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Progress and Trends in Damage Detection Methods, Maintenance, and Data-driven Monitoring of Wind Turbine Blades – A Review

涡轮机 可再生能源 结构健康监测 涡轮叶片 工程类 风力发电 无损检测 分层(地质) 可靠性工程 状态监测 机械工程 结构工程 电气工程 生物 放射科 构造学 古生物学 医学 俯冲
作者
Kyungil Kong,Kirsten Dyer,Christopher J. Payne,Ian Hamerton,Paul M. Weaver
出处
期刊:Renewable Energy Focus [Elsevier]
卷期号:44: 390-412 被引量:52
标识
DOI:10.1016/j.ref.2022.08.005
摘要

In recent decades, renewable energy has attracted attention as a viable energy supply. Among renewable energy sources, offshore wind energy has been considerably growing since longer and larger wind turbine composite blades were deployed. The manufacture of the longer and larger composite blades leads to more wind energy production. However, the wind turbine composite blades are susceptible to damage and defects due to multiple structural loads and harsh operating environments in service. Hence, condition monitoring and maintenance of wind turbine composite blades require in-depth investigation to prevent structural damage and defects and to improve remaining lifetime of the composite structure. The types of damage and defects in wind turbine composite blades are typically delamination, debonding, and cracks, which are influenced by the intrinsic structural nonlinearities, manufacturing process stage, and harsh environmental impacts in service. For these reasons, the regular condition monitoring of the composite blades is required to assess degradation in performance and structural condition to minimise levelised energy costs for maintenance. To improve reliability and sustainability, data-driven inspection with digital twin technology is reviewed as a trend of condition monitoring frameworks. Advanced functional materials to potentially assist current non-destructive testing (NDT) methods or to be utilised as self-sensing performance are reviewed. From manufacturing to the system level, a comprehensive review on progress and trends of monitoring of wind turbine composite blades is carried out including physics-based NDT methods, data fusion in sensor networks, automated system, mechanics, and digital twin technology with the environmental coupling.
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