Wind

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.

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.