Liao, Kuo-WeiKuo-WeiLiaoLiu, Yin-TingYin-TingLiuThedy, JohnJohnThedy2026-01-152026-01-152026-0200298018https://www.scopus.com/pages/publications/105025202320?origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/735314Offshore wind is a major renewable source, yet faces unique operational and environmental challenges requiring robust Operation and Maintenance (O&M) strategies. This study develops a Taiwan-specific O&M model to minimize costs while ensuring reliable power. Using the Formosa 2 Offshore Wind Farm as a case study, the research incorporates local wind patterns and extreme typhoon events into both baseline and advanced modeling frameworks. The model considers failure replacement, preventive replacement, and preventive repair, with decision variables including maximum and minimum maintenance thresholds and O&M team dispatch intervals. A novel Bayesian Weibull First Order Reliability Method (BW-FORM) is integrated to update component serviceable life estimates and enhance reliability assessment. Performance evaluations compare the baseline and advanced models under various operational scenarios. Results demonstrate that the proposed BW-FORM–based model enhances reliability control and operational stability while maintaining cost-effectiveness. Maintenance frequency, particularly dispatch interval, is identified as a critical factor influencing availability and cost variability. The model's scalability is validated across turbine fleet sizes, and optimal strategies are determined using Reliability-Based Design Optimization (RBDO). The identified optimal solution achieves a balanced trade-off between cost, downtime, and reliability, offering a robust and adaptive approach for offshore wind O&M planning under uncertainty.falseBayesian methodO&M optimizationOffshore wind farmTyphoon[SDGs]SDG7journal article10.1016/j.oceaneng.2025.1238592-s2.0-105025202320