Probabilistic digital twin for reliability-based maintenance optimization of offshore wind turbines

This paper introduces a novel digital twin framework for Offshore Wind Turbine (OWT) operations that enhances risk-based structural integrity assessments and optimizes maintenance strategies to improve cost efficiency. The key contribution of this framework lies in its integration of real-time environmental and operational data, which enables probabilistic failure prediction and facilitates proactive maintenance planning. Further, the framework automates data collection, forecasts OWT conditions, and incorporates a comprehensive decision-support system to facilitate risk-informed decision-making. It incorporates probabilistic condition prediction and maintenance cost estimation, effectively accounting for uncertainties in weather conditions, labor costs, and electricity market fluctuations. The efficacy of the framework is demonstrated through two case studies, focusing on optimizing OWT construction site selection and maintenance strategies under extreme weather conditions. The demonstrated case studies validate the robustness and adaptability of the proposed digital twin framework and underscore its potential to enhance decision-making in the context of lifecycle management within renewable energy sectors.