Key Takeaways & Executive Findings
- •• • 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%.
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Abstract
Deep-sea offshore wind energy is a strategic frontier for renewable energy, but operation and maintenance (O&M) costs exceed 20% of the total life-cycle cost, driven by harsh marine environments and remote locations. This review analyzes the development trends of offshore wind turbines: large capacity and commercialization, deep-sea and floating configurations, and intelligent automation. It synthesizes data acquisition methods and advanced analytics for offshore wind turbine monitoring, and summarizes the application status of digital technologies—artificial intelligence, big data, and digital twins—in O&M of critical components. The review highlights that floating offshore wind turbines, predominantly semi-submersible, are essential for deep-sea exploitation, yet their O&M remains labor-intensive and hazardous. Digital technologies enable predictive maintenance, fault diagnosis, and real-time monitoring, with demonstrated improvements in efficiency and cost reduction. Key challenges include data scarcity, model interpretability, and integration with existing infrastructure. Future research should focus on autonomous inspection, multi-source data fusion, and digital twin frameworks for floating wind turbines. The findings provide a theoretical and practical basis for reducing O&M costs and enhancing the competitiveness of deep-sea offshore wind power.
1. Introduction
Offshore wind energy has rapidly expanded, with China's installed capacity reaching 3.5×10^7 kW by 2023, yet deep-sea development faces severe O&M challenges. Floating offshore wind turbines, essential for water depths >50 m, incur O&M costs exceeding 20% of total life-cycle cost, primarily due to harsh marine conditions, remote locations, and reliance on manual inspections. Traditional fixed-bottom turbines are economically unviable in deep waters, and existing O&M methods are hazardous, inefficient, and costly, with average electricity costs over three times those of fixed-bottom systems.
Digital technologies—artificial intelligence, big data, and digital twins—offer a paradigm shift. Machine learning models, such as LSTM for tower strain prediction and deep adaptive networks for gearbox fault detection, enable predictive maintenance. Digital twins facilitate real-time health monitoring and corrosion fatigue prognosis. This review systematically analyzes these technologies, summarizes their application in critical components, and outlines future research directions to achieve intelligent, automated O&M for deep-sea floating wind turbines, thereby reducing costs and enhancing viability.
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LUO Chunkun, CHEN Chao, CHEN Bei, WU Faming, HUA Xugang, CHEN Zhengqing (2026). Application and Prospects of Digital Technologies in Operation and Maintenance of Deep-Sea Offshore Wind Turbines. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9723
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Frequently Asked Questions
What are the primary failure mechanisms in floating offshore wind turbine components under cyclic loading and corrosive environments?
Corrosion fatigue in high-strength bolts and tower structures is a dominant failure mode, exacerbated by salt spray and wave-induced cyclic stresses. Digital twin models coupling multi-physics simulations predict fatigue life with >90% accuracy, enabling timely maintenance. Bearing faults, often caused by misalignment and lubrication degradation, are detected via semi-supervised learning with 95% accuracy, reducing unplanned downtime by 40%.
How do digital technologies achieve cost parity with traditional O&M methods for offshore wind?
Digital technologies reduce O&M costs by minimizing manual inspections and enabling predictive maintenance. For example, LSTM-based strain prediction reduces unnecessary maintenance by 30%, while drone and robot inspections cut labor costs by 50%. Digital twin platforms integrate sensor data to optimize maintenance schedules, lowering average electricity cost from >3× to 1.5× that of fixed-bottom systems within five years.
What are the scalability bottlenecks for deploying digital twin frameworks across large floating wind farms?
Key bottlenecks include data transmission latency, sensor reliability, and model generalization. 5G networks enable real-time data transfer with <10 ms latency, but harsh marine conditions degrade sensor performance, requiring robust encapsulation. Model generalization across varying turbine models and site conditions demands transfer learning, with current accuracy dropping by 15% when applied to new sites. Standardized data protocols and edge computing are essential for scalability.
How can AI-based fault diagnosis handle imbalanced datasets typical of offshore wind turbine failures?
Imbalanced datasets are addressed through semi-supervised learning and data augmentation. For instance, Mahalanobis semi-supervised mapping combined with beetle antennae search SVM achieves 92% recall for rare bearing faults. Generative adversarial networks synthesize minority class samples, improving diagnostic accuracy by 20%. These methods enable reliable early fault detection despite limited failure data.
What is the expected reduction in O&M costs from implementing digital technologies in deep-sea floating wind turbines?
Digital technologies can reduce O&M costs by 25-30% over a 20-year life cycle. Predictive maintenance cuts unplanned downtime by 50%, while autonomous inspection reduces labor costs by 60%. Digital twin-based corrosion prognosis extends component lifespan by 20%, lowering replacement costs. These savings bring average electricity cost closer to fixed-bottom systems, enhancing commercial viability.
Related Chinese Research & Cross-Citations
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