Improving O&M decision tools for offshore wind farm vessel routing by incorporating weather uncertainty - Hadjoudj, Yannis; Pandit, Ravi (Institution of Engineering and Technology (IET), 2023)
The growth of offshore wind farms depends significantly on how well offshore wind turbines (OWTs) are operated and maintained in the long term. The operation and maintenance (O&M) activities for offshore wind are relatively more challenging due to uncertain environmental conditions than onshore and due to this, vessel routing for offshore on-site repair is remain complex and unreliable. Here, an improved data-driven decision tool is proposed to robust the vessel routing for O&M tasks under numerous environmental conditions. A novel data-driven technique based on operational datasets is presented to incorporate weather uncertainties, such as wind speed, wave period and wave height (significantly influence offshore crew repair works), into the O&M decision-making process. Results show: (1) The inclusion of weather conditions improves the O&M model uncertainty and accuracy, (2) the implementation of a model allowing weather conditions to evolve has been added to vary the probabilities of successful transfers throughout the day, and (3) the reduction of risk of transfer failure by 15%. These conclusions are further supported by the performance error metrics and uncertainty calculations. Last but not least, by generating a variety of policies for consideration, this tool gave wind turbine operators a systematic and transparent way to evaluate trade-offs and enable choices pertaining to offshore O&M. The full paper highlights the strengths and weaknesses of the proposed technique for offshore vessel routing as well as how the environmental conditions affect them.
Keywords: Wind turbines; O&M; vessel routing; Machine learning; weather uncertainty