Search Results (4,615)

Recent years have seen a considerable increase in clean, green electricity output from wind energy (WE). It is crucial to obtain the optimum parameters of the two-parameter Weibull distribution (TPWD) for wind speed (WS) to calculate the potential WE. This paper proposes to use the superEGO (SEGO) along with maximum likelihood estimation (MLE) to obtain optimum parameters of the TWPD for WS data. The results showed that SEGO provided better results compared other optimization algorithms used in this context. Moreover, the potential WE for Gdańsk, a city located by the Baltic Sea in northern Poland, was calculated using parameters obtained by using SEGO. It was observed that SEGO performs the best among other optimization algorithms to find optimum parameters for the two-parameter Weibull distribution along with MLE for wind speed. Full article

With the rapid development of solar thermal power generation technology, the structural stability of the dish solar concentrator system under complex wind environments has become a critical limiting factor for its large-scale application. This study investigates the flow field distribution and structural response under fluctuating wind loads using computational fluid dynamics (CFD). A three-dimensional model was developed and simulated in ANSYS Fluent under varying wind angles and speed cycles. The results indicate that changes in the concentrator’s orientation significantly influence the airflow field, with the most adverse effects observed at low elevation angles (0°) and an azimuth angle of 60°. Short-period wind loads (T = 25 s) exacerbate transient impact effects of lift forces and overturning moments, markedly increasing structural fatigue risks. Long-period winds (T = 50 s) amplify cumulative drag forces and tilting moments (e.g., peak drag of −73.9 kN at β = 0°). Key parameters for wind-resistant design are identified, including critical angles and period-dependent load characteristics. Full article

Measuring the long-term trend of PM2.5 mass-concentration in urban environments is essential as it has a direct impact on human health. PM2.5 levels depend not only on the intensity of local emission sources and on imported pollution, but also on meteorological conditions (e.g., anticyclonic versus windy conditions), which leads to yearly variations in mean PM2.5 values. Two datasets available for Paris, France, are considered: measurements from Airparif air quality agency network and from the Pollutrack network of mobile car-based sensors. Also, meteorological parameters coming from ERA5 analysis (ECMWF) are considered. Annual values are calculated using three different statistical methods, which yield different results. For the 2013–2024 period, a clear relationship between wind speed and PM2.5 mass-concentration levels is established. The results show a linear decrease in both concentration and standard deviation for wind speeds in the 0–6 m·s⁻¹ range, followed by nearly stable values for wind speed above 6 m·s⁻¹. This behavior is explained by the dispersive effect of strong winds on air pollution. Under such conditions, which occur about 10% of the time in Paris, the contribution of persistent background sources can be isolated. Using the 6 m·s⁻¹ threshold, the average annual linear decrease in emissions from local sources is estimated at 4.1 and 4.3% per year for the Airparif and Pollutrack data, respectively. Since 2023, the annual background value attributed to emission has been close to 5 µg·m⁻³, in agreement with WHO recommendations. This approach could be used to monitor the effects of regulations on traffic and heating emissions and could be applied to other cities for estimating background pollution levels. Finally, future studies should therefore prioritize number concentrations and size distributions, rather than mass-concentrations. Full article

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article

The short-circuit withstanding capability of a transformer is a critical indicator for evaluating its operational reliability. This study investigates the influence of the low-voltage winding height, a key structural parameter, on the electromagnetic forces induced by short-circuit currents and the resultant short-circuit withstand capability. First, theoretical calculation formulas for the transformer leakage magnetic field and winding electromagnetic forces were derived, establishing a foundation for subsequent analysis. Subsequently, two 110 kV transformers, identical in all structural parameters except for their low-voltage winding heights, were selected as case studies. Three-dimensional finite element models were constructed to perform detailed simulations and a comparative analysis of the leakage magnetic field distribution and electromagnetic forces under short-circuit conditions. Finally, practical short-circuit tests were conducted on both transformers for experimental validation, monitoring and comparison of their short-circuit reactance variation curves. Furthermore, a CNN-LSTM model, utilizing the winding axial height of a 110 kV three-phase three-limb transformer as the characteristic parameter, is developed to detect short-circuit fault damage in such transformers with varying winding heights. Through a combined approach of theoretical analysis, simulation, and experimental verification, this study confirms that the low-voltage winding height was a crucial factor affecting the transformer’s short-circuit withstand capability of the transformer. Studies have shown that with the increase in the height of low-voltage windings, the leakage magnetic flux of the low-voltage windings increases by 36%, the radial electromagnetic force increases by 37.5%, and the axial electromagnetic force increases by 8.5%. Excessively tall windings amplify radial electromagnetic forces, compromising mechanical stability and consequently increasing the risk of damage during short-circuit faults. Full article

Wind and sand disaster prevention is a critical challenge for global environmental sustainability, with mechanical wind fences being key engineering measures. Current fences, including solid and permeable types, often struggle to balance environmental impact, windbreak efficiency, and stability. Solid fences provide effective sand control but have limited windbreak efficiency, while permeable fences improve airflow but require deep burial and are prone to erosion on uneven terrain. This study proposes a novel composite wind fence with a polylactic acid (PLA) sandbag base and a fiber mesh top, combining stability and permeability. We assessed windbreak performance using computational fluid dynamics simulations and verified results through wind tunnel experiments. Results show that the novel composite wind fence enhances windbreak efficiency and stability by optimizing airflow distribution, with the PLA sandbag base suppressing high–speed airflow and mesh fence weakening of leeward side vortices. Under wind speeds of 10 m/s, 18 m/s, and 28 m/s, the effective protection distance of the novel composite wind fence improved by 22.33% to 36.51%, 10.96% to 34.22%, and 0.94% to 28.98%, respectively, compared to single PLA and mesh wind fence. The optimal row spacing for the novel wind fences in three rows is 12 h when the incoming wind speed is 10 m/s, while the recommended spacings are 8 h and 6 h for wind speeds of 18 m/s and 28 m/s, respectively, ensuring continuous and effective protection. These findings present a novel wind fence technology with improved wind resistance, a more stable structure, and prolonged protective effects, offering an effective solution for environmental conservation initiatives aimed at preventing wind and sand disasters while promoting the sustainability of ecosystems. Full article

In response to China’s “Dual Carbon” goals, this paper proposes a Transmission Network Expansion Planning (TNEP) model that explicitly incorporates the operational flexibility of Virtual Power Plants (VPPs). Unlike conventional approaches that focus mainly on transmission investment, the proposed method accounts for the aggregated dispatchable capability of VPPs, providing a more accurate representation of distributed resources. The VPP aggregation model is characterized by the inclusion of electric vehicles, which act not only as load-side demand but also as flexible energy storage units through vehicle-to-grid interaction. By coordinating EV charging/discharging with photovoltaics, wind generation, and other distributed resources, the VPP significantly enhances system flexibility and provides essential support for grid operation. The vertex search method is employed to delineate the boundary of the VPP’s dispatchable feasible region, from which an equivalent model is established to capture its charging, discharging, and energy storage characteristics. This model is then integrated into the TNEP framework, which minimizes the comprehensive cost, including annualized line investment and the operational costs of both the VPP and the power grid. The resulting non-convex optimization problem is solved using the Quantum Particle Swarm Optimization (QPSO) algorithm. A case study based on the Garver-6 bus and Garver-18 bus systems demonstrates the effectiveness of the approach. The results show that, compared with traditional planning methods, strategically located VPPs can save up to 6.65% in investment costs. This VPP-integrated TNEP scheme enhances system flexibility, improves economic efficiency, and strengthens operational security by smoothing load profiles and optimizing power flows, thereby offering a more reliable and sustainable planning solution. Full article

Understanding internal resonance phenomena in transformer windings is essential for evaluating insulation performance and preventing equipment failure under transient conditions. This study presents a measurement-based modeling approach to assess internal voltage distributions in a high-voltage transformer winding of a dry-type distribution transformer. Frequency-domain admittance and voltage transfer functions were experimentally obtained and approximated using vector fitting. The resulting models were employed to simulate time-domain responses through a two-step procedure that integrates electromagnetic transient simulations of the terminal circuit with frequency-derived internal voltage models. The validation was performed using a sinusoidal excitation at 51 kHz, corresponding to the first-mode resonance frequency. Simulated internal voltages and input currents showed close agreement with experimental measurements, confirming the model’s accuracy. The study identified two critical resonance frequencies at 51 kHz and 59 kHz, at which voltage amplification can become severe. At 51 kHz, the maximum overvoltage reached nearly seven times the applied voltage at the winding midpoint, indicating a substantial risk of dielectric failure. These findings highlight the importance of accurately characterizing internal resonances in transformer windings, especially during insulation coordination studies. The proposed methodology offers an effective tool for analyzing internal overvoltages and contributes to the development of more robust transformer design and protection strategies. Full article

The uneven distribution of airflow on the blade surface of a yaw wind turbine triggers a complex non-constant flow, resulting in turbine flow field operation disorder, which, in turn, affects the structural field. In view of the different degrees of influence of different blade materials on the structural characteristics of a wind turbine, a numerical simulation of the flow field and structural field of the horizontal-axis wind turbine under different yaw conditions is carried out by using the fluid–solid coupling method to quantitatively analyse the degree of influence of the material on the structural characteristics of the wind turbine. The results show that the average velocity of the wake cross-section shows a trend of decreasing, then increasing, and then stabilising at all yaw angles. The larger the yaw angle, the wider is the vortex structure dispersion. As the wake develops downstream, the turbulence intensity is shown to decrease and then increase, and the yaw perturbation exacerbates the turbulence disorder in the wake flow field. Along the wind turbine blade spreading direction, the blade deformation phenomenon is significant. The yaw angle increases, the wind turbine blade deformation increases, and the maximum blade stress first increases and then decreases. At a 15° yaw angle, the airflow on the blade surface is more easily separated, and vortices are formed in the vicinity, which impede the airflow in the boundary layer and lead to a reduction in the velocity in the boundary layer in this region. The minimum deformation and maximum stress of the three materials under a 15° yaw angle indicate that the blades are more capable of resisting external deformation under this condition, so 15° yaw is the best operating condition for the wind turbine. This paper employs different materials to quantitatively analyse the extent to which structural characteristics influence wind turbine performance. The findings from this research can provide valuable insights for optimising wind turbine designs. Full article

This study investigates the wind field characteristics of long-span suspension bridges, with a particular focus on the disturbance effects introduced by the bridge deck on wind measurements. Field data are collected using anemometers installed on both the upstream and downstream sides at the midspan of the bridge girder. A comparative analysis of these measurements reveals notable discrepancies attributable to deck-induced flow disturbances. To systematically assess these effects, the disturbed wind parameters are identified, and their spatial distribution patterns are examined. A statistical model is then developed to quantify and correct the disturbance influence. This model isolates the disturbance component and establishes empirical correlations between the disturbed and actual wind parameters. The results confirm that the proposed correction approach effectively reduces measurement bias caused by deck interference, thereby enabling more accurate wind load evaluation for long-span suspension bridge structures. Full article

Temperature distribution inside the vacuum chamber of the TRX 2000(A) near space environment simulation test system (NSESTS) was investigated through both experimentation and computational fluid dynamics simulation. Comparison between the experimental result and the simulation result showed that these two results were very close to each other, validating the feasibility of using the simulation method to study the temperature distribution inside the NSESTS. Then, the effect of wind, either downwind or upwind, on temperature uniformity inside the NSESTS was investigated through the simulation method. The simulation result showed that the non-uniformity coefficient will be reduced from 0.2757 to 0.2012 (by 27.1%) in the case of downwind and to 0.2055 (by 25.5%) in the case of upwind. Then, the simulation result was validated by experiment. The result of this research indicates that the temperature uniformity can be greatly improved through installment of additional fans inside the NSESTS. Full article

This study compares the mapping accuracy of a non-RTK ultra-lightweight drone (DJI Mini2) with two survey-grade RTK-enabled drones (DJI Mavic3E and Phantom4) in three different sites. Flight parameters and weather conditions were the same on each site. The outputs were orthomosaics and digital surface models, whose accuracies were inspected by descriptive statistics and variance analysis tools. The data of the ultralight drone on the first site could not be processed due to strong wind, but its results for the second site (11 hectares) were comparable to those of survey-grade drones, i.e., the range and average of checkpoint errors for Mini2 were 0.17 m and 0.04 m, respectively, while those were 0.10 m and 0.02 m for Phantom4 and Mavic3E. In the third site (34 hectares), survey-grade drones produced accurate results with a checkpoint error range of 0.26 m, while that was 0.87 m for the ultralight drone, implying lower accuracy results. The results obtained suggest that ultralight drones under certain circumstances can produce reliable mapping products depending on weather conditions, the number and distribution of ground control points, and area size. Their biggest drawback is their vulnerability to wind, and in calm weather conditions, due to non-RTK error accumulation, their mapping accuracy degenerates as the area size increases. Full article

Clear-air turbulence (CAT), as a key meteorological hazard threatening aviation safety, urgently requires the revelation of its spatiotemporal distribution patterns and formation mechanisms within the China region. Based on the first release of 12,539 aircraft turbulence voice reports from China’s civil aviation from 2022 to 2024 and ERA5 high-resolution reanalysis data, this study constructs for the first time a climatological portrait of aircraft turbulence over China, revealing the spatiotemporal distribution characteristics and formation mechanisms of CAT in the region: turbulence occurs predominantly at 3000–8000 m (accounting for 61.0%), peaking at 7000–8000 m, driven by strong low-level jet wind shear and Kelvin–Helmholtz instability (KHI); wintertime exhibits a high frequency (33.4%) stemming from strong upper-level jets (>30 m s⁻¹), while summer is dominated by low-level thermal convection (21.0%); the high-incidence zones of Central-South and Southwest China (>2800 events) are jointly governed by a mid-level strong horizontal gradient of vertical vorticity, divergence perturbations, and jet shear, with the winter jet shifting southward (22–30° N), further intensifying the turbulence risk. The findings establish a dynamic–thermodynamic coupling mechanism for CAT over China, providing a scientific basis for aviation safety early warning. Full article

The paper describes an installation and procedure for evaluating the velocity profile for the airflow produced by the fan of the cleaning unit of a New Holland wheat combine harvester. The velocity profile is based on measurements taken at 52 points spread over the entire surface of the top and bottom sieves, for different speeds of the fan, different positions of the wind boards and different opening positions of the sieves. The experimental data obtained were graphically represented using the Radial Basis Function (RBF) interpolation model and highlighted that the airflow generated by the fan at the upper screen level, in the longitudinal plane and, especially, in the transverse plane, is distributed unevenly, and depends on the fan rotor speed, the opening of the louvers of the two screens and the arrangement of the two deflectors. The correct adjustment of the cleaning unit and correct evaluation of the air velocity profile over the sieves result in the reduction in grain losses from the upper sieve due to grain flotation, reduction in the content of broken grains in the grain tank due to the reduction in the material flow from the tailing auger as well as reduction in the impurities content of the grain tank due to better separation of the material over the surface of the lower sieve. Full article