Wind

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

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

This research presents a detailed assessment of the wind power potential in six Palestinian cities—Bethlehem, Jericho, Jenin, Nablus, Ramallah, and Tulkarm—utilizing daily wind speed data from the years 2015 to 2021. The primary goal of this study is to formulate a robust, data-driven framework for the strategic placement of turbines and the economical production of energy in areas with limited wind resources. A critical aspect of this research is the application of nine numerical methods, including the Maximum Likelihood Method (MLM) and the Energy Pattern Factor Method (EPF), to analyze the wind data. These methods were employed to estimate the shape and scale parameters of the Probability Distribution Function (PDF) that represents the Weibull distribution for various shape factor values. The accuracy of the numerical methods was validated through five statistical tools, including the Root Mean Square Error (RMSE) and Chi-square tests ($X^2$). The Weibull parameters obtained from the numerical techniques indicated shape factors ranging from 1.27 to 1.96 and scale factors between 1.16 and 3.21 m/s. The energy output was calculated based on the swept area of the wind turbine, following Betz’s limit. The estimated annual energy production per square meter in the six cities is as follows: Ramallah—123 kWh/m², Bethlehem—24.42 kWh/m², Jenin—31.12 kWh/m², Nablus—22 kWh/m², Tulkarm—15.5 kWh/m², and Jericho—10.36 kWh/m². A 5 kW small-scale wind turbine was utilized to evaluate the technical feasibility, sustainability, and economic viability of small-scale wind energy applications. The anticipated energy output from the proposed wind turbine is 2054 kWh, with an estimated payback period of approximately 11.6 years. Full article

The parametric design of a low-power (<1 kW) H-type vertical-axis wind turbine tailored to the wind conditions of the Yucatán Peninsula is presented. Nine airfoils were evaluated using the Double Multiple Streamtube method and Qblade Lifting-Line Theory numerical simulations, considering variations in solidity (σ = 0.20–0.30), aspect ratio (A_r = H/R = 2.6–3.0), number of blades (2–5), and a swept-area constraint of 4 m². The parametric study shows that fewer blades increase Cp, although a three-blade rotor improves start-up torque, vibration mitigation, and load smoothing. The recommended configuration—three blades, A_r = 2.6, σ = 0.30 and S1046 (or NACA 0018) operated near λ ≈ 3.75—balances efficiency and start-up performance. For the representative mean wind velocity of 5 m/s, typical of the Yucatán Peninsula, the VAWT achieves a maximum output of 136 W at 220 rpm. Under higher-wind conditions observed in specific sites within the region, the predicted maximum output increases to 932 W at 380 rpm. Full article

Voltage instability and power quality degradation represent critical barriers to the reliable operation of modern wind farm-based microgrids. As the share of distributed wind generation continues to grow, fluctuating wind speeds and variable reactive power demands increasingly challenge grid stability. This study proposes an adaptive decentralized framework integrating a Dynamic Distribution Static Compensator (DSTATCOM) with an Artificial Neuro-Fuzzy Inference System (ANFIS)-based control strategy to enhance dynamic voltage and frequency stability in wind farm microgrids. Unlike conventional centralized STATCOM configurations, the proposed system employs parallel wind turbine modules that can be selectively switched based on voltage feedback to maintain optimal grid conditions. Each turbine is connected to a capacitive circuit for real-time voltage monitoring, while the ANFIS controller adaptively adjusts compensation signals to ensure minimal voltage deviation and reduced harmonic distortion. The framework was modeled and validated in the MATLAB/Simulink R2023a environment using the Simscape Power Systems toolbox. Simulation results demonstrated superior transient response, voltage recovery, and power factor correction compared with traditional PI and fuzzy-based controllers, achieving a total harmonic distortion below 2.5% and settling times under 0.5 s. The findings confirm that the proposed decentralized DSTATCOM–ANFIS approach provides an effective, scalable, and cost-efficient solution for maintaining dynamic stability and high power quality in wind farm based microgrids. Full article

The growing demand for renewable energy has amplified the need for efficient and reliable wind turbine technologies, where understanding aerodynamic performance and aeroelastic behavior plays a critical role. In this study, a high-fidelity computational fluid dynamics (CFD) model was developed to analyze the aerodynamic loads and structural responses of a 2 kW horizontal-axis wind turbine, while an artificial neural network (ANN) was trained using CFD-generated data to predict power output and aeroelastic characteristics. The work combines ANN predictions and CFD simulations to determine the feasibility of machine learning as a surrogate model, which is much less expensive in terms of computational costs and time, with no negative effects on the accuracy. Findings indicate ANN predictions are closely comparable to CFD results with under 5–7% deviation at optimal blade pitch angles, which was shown to be very reliable in capturing nonlinear aerodynamic trends at different wind speeds and blade pitch angles. In addition, the obtained result emphasizes the example of the trade-off between aerodynamic efficiency and structural safety, where the largest power coefficient (0.42) was achieved at $\left(0^{°}\right)$ pitch and the tip deflections were reduced by almost 60% as the pitch was raised to $\left(5^{°}\right)$. Such results substantiate the usefulness of ANN-based methods in the rapid aerodynamic and aeroelastic simulation of wind turbines and provide a prospective direction for effectively designed wind power generation and optimization. Full article

The current study investigates the effect of terrain features on wind resources in a region with extremely diverse terrain. To that end, a case study of Nepal based on annual wind data collected from 10 different sites is performed. The evaluation of mean wind speeds using Weibull probability density functions (PDFs) shows that complex-terrain sites exhibit greater variability in 10-min average wind speeds relative to the annual average wind speeds. This pattern is also evident in comparisons of short- and long-term average wind speeds. At the complex-terrain sites, the wind speeds exhibited strong short-term variations, suggesting that local terrain effects dominate over seasonal wind variation. Terrain complexity also strongly affected turbulence. The flat-terrain sites showed turbulence intensities below the lowest IEC category turbulence profile, while the complex-terrain sites exceeded the highest IEC profile. This indicates that the IEC standard may require modification based on site complexity parameters, such as the standard deviation of elevation fluctuations. The power law exponent ($\alpha$), used to extrapolate wind speeds to higher elevations, deviated notably from the typical 1/7 value, even in flat terrain. Finally, a power potential analysis indicated that three sites with higher mean wind speeds achieved higher capacity factors. Full article

Wind energy has become a cornerstone of sustainable electricity generation, yet the reliable integration of wind energy conversion systems (WECSs) into modern grids remains challenged by dynamic variations in wind speed and stringent fault ride-through (FRT) requirements. Among the available technologies, the Doubly Fed Induction Generator (DFIG) and the Permanent Magnet Synchronous Generator (PMSG) dominate commercial applications; however, a comprehensive comparative assessment under diverse grid and fault scenarios is still limited. This study addresses this gap by systematically evaluating the performance of DFIG- and PMSG-based WECSs across three operating stages: (i) normal operation at constant speed, (ii) variable wind speed operation, and (iii) grid fault conditions including single-line-to-ground, line-to-line, and three-phase faults. To enhance fault resilience, a DC-link Braking Chopper is integrated into both systems, ensuring a fair evaluation of transient stability and compliance with low-voltage ride-through (LVRT) requirements. The analysis, performed using MATLAB/Simulink, focuses on active and reactive power, rotor speed, pitch angle, and DC-link voltage dynamics. The results reveal that PMSG exhibits smoother transient responses and lower overshoot compared to DFIG. Under fault conditions, the DC-link Braking Chopper effectively suppresses voltage spikes in both systems, with DFIG achieving faster reactive power recovery in line with grid code requirements, while PMSG ensures more stable rotor dynamics with lower oscillations. The findings highlight the complementary strengths of both technologies and provide useful insights for selecting appropriate WECS configurations to improve grid integration and fault ride-through capability. Full article

The scaling effect of machines from kW to MW greatly affects electromagnetic performance and needs to be investigated for different machines. Therefore, this paper presents a comprehensive comparative study on the intriguing electromagnetic performance of contra-rotating permanent-magnet vernier machines and dual-port, wound-field-excited, flux-switching machines at the MW power level for contra-rotating wind turbine applications. The analysis evaluates both machines across various slot/pole combinations while maintaining constant key design parameters. The electromagnetic performance analysis reveals that the permanent-magnet vernier machine (PMVM) exhibits superior torque and power, with minimal cogging torque compared to the wound-field flux-switching machine (WFFSM). Conversely, the WFFSM outperforms the PMVM in terms of power factor and efficiency. This study provides valuable perspectives on the strengths and weaknesses of each machine, highlighting their potential for contra-rotating turbine and wind power generation. Finally, to justify the findings of the finite element analysis and the proof of concept, an experimental prototype is tested to validate the study. Full article

The present study focuses on wind power forecasting (WPF) models based on deep neural networks (DNNs), aiming to evaluate current approaches, identify gaps, and provide insights into their importance for the integration of Renewable Energy Sources (RESs). The systematic review was conducted following the methodology of Kitchenham and Charters, including peer-reviewed articles from 2020 to 2024 that focused on WPF using deep learning (DL) techniques. Searches were conducted in the ACM Digital Library, IEEE Xplore, ScienceDirect, Springer Link, and Wiley Online Library, with the last search updated in April 2024. After the first phase of screening and then filtering using inclusion and exclusion criteria, risk of bias was assessed using a Likert-scale evaluation of methodological quality, validity, and reporting. Data extraction was performed for 120 studies. The synthesis established that the state of the art is dominated by hybrid architectures (e.g., CNN-LSTM) integrated with signal decomposition techniques like VMD and optimization algorithms such as GWO and PSO, demonstrating high predictive accuracy for short-term horizons. Despite these advancements, limitations include the variability in datasets, the heterogeneity of model architectures, and a lack of standardization in performance metrics, which complicate direct comparisons across studies. Overall, WPF models based on DNNs demonstrate substantial promise for renewable energy integration, though future work should prioritize standardization and reproducibility. This review received no external funding and was not prospectively registered. Full article

Day-ahead wind power forecasting is often limited to on-site Supervisory Control and Data Acquisition (SCADA) datasets without Numerical Weather Prediction (NWP) information. In this regime, practitioners extend autoregressive windows over many variables, so the input size grows with both features and lags. Many lag–feature pairs are redundant, increasing the training time and overfitting risk. A lightweight, differentiable joint gate over the lag–feature plane trained with a temperature-annealed sigmoid is proposed. Sparsity is induced by capped penalties that (i) bound the total open mass to the top-M features and (ii), within each selected feature, bound the mass to the top-k lags. An additional budget-aware off-state term pushes unused logits negative in proportion to the excess density over the $(M\times k)$ budget. A lightweight, per-feature softmax pooling head supplies the forecasting loss during selection. After training, the learned probabilities are converted into compact, non-contiguous lag–feature subsets (top-M features; per-feature top-k lags) and reused by downstream predictors. Tests on the Offshore Renewable Energy (ORE) Catapult Platform for Operational Data (POD) from the Levenmouth Demonstration Turbine (LDT) dataset show that the joint gate reduces the input dimensionality and training time while improving accuracy and stability relative to Pearson’s correlation, mutual information, and cross-correlation function selectors. Full article