SCADA-Based Comparative Assessment of Power Curve Modeling Methods for a Low-Power Vertical-Axis Wind Turbine

Accurate modeling of wind turbine power curves is essential for performance assessment, energy forecasting, condition monitoring, and operational optimization in wind energy systems. This study presents a SCADA-based comparative assessment of established power curve modeling approaches through a single-site case study conducted on a low-power Vertical Axis Wind Turbine (VAWT) operating under real environmental conditions at the University of the Isthmus, located in the Isthmus of Tehuantepec, Oaxaca, Mexico. The evaluated methods included the maximum power curve, an aerodynamic model based on Blade Element Momentum Theory (BEMT), parametric approaches using polynomial and logistic regressions, and non-parametric data-driven methods based on Random Forest (RF), Gaussian Process Regression (GPR), and Kernel Density Estimation (KDE). One year of SCADA data, including wind speed and generated power measurements, was analyzed, while model performance was assessed using RMSE, MAE, and R2 metrics. The results showed that the machine learning approaches achieved the lowest prediction errors among the evaluated models, with RF providing the best overall performance (RMSE = 7.4%, MAE = 4.8%, R2 = 0.9814), followed by GPR and KDE under the investigated operating conditions. Additionally, Weibull analysis yielded parameters of k = 1.887 and c = 7.875 m/s, while the largest prediction errors were observed within the partial-load operating region (approximately 4–10 m/s) and as the turbine approached the rated operating condition (approximately 10–12 m/s). These findings indicate that, for the investigated low-power VAWT operating at the experimental site, non-parametric approaches provided the most accurate representation of the power curve among the evaluated models, highlighting the potential of SCADA-based data-driven techniques for comparative model assessment under similar operating conditions.