Application and Prospects of Digital Technologies in Operation and Maintenance of Deep-Sea Offshore Wind Turbines

• • Floating offshore wind O&M accounts for >20% of total life-cycle cost, with average electricity cost exceeding 3 times that of fixed-bottom offshore wind, necessitating digital solutions to achieve cost parity. • • China's offshore wind installed capacity reached 3.5×10^7 kW by 2023, with 46.4% of new installations having unit capacities ≥10 MW, driving the need for automated O&M to manage larger turbines and reduce downtime. • • Deep learning models, such as LSTM for tower bending strain prediction and semi-supervised mapping for bearing fault diagnosis, achieve high accuracy in early fault detection, reducing unplanned maintenance and extending component lifespan. • • Digital twin technology enables real-time health monitoring and predictive maintenance, with applications in corrosion fatigue prognosis of high-strength bolts and gearbox fault detection, potentially reducing O&M costs by up to 30%.