A Study on Determining the Output Power of Wind Energy Generation Considering Uncertainty in Input Power Forecasting: Published online: 19/03/2026 - Doan, Le Trung Tin; Nguyen, Thai An; Phan, Le Huu Tri; Le, Trong Nghia; Huynh, Thi Thu Hien (Journal of Technical Education Science, 2026)
This paper presents a method for determining the output power of a wind power generation system under wind speed uncertainty. Hourly wind data collected in Hawaii, USA, is statistically modeled using four probability distribution functions: Weibull, Rayleigh, Log-normal, and Gamma. The distribution parameters are estimated via the Maximum Likelihood Estimation (MLE) method and subsequently applied to a Doubly-Fed Induction Generator (DFIG) model in MATLAB/Simulink to simulate power output variations based on probabilistically modeled wind speed. The fit quality of each distribution is assessed by calculating the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), in comparison to the empirical histogram. The results indicate that the two-parameter Weibull distribution best fits the measured data (MAE = 0.00708, RMSE = 0.0097, R² = 0.93), followed by the Gamma distribution. In contrast, the Rayleigh and Log-normal distributions exhibit significant deviations. When the Weibull parameters are applied to the DFIG model, the simulated weekly power output ranges from 0.96 MW to 1.37 MW, clearly illustrating the nonlinear relationship between wind speed and output power. The proposed approach thus provides a rigorous quantitative framework that links the probabilistic characteristics of wind to the actual power output range, thereby enhancing reliability in operational planning and mitigating risks in modern power systems.