Wind

Wind is a significant contributor to global energy requirement, with technological advancements in this industry enabling its rapid growth over the last few decades. The rise in demand for clean energy provides the driving factor to make wind more efficient and widespread. One such solution involves mitigating the aerodynamic noise of wind turbine rotors to harness untapped energy and improve turbine efficiency. Quieter wind turbines gain community acceptance, promoting their widespread application. This article explores passive compliant coatings applied to a flat plate under fully turbulent conditions through Computational Fluid Dynamics (CFD) and wind tunnel testing. It extends prior flat plate investigations by evaluating the noise mitigation potential of passive compliant coatings in the context of wind turbine trailing edge (TE) noise. Two coatings with distinct material properties were investigated through Computational Aeroacoustics Analysis (CAA) and Fluid–Structure Interaction (FSI). While coating-1 (Dow Corning Silastic S-2) increased the overall sound pressure level (OASPL) by 2.89 dB, coating-2 (Dow Corning Sylgard 184) reduced TE noise by 2–4 dB/Hz between 600 and 1575 Hz and lowered the OASPL by 1.85 dB. Within the two configurations investigated, the differences in noise mitigation characteristics may be attributed to variations in coating stiffness and geometric compliance. Based on these simulations, wind tunnel tests were conducted to record noise measurements using coating-2 which revealed a 3.23 dB OASPL reduction, suggesting its suitability for wind turbine noise mitigation applications. Full article

This study focuses on the aerodynamic performance optimization of the SG6043 airfoil for application in small horizontal axis wind turbines (HAWTs) operating under low-Reynolds-number conditions. Recognizing the critical role of lift-to-drag ratio (Cl/Cd) in maximizing turbine power output, the research investigates the performance of SG6043 through design modifications and computational analysis. Initially, the baseline airfoil’s aerodynamic characteristics were verified using simulation tools like QBlade v0.96.3 software, confirming its previously reported performance. Subsequently, the airfoil was systematically modified by varying key parameters including thickness-to-camber ratio and angle of attack (AOA), operating at different Reynolds numbers. Among the modified versions, SG6043M5-7, SG6043M5-8, and SG6043M5-9 showed significant aerodynamic performance improvement, with SG6042M5-9 achieving the highest Cl/Cd ratio of 193.44 at Re = 6 × 10⁵ and AOA = 3.5°. The results demonstrated that a reduced thickness (5%) combined with moderate to high camber (7–9%) enhances the aerodynamic performance. Full article

As the population increases, the demand for power continues to rise. As fossil fuel resources reduce, wind energy emerges as a sustainable alternative and helps address adverse effects of global warming and environmental pollution caused by fossil fuels. Thus, this study focuses on increasing the efficiency of wind turbines by improving their energy conversion. In this study, the NREL Phase VI wind turbine blade was modified by adding a Gurney flap at trailing edge along the entire span. Computational fluid dynamics simulations using ANSYS CFX 19.2 were performed on the modified blades to evaluate their aerodynamic performance. Three different flap lengths were investigated with six wind speeds varying from 5 m/s to 20 m/s. The results obtained were compared with those from NREL Phase VI original shape and a blade equipped with a winglet. Computational domain was divided into a rotating cylindrical region and a stationary rectangular part. The aerodynamic parameters calculated include torque, thrust, and normal and tangential forces coefficients. At low velocities, the addition of a Gurney flap had an insignificant impact on torque and thrust, whereas at medium to high wind speeds, significant increases were observed on torque, indicating more power production. Full article

In various parts of Indonesia, particularly in coastal areas, wind energy can be used as a source of electricity, using wind turbines, whose energy depends on wind speed. Basically, the number of blades in a wind turbine affects the overall turbine performance. This research analyzes the influence of the blade number on the performance of a small-scale horizontal-axis wind turbine using experimental measurements and Computational Fluid Dynamics (CFD) simulations. The CFD simulations were conducted using ANSYS 2022 R2 software on a small-scale horizontal-axis wind turbine with variations in the number of blades, specifically three, four, and five blades, conducted at various wind speeds. It should be noted that due to the setup limitation in the experiment, only the RPM of the three-bladed turbine was measured. Other variables such as torque and power were derived from CFD simulations. The results of this research indicate that an increase in the number of turbine blades tends to result in higher power output, where the highest output obtained was 46.25 Watts. Furthermore, as the number of turbine blades increases, the turbine efficiency also tends to increase, but as wind speed increases, the efficiency decreases. This is demonstrated by the research results, where a wind turbine with five blades achieved the highest efficiency at a speed of 3 m/s, at 38.00%, while at a speed of 6 m/s, the efficiency was 34.80%. Overall, through experiments and cross-validation of CFD and QBlade version 0.963.1, the present study could confirm the significant effect of the number of blades on the power produced by a small-scale horizontal-axis wind turbine under low-speed conditions. Full article

As wind farms in North America near the end of their design life, different end-of-life options need to be considered. Common options include decommissioning, lifetime extension, and repowering. In this research, a methodology to support early-stage repowering decisions is presented. Performance decline and repowering forecasts are obtained by combining analysis of past performance data and preliminary site plans for new turbines with turbine performance models from windPRO software. Financial metrics are computed using a simple techno-economic model with parameters informed by historical financial records. Repowering decisions are often sensitive to assumptions on key parameters, such as capital cost of repowering, which are poorly defined at the beginning of the process and subject to change quickly. This makes it difficult to provide guidance that will remain relevant as more information is obtained during future project planning stages. In this work, constrained optimization methods are used to identify sets of the key inputs that lie on the break-even point at which repowering is more profitable than continuing operation. Using this approach, which is novel in this context, the client gains an intuition for the ‘envelope’ within which the recommended guidance still holds. This decision-making process is applied to a case study using performance data and cost ranges from a real, anonymous wind farm. Full article

Wind design aims to ensure the stability, safety, and durability of a structure exposed to wind forces. This comparative study using Computational Fluid Dynamics (CFD) was conducted to evaluate the effects of surrounding structures in wind building design. Two scenarios were analyzed: the first, in which the building was exposed to an open field, and the second, in which the building was surrounded by other buildings of equal or lower height. A CFD model, previously calibrated with experimental data, was used to simulate wind behavior. The results obtained showed significant differences between the two scenarios, confirming that nearby structures have a considerable impact on the distribution of wind pressures on the building. Therefore, the importance of considering surrounding buildings is highlighted. CFD could be a useful complementary tool for obtaining pressure coefficients and for detailed analyses of wind behavior, which could improve the design and safety of buildings under wind loads. Full article

The research creates classification maps of wind turbine operational speeds based on the wind regimes of four governorates in central and southern Iraq: Wasit, Diwaniyah, Maysan, and Dhiqar. High-resolution wind data from GEOSUN resource maps, together with statistical analysis of the Weibull distribution, are used to derive site-specific shape and scale parameters, which are then utilized to calculate the ideal cut-in, rated, and cut-out wind speeds for each location. A turbine performance index integrates capacity factor and normalized power output to determine the turbine speed combination that optimizes energy production for the local wind distribution. The resultant maps exhibit distinct geographical gradients: in all four governorates, cut-in, rated, and cut-out speeds consistently escalate towards the eastern regions of the research area, therefore broadening the range of technologically suitable turbines. Quantitatively, Wasit demonstrates the highest rated wind speeds, ranging from approximately 11.1 to 14.9 m per second, and cut-out speeds from about 20.5 to 27.6 m per second, indicating superior wind resource quality relative to other governorates. In contrast, Diwaniyah is suitable for lower-speed turbines, with minimum rated speeds between 8.9 and 9.5 m per second and minimum cut-out speeds around 16.6 to 17.6 m per second. Analysis of wind direction indicates that around fifty percent of the wind power potential originates from the northwest sector, suggesting that turbines should be aligned toward the northwest to optimize yearly energy acquisition. The maps serve as an effective decision support instrument that connects quantitative wind resource assessment to turbine operational specifications, facilitating expedited preliminary turbine selection, enhanced energy efficiency, and diminished dependence on traditional fossil fuel power plants in areas experiencing persistent electricity deficits. Full article

Accurate wind field simulation in forest areas is crucial for wind energy development but remains challenging for traditional WRF models due to complex terrain and vegetation heterogeneity. This study proposes a multi-source optimization framework integrating seasonal PBL scheme selection, localized leaf area index (LAI) adjustment, and 3DVAR data assimilation to improve WRF performance in forested terrain. The framework was validated using observations at 20 m, 50 m, and 100 m heights in Maoershan forest area. Results show that: (1) PBL schemes exhibit significant seasonal dependence—YSU performs best in spring (unstable conditions), while MYJ shows slight advantages near the surface in winter (stable conditions). (2) Localized LAI correction reduces near-surface wind speed bias by 35% and improves wind direction accuracy by 28%, with stronger effects in summer. (3) 3DVAR assimilation further enhances accuracy, achieving correlation coefficients of 0.869 for wind speed and 0.813 for wind direction, with greater improvements in summer and near the surface. (4) Winter wind power density at 100 m reaches 475 W/m², 38% higher than summer, indicating stable exploitable resources. The proposed framework provides a replicable methodology for wind field simulation in forest regions worldwide. Full article

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations. Full article

Floating offshore wind holds immense promise for nations with deep coastal waters and robust wind resources. Taiwan, with 90% of its territorial waters deeper than 50 m and consistently strong wind speeds, is well-positioned to lead in this domain. However, recent project withdrawals by major developers have raised concerns over the sector’s viability. This paper investigates the stagnation of Taiwan’s floating wind industry by comparing its development framework with that of France, now a global frontrunner in floating offshore wind. Through a mixed-method approach combining literature review, techno-economic benchmarking, and thematic analysis of interviews with industry leaders, the research identifies key barriers in Taiwan, including insufficient port infrastructure, unclear regulatory frameworks, fragmented supply chains, and a lack of financial incentives. Drawing on lessons from France’s structured tendering system and phased industrial strategy, the paper outlines actionable recommendations for revitalizing Taiwan’s floating wind sector. These include policy reforms, supply chain enhancements, and demonstration-scale deployments. The findings aim to inform both policymakers and industry stakeholders in shaping a more viable future for floating offshore wind in Taiwan. Full article

The integration of renewable energy sources (RESs) into modern power systems has introduced significant challenges in maintaining system stability and reliability. Among these challenges, load frequency control (LFC) has become a vital area of research. The variable nature of RESs, such as wind and solar, along with their intermittent availability, necessitates advanced management systems for effective frequency regulation. LFC plays a crucial role in ensuring the stability and performance of electrical power systems by managing frequency through the balance of supply and demand, accounting for variations in load, generation, and other disturbances within the system. In traditional power systems, LFC is achieved through a combination of primary, secondary, and tertiary control mechanisms. However, the advent of smart grids has considerably complicated and enhanced the potential for LFC. In these smart grids, which leverage digital communication, sensors, and automation technologies, LFC becomes more intricate and adaptable. These systems not only utilize traditional centralized control but also incorporate RESs, decentralized resources, energy storage solutions, and real-time data to improve frequency management. This research methodically evaluates current LFC techniques using a hierarchical control and technology-focused framework, classifying approaches as conventional, intelligent, and hybrid control schemes within centralized and decentralized system architectures. An evaluative analysis reveals that while intelligent and hybrid control strategies markedly enhance dynamic frequency response and robustness with substantial renewable energy source (RES) integration, persistent challenges remain regarding controller coordination, scalability, computational requirements, and real-time execution. The analysis highlights adaptive hybrid intelligent control schemes, namely those that combine data-driven learning with physical system models, as the most promising avenue for future research, particularly in low-inertia and highly dispersed smart grid scenarios. Full article

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. Because wind turbines are susceptible to these conditions, accurately characterising wind flow in forested environments is vital to ensuring structural reliability and realistic energy-yield assessments. In Latvia, where approximately 51.3% of the territory is covered by forests; the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Wind conditions were evaluated using NORA3 reanalysis data. Wake effects were simulated with the Jensen wake model to estimate annual energy production (AEP), which then informed levelised cost of energy (LCOE) calculations at various hub heights. The results indicate clear seasonal variability and show that increasing hub height leads to higher AEP and lower LCOE, owing to higher wind speeds and reduced turbulence. For forest heights of 0–25 m, the AEP loss increases from 7.8% (hub height = 199 m) to 22.9% (hub height = 114 m). Higher hub heights are also less sensitive to canopy-induced variability, reducing the impact of forest-related turbulence on energy production. Full article

Considering the growing potential of artificial intelligence (AI), its application has become increasingly relevant in climate-related studies and energy assessments. In this study, the Random Forest algorithm was applied to impute missing values in time series of air temperature, wind speed, atmospheric pressure, and wind direction. The performance of the data imputation was evaluated using RMSE, MSE, and MAE metrics, as well as the Kolmogorov–Smirnov (KS) test, which supported the selection of the most appropriate exogenous variable. Subsequently, short-term wind speed forecasting was performed using the SARIMAX model, and monthly energy generation was estimated for the V80/2000, SWT-2.3-101, and S95/2100 wind turbine models. The proposed methodology was applied to data from 50 conventional meteorological stations of the National Institute of Meteorology (INMET) located in Northeast Brazil. The results indicate that the gap-filling procedure was effective, particularly for wind speed and mean air temperature. Moreover, the SARIMAX model demonstrated good forecasting performance at most of the analyzed stations. Overall, the findings suggest that the majority of the locations analyzed present favorable conditions for wind-based electricity generation. Full article

Considering the global shift towards clean energy, advancing wind power generation technology is crucial. In complex terrain, turbines face significant challenges, such as increased fatigue loads from turbulent airflow, which reduce efficiency and structural integrity. Yaw optimization has emerged as a key solution to enhance performance in these environments. By dynamically adjusting the nacelle orientation, it improves wind capture, mitigates load fluctuations, and alleviates stress on turbine components. This not only boosts energy output but also extends equipment lifespan and reduces operational costs, supporting the economic feasibility of wind projects in complex terrain. This paper reviews current research and development trends in yaw optimization for wind farms in such settings, focusing on adaptive control strategies and the balance between load management and power efficiency. It examines the impact of different yaw optimization approaches on both individual turbines and overall wind farm performance. In conclusion, tailored yaw optimization strategies are proposed to maximize wind resource utilization in complex terrain, providing a reference for more resilient and efficient wind energy systems. Full article

Leading-edge erosion of wind turbine blades caused by repeated raindrop impingement can significantly reduce power output and increase maintenance costs. This study develops a rain erosion atlas for Japan over 11 years from 2006 to 2016 based on the CRIEPI-RCM-Era2 dataset. The NREL 5 MW, DTU 10 MW, and IEA 15 MW wind turbines were employed to evaluate the incubation time (erosion onset time) of commercial polyurethane-based coating at the blade tip. Erosion progression was simulated using an empirical damage model that relates raindrop impingement and impact velocity to the incubation time. The rain erosion atlas reveals a clear correlation between wind turbine size and erosion risk: the NREL 5MW turbine shows an incubation time of 3–12 years, the DTU 10MW turbine 1–4 years, and the IEA 15MW turbine 0.5–2 years. Shorter incubation times are observed on the Pacific Ocean side, where annual precipitation is higher than on the Sea of Japan side. Additionally, the influence of coating degradation due to ultraviolet radiation was assessed using solar radiation data, revealing a further reduction in incubation time on the Pacific Ocean side. Finally, the potential of erosion-safe mode operation was examined, demonstrating its effectiveness in alleviating erosion progression. Full article

Wind energy has the potential to become an important source of energy for remote Arctic regions. However, there are risks associated with the exposure of coastal wind parks to extremely strong winds caused by storms and polar lows. Extreme winds can either enhance or reduce wind energy production. The outcomes largely depend on the coastal landscape surrounding the wind park. To address these questions, we conducted a series of simulations using the Weather Research and Forecasting (WRF) model. This study focuses on one of the strongest wind events along the western Norwegian coast—the landfall of the storm “Ylva” (24–27 November 2017). The study employs terrain-resolving downscaling by zooming in on the area of the Kvitfjell–Raudfjell wind park, Norway. The terrain-resolving WRF simulations reveal stronger winds at turbine hub height (80 m to 100 m above the ground level) in the coastal area. However, it was previously overlooked that the landfall of an Atlantic storm, which approaches this area from the southwest, brings the strongest winds from southeast directions, i.e., from the land. This creates geographically extensive and vertically deep wind-sheltered areas along the coast. Wind speeds at hub height in these sheltered areas are reduced, while they remain extreme over wind-channeling sea fjords. The novelty and applied value of this study is that it reveals an overlooked opportunity for optimal wind park siting. The coastal wind parks can take advantage of both sustained westerly winds during normal weather conditions and wind sheltering during extreme storm conditions. We found that the Kvitfjell–Raudfjell location is nearly optimal with respect to the extreme winds of “Ylva.” Full article

An optimal topographical arrangement of wind turbines (WTs) is essential for increasing the total power production of a wind farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions; therefore, hybrid PSO-GA can effectively handle issues involving multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, the variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the cost per power while analyzing the wake loss using the Jenson wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. This comparative study on the past layout improvement process models demonstrates that the proposed hybrid algorithm enhanced performance with a significant power improvement of 0.03–0.04% and a 24–27.3% reduction in wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Southern Patagonia in Argentina possesses a world-class wind resource; however, its remote location challenges long-term monitoring. This study presents the first long-term Doppler LiDAR-based wind characterization in the region, analyzing six months of high-resolution data at a 100 m hub height. Power for the LiDAR is provided by a hybrid system combining photovoltaic (PV) and grid sources, with remote monitoring. The results reveal two distinct seasonal regimes identified through a multi-model statistical framework (Weibull, Lognormal, and non-parametric Kernel Density Estimation: a high-energy summer with concentrated westerly flows and pronounced diurnal cycles (Weibull scale parameter A ≈ 11.9 m/s), and a more stable autumn with a broad wind direction spectrum (shape parameter k ≈ 2.86). Energy output, simulated using Windographer v5.3.12 (Academic License) for a Vestas V117-3.3 MW turbine, shows close alignment (~15% difference) with the operational Bicentenario I & II wind farm (Jaramillo, AR), validating the site’s wind energy potential. This study confirms the viability of utility-scale wind power generation in Southern Patagonia and establishes Doppler LiDAR as a reliable tool for high-resolution wind resource assessment in remote, high-wind environments. Full article