CSP_MSEIE) module is introduced to improve multi-scale feature extraction, enhancing the detection of targets with significant scale diversity while reducing computational complexity. Second, we reconstruct the neck network with a Context-Guided Spatial Feature Reconstruction for Feature Pyramid Networks (CGRFPN), which promotes cross-scale feature fusion and enriches the fine-grained details of small objects, thereby alleviating feature loss in deeper network layers. Finally, a Lightweight Shared Convolutional Detection Head (LSCD) is employed, leveraging shared convolutional layers to decrease model parameters and computational costs without sacrificing detection precision. Experimental results demonstrate that, compared to the baseline YOLOv8n model, IPE-YOLO improves defect detection accuracy for insulators and wind turbine blades by 2.6% and 2.9%, respectively, while reducing the number of parameters by 12.3% and computational costs by 24.7%. These results indicate that IPE-YOLO achieves a superior balance between accuracy and efficiency, making it well-suited for practical engineering deployments in power equipment inspection. Full article

In the global shift toward sustainable energy, enhancing the efficiency of renewable energy systems plays a pivotal role in advancing the Sustainable Development Goals. This study focuses on optimizing the design of a corrugated-flange diffuser integrated with a wind turbine to enhance its performance, particularly in low-wind conditions. While most previous research has examined wind farm performance at high wind speeds, the challenge of effective power extraction at low wind speeds remains largely unresolved. The potential of diffusers to enhance wind turbine efficiency under low-wind conditions has received limited investigation, with most prior studies focusing solely on empty diffuser configurations without turbine integration. In addition, the influence of flange geometry on diffuser performance remains largely unexplored. In this study, parametric analyses were conducted to identify the optimal diffuser design, followed by comparative performance evaluations of configurations with and without turbine integration, using computational fluid dynamics (CFD) simulations. The results show that integrating a turbine with the optimized corrugated-flange diffuser increased flow velocity by 67.85%, achieving an average of approximately 14 m/s around the blade region. In comparison, the optimized corrugated-flange diffuser alone increased flow velocity by 44%, from 4.5 m/s to 8.036 m/s. These findings highlight the potential of optimized diffuser designs to enhance small-scale wind turbine performance in low-wind conditions. Full article

Micro and small wind turbines (MAWTs) are increasingly integrated into residential and prosumer hybrid energy systems. However, their real-world performance often falls short of catalog specifications due to mismatched wind resources, siting limitations, and insufficient attention to human comfort. This paper presents a comprehensive decision-support framework for selecting the type and scale of MAWTs under actual local conditions. The energy assessment module combines aerodynamic performance scaling, wind speed-frequency modeling based on Weibull distributions, turbulence intensity adjustments, and component-level efficiency factors for both horizontal and vertical axis turbines. The framework addresses three key design objectives: efficiency—aligning turbine geometry and control strategies with local wind regimes to maximize energy yield; comfort—evaluating candidate designs for noise emissions, shadow flicker, and visual impact near buildings; and climate adaptation—linking turbine siting, hub height, and rotor type to terrain roughness, turbulence, and built environment characteristics. Case studies from low and moderate wind locations in Central Europe demonstrate how multi-criteria filtering avoids oversizing, improves the autonomy of hybrid PV–wind systems, and identifies configurations that may exceed permissible limits for noise or flicker. The proposed methodology enables evidence-based deployment of MAWTs in decentralized energy systems that balance technical performance, resilience, and occupant well-being. Full article

Vertical axis wind turbines (VAWTs) are increasingly utilized for decentralized power generation in urban and low-wind settings because of their omnidirectional wind capture and compact form. This study numerically investigates the aerodynamic performance of Darrieus-type VAWT blades as their curvature varies systematically from deeply convex (−50 mm) to strongly concave (+50 mm) across seven configurations. Using steady-state computational fluid dynamics (CFD) with the frozen rotor method, simulations were conducted over a low Reynolds number range of 25 to 300, representative of small-scale and rooftop wind scenarios. The results indicate that deeply convex blades achieve the highest lift-to-drag ratio (Cl/Cd), peaking at 1.65 at Re = 25 and decreasing to 0.76 at Re = 300, whereas strongly concave blades show lower and more stable values ranging from 0.95 to 0.86. The power coefficient (Cp) and torque coefficient (Ct) similarly favor convex shapes, with Cp starting at 0.040 and remaining above 0.030, and Ct sustaining a robust 0.067 at low Re. Convex blades also maintain higher tip speed ratios (TSR), exceeding 1.30 at Re = 300. Velocity and pressure analyses reveal that convex profiles promote stable laminar flows and compact wakes, whereas concave geometries experience early flow separation and fluctuating torque. These findings demonstrate that optimizing the blade curvature toward convexity enhances the start-up, torque stability, and power output, providing essential design guidance for urban VAWTs operating under low Reynolds number conditions. Full article

Remote sensing has emerged as a promising technology for large-scale detection and updating of global wind turbine databases. High-resolution imagery (e.g., Google Earth) facilitates the identification of offshore wind turbines (OWTs) but offers limited offshore coverage due to the high cost of capturing vast ocean areas. In contrast, medium-resolution imagery, such as 10-m Sentinel-2, provides broad ocean coverage but depicts turbines only as small bright spots and shadows, making accurate detection challenging. To address these limitations, We propose a novel deep learning approach to capture the variability in OWT appearance and shadows caused by changes in solar illumination and satellite viewing geometry. Our method learns intrinsic, imaging geometry-invariant features of OWTs, enabling robust detection across multi-seasonal Sentinel-2 imagery. This approach is implemented using Faster R-CNN as the baseline, with three enhanced extensions: (1) direct integration of imaging parameters, where Geowise-Net incorporates solar and view angular information of satellite metadata to improve geometric awareness; (2) implicit geometry learning, where Contrast-Net employs contrastive learning on seasonal image pairs to capture variability in turbine appearance and shadows caused by changes in solar and viewing geometry; and (3) a Composite model that integrates the above two geometry-aware models to utilize their complementary strengths. All four models were evaluated using Sentinel-2 imagery from offshore regions in China. The ablation experiments showed a progressive improvement in detection performance in the following order: Faster R-CNN < Geowise-Net < Contrast-Net < Composite. Seasonal tests demonstrated that the proposed models maintained high performance on summer images against the baseline, where turbine shadows are significantly shorter than in winter scenes. The Composite model, in particular, showed only a 0.8% difference in the F1 score between the two seasons, compared to up to 3.7% for the baseline, indicating strong robustness to seasonal variation. By applying our approach to 887 Sentinel-2 scenes from China’s offshore regions (2023.1–2025.3), we built the China OWT Dataset, mapping 7369 turbines as of March 2025. Full article

Due to the structural aspects of the wind turbine, such as wind shear and tower shadow effects, the output power of the wind turbine has periodic fluctuations, known as 3P fluctuations. These fluctuations can reduce overall power generation and deteriorate power quality. In this context, this paper proposes a power smoothing control method that utilizes rotor inertia and a DC-link capacitor as small-scale energy storage devices. First, the typical energy storage capacities of the rotor’s rotational kinetic energy and the DC-link capacitor’s electrostatic energy are analyzed to assess their smoothing potential. Secondly, a control method is presented to apply the rotor and the DC-link capacitor as small-scale energy storage, with the smoothing frequency range allocated according to their respective storage capacities. Finally, the proposed method is compared with the conventional maximum power point tracking (MPPT) method and the 3P-notch filter method. The effectiveness of the proposed algorithm is verified through MATLAB/Simulink simulations, demonstrating its capability to mitigate periodic power fluctuations. The results showed that the proposed control method is applicable, reliable, and effective in mitigating periodic power fluctuations. Full article

The calculation and evaluation of wave loads represent a critical component in the design process of offshore wind turbines, which is of significant value for ensuring the safety and stability of offshore wind turbines during operation. In recent years, as the offshore wind power industry has extended into deep-sea areas, wind turbines and their foundation structures have gradually increased in scale. Due to the continuously growing diameter of fixed foundation structures, the wave loads they endure can no longer be evaluated solely by traditional methods. This study simplifies the monopile foundation structure of wind turbines into an upright circular cylinder. The open-source CFD platform OpenFOAM is employed to establish a numerical wave tank, and large eddy simulation (LES) models are used to conduct numerical simulations of its force-bearing process in wave fields. Through this approach, the hydrodynamic loads experienced by the single-cylinder structure in wave fields and the surrounding wave field data are obtained, with further investigation into its hydrodynamic characteristics under different wave environments. By analyzing the wave run-up distribution around cylinders of varying diameters and their effects on incident waves, a more suitable value range for traditional theories in engineering design applications is determined. Additionally, the variation laws of horizontal wave loads on single-cylinder structures under different parameter conditions (such as cylinder diameter, wave steepness, water depth, etc.) are thoroughly studied. Corresponding hydrodynamic load coefficients are derived, and appropriate wave force calculation methods are established to address the impact of value errors in hydrodynamic load coefficients within the transition range from large-diameter to small-diameter cylinders in traditional theories on wave force evaluation. This contributes to enhancing the accuracy and practicality of engineering designs. Full article

Tree belts are commonly applied over agricultural terrain where seeds of wheat and other vegetation are planted in the ground in order to prevent the seeds from being blown by the wind. The tree belt comprises a long and thin (10–20 m thick) section of trees, which spans in a direction normal to the prevailing wind direction. While serving its agricultural goal, the belt does inevitably modify the boundary layer profile of the wind. This, on its part, is likely to affect the operation of small-scale wind turbines installed in the vicinity of the belt. The goal of this study is to determine the span and range at which this effect manifests itself. It was found that in the near vicinity downstream and slightly above the tree belt, the wind velocity actually increased due to the mass conservation. The flow became independent on the tree belt drag coefficient when its value was higher than 0.2 1/m. The turbulence introduced by the belt was restricted to a height of 1.5–2 tree belts. Full article

This paper presents a real-time remote-control platform for small wind turbines (SWTs) equipped with a permanent magnet synchronous generator (PMSG). The proposed system integrates a DC–DC boost converter controlled by an Arduino^® microcontroller, a Raspberry Pi^® hosting a WebSocket server, and a desktop application developed using MATLAB^® App Designer (version R2024b). The platform enables seamless remote monitoring and control by allowing upper layers to select the turbine’s operating mode—either Maximum Power Point Tracking (MPPT) or Power Curtailment—based on real-time wind speed data transmitted via the WebSocket protocol. The communication architecture follows the IEC 61400-25 standard for wind power system communication, ensuring reliable and standardized data exchange. Experimental results demonstrate high accuracy in controlling the turbine’s operating points. The platform offers a user-friendly interface for real-time decision-making while ensuring robust and efficient system performance. This study highlights the potential of combining open-source hardware and software technologies to optimize SWT operations and improve their integration into distributed renewable energy systems. The proposed solution addresses the growing demand for cost-effective, flexible, and remote-control technologies in small-scale renewable energy applications. Full article

In recent years, the twin-barge float-over method has been widely used in offshore installations. This paper conducts numerical simulation and experimental research on the twin-barge float-over installation of offshore wind turbines (TBFOI-OWTs), focusing primarily on seakeeping performance, and also explores the influence of the gap distance on the hydrodynamic behavior of TBFOI-OWTs. Model tests are conducted in the ocean basin at Tsinghua Shenzhen International Graduate School. A physical model with a scale ratio of 1:50 is designed and fabricated, comprising two barges, a truss carriage frame, two small wind turbines, and a spread catenary mooring system. A series of model tests, including free decay tests, regular wave tests, and random wave tests, are carried out to investigate the hydrodynamics of TBFOI-OWTs. The experimental results and the numerical results are in good agreement, thereby validating the accuracy of the numerical simulation method. The motion RAOs of TBFOI-OWTs are small, demonstrating their good seakeeping performance. Compared with the regular wave situation, the surge and sway motions in random waves have greater ranges and amplitudes. This reveals that the mooring analysis cannot depend on regular waves only, and more importantly, that the random nature of realistic waves is less favorable for float-over installations. The responses in random waves are primarily controlled by motions’ natural frequencies and incident wave frequency. It is also revealed that the distance between two barges has a significant influence on the motion RAOs in beam seas. Within a certain range of incident wave periods (10.00 s < T < 15.00 s), increasing the gap distance reduces the sway RAO and roll RAO due to the energy dissipated by the damping pool of the barge gap. For installation safety within an operating window, it is meaningful but challenging to have accurate predictions of the forthcoming motions. For this, this study employs the Whale Optimization Algorithm (WOA) to optimize the Long Short-Term Memory (LSTM) neural network. Both the stepwise iterative model and the direct multi-step model of LSTM achieve a high accuracy of predicted heave motions. This study, to some extent, affirms the feasibility of float-over installation in the offshore wind power industry and provides a useful scheme for short-term predictions of motions. Full article

Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems. Full article

This study explores the potential of wind energy to address electricity shortages in South Africa, focusing on the Ekuphumleni community in Whittlesea. Given the challenges of expanding the national grid to these areas, wind energy is considered to be a feasible alternative to provide clean, renewable energy and reduce fossil fuel dependence in this community. This research evaluates wind potential utilizing the two-parameter Weibull distribution, with scale and shape parameters estimated by five traditional numerical methods and one metaheuristic optimization technique: whale optimization algorithm (WOA). Goodness-of-fit tests, such as the coefficient of determination (R²) and wind power density error (WPDE), were utilized to determine the best method for accurately estimating Weibull scale and shape parameters. Furthermore, net fitness, which combines R² and WPDE, was employed to provide a holistic assessment of overall performance. Whittlesea showed moderate wind speeds, averaging 3.88 m/s at 10 m above ground level (AGL), with the highest speeds in winter (4.87 m/s) and optimum in July. The WOA method outperformed all five numerical methods in this study in accurately estimating Weibull distribution parameters. Interestingly, the openwind method (OWM), a numerical technique based on iterative methods, and the Brent method showed comparable performance to WOA. The wind power density was 67.29 W/m², categorizing Whittlesea’s potential as poor and suitable for small-scale wind turbines. The east wind patterns favor efficient turbine placement. The study recommends using augmented wind turbines for the site to maximize energy capture at moderate speeds. Full article

This study examines output energy and efficiency of a torsional flutter harvester in gusty winds. The proposed apparatus exploits the torsional flutter of a rigid flapping foil, able to rotate about a pivot axis located in the proximity of the windward side. The apparatus operates at the “meso-scale”; i.e., the apparatus’ projected area is equal to a few square meters. It has unique properties in comparison with most harvesting devices and small wind turbines. The reference geometric chord length of the flapping foil is about one meter. Energy conversion is achieved by an adaptable linkage connected to a permanent magnet that produces eddy currents in a multi-loop winding coil. Operational conditions and the post-critical flutter regime are investigated by numerical simulations. Several configurations are examined to determine the output power and to study the effects of stationary turbulent flows on the energy-conversion efficiency. This paper is a continuation of recent studies. The goal is to examine the operational conditions of the apparatus for a potentially wide range of applications and moderate mean wind speeds. Full article

This study investigates the integration of three types of vertical-axis wind turbines (VAWTs)—helical, IceWind, and a combined design—on residential buildings in Çeşme, Türkiye, a region with an average wind speed of 7 m/s. The research explores the potential of small-scale wind turbines in urban areas, providing sustainable solutions for renewable energy generation and reducing reliance on conventional energy sources. The turbines were designed and analyzed using SolidWorks and ANSYS Fluent, achieving power outputs of 350 W for the helical turbine, 430 W for the IceWind turbine, and 590 W for the combined turbine. A total of 42 turbines were mounted on a five-storey residential building model, and DesignBuilder software was utilized to simulate and evaluate the energy consumption. The baseline energy consumption of 172 kWh/m² annually was reduced by 18.45%, 22.93%, and 30.88% for the helical, IceWind, and combined turbines, respectively. Furthermore, the economic analysis showed payback periods of 12.89 years for the helical turbine, 10.60 years for the IceWind turbine, and 10.49 years for the combined turbine. These findings emphasize the viability of integrating VAWTs into urban buildings as an effective strategy for reducing energy consumption, lowering costs, and enhancing energy efficiency. Full article