Wind

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.

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.

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.