Digital Twins for Offshore Wind Operation and Maintenance: A Review

ABSTRACT Offshore wind has become a key focus for achieving carbon neutrality. However, offshore wind turbines face considerable operational and maintenance challenges due to harsh conditions and difficult access. Integrating digital twin technology helps support operation and maintenance activities. This study conducts a systematic literature review of recent wind energy digital twin research. It aims to identify the underlying modelling trend of wind energy digital twins, assess their implementation maturity and their compliance with the adopted digital twin definition, classify the functions supporting offshore wind operation and maintenance and examine the integration of emerging enabling technologies. The findings reveal a scale‐dependent redistribution of modelling approaches: physics‐based approaches dominant at the component level, to data‐driven approaches prominent at the turbine level, and both data‐driven and hybrid approaches at the wind farm level. A functional analysis further shows that predictive maintenance and real‐time monitoring dominate digital twin applications at the component level, whereas wind farm‐level digital twin increasingly demonstrate integrated capabilities that combine multiple functions. Meanwhile, enabling technologies, including the Internet of Things, cloud computing and machine learning, are increasingly being integrated into digital twin systems, establishing a foundation for more intelligent and autonomous operation and maintenance strategies. The findings also reveal that most existing digital twin research remain at the pre‐implementation stage, and only a limited number satisfy the requirements of a full digital twin. Therefore, significant gaps remain between research and operational implementation, particularly regarding effective feedback mechanisms from virtual models to physical assets.