Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions.

Conventional statistical models consider all wind speed ranges as equally important, causing significant prediction errors, particularly in wind speed intervals that contribute the most to wind turbine power generation. To overcome this limitation, this study proposes a novel parameter estimation method—Weighted Maximum Likelihood Estimation (WMLE)—to improve the accuracy of annual energy production (AEP) predictions for wind turbine systems. The proposed WMLE incorporates wind-speed-specific weights based on power generation contribution, along with a weighting amplification factor (β), to construct a power-oriented wind distribution model. WMLE performance was validated by comparing four offshore wind farm candidate sites in Korea—each exhibiting distinct wind characteristics. Goodness-of-fit evaluations against conventional wind statistical models demonstrated the improved distribution fitting performance of WMLE. Furthermore, WMLE consistently achieved relative AEP errors within ±2% compared to those of time-series-based methods. A sensitivity analysis identified the optimal β value, which narrowed the distribution fit around high-energy-contributing wind speeds, thereby enhancing the reliability of AEP predictions. In conclusion, WMLE provides a practical and robust statistical framework that bridges the gap between statistical distribution fitting and time-series-based methods for AEP. Moreover, the improved accuracy of AEP predictions enhances the reliability of wind farm feasibility assessments, reduces investment risk, and strengthens financial bankability.

In this study, the aerodynamic performance of single- and multi-rotor sail systems was numerically investigated under various inflow directions and array configurations using Computational Fluid Dynamics (CFD) simulations. For a single-rotor sail, the optimal spin ratio (SR) for each wind direction was derived from the energy efficiency index, and an appropriate spacing distance of 9D was identified, within the present steady-RANS framework, as a preliminary guideline based on wake-recovery characteristics. Using these findings, four array configurations were established to reflect the practical installation conditions of a medium-range (MR) tanker. The wake interference and aerodynamic performance variations in each configuration were quantitatively compared and analyzed. The results showed that the average lift in all arrays remained within ±1% of that of a single-rotor, and the 1 × 1 × 1 array exhibited the most stable performance. These findings confirm that the wake-based spacing design and wind direction-dependent SR optimization proposed in this study are crucial for maintaining aerodynamic stability and improving efficiency in multi-rotor sail systems. It is expected that the results of this study will contribute to establishing design guidelines and operational strategies for the practical applications of rotor sails on ships.