Identification methods for offshore wind turbine structures and foundations

在复杂载荷条件下，海上风力涡轮机的基础建模存在很多不确定性。因此，海上风力涡轮机基础的设计往往过于保守，导致成本过高。然而，来自结构健康监测和机器学习领域的相对成熟的方法提供了对海上风电基础和结构建模的有效性和缺点提供准确反馈的能力，从而降低了设计、运行和延长寿命的成本。因此，研究需要交付和验证用于OWF结构属性估计的数据驱动方法，这些方法可以为工业基础和结构设计过程提供信息并嵌入其中。关键的研究问题是：如何应用系统识别方法来提供设计关键海上风基础和结构特性的准确估计？这些方法，包括算法和传感系统，如何最好地应用于现场监测数据？如何利用估计的属性为海上风电场的设计、维护和寿命重新评估提供反馈？如何优化设计传感器系统，为结构评估或设计方法开发提供数据。

There is a lot of uncertainty around modelling the foundations of offshore wind turbines under complex loading regimes. As a result, the design of offshore wind turbine foundations are often conservative leading to excess costs. However, relatively mature methods from the field of structural health monitoring and machine learning offer the ability to provide accurate feedback on the efficacy and shortcomings of offshore wind foundation and structural modelling, leading to cost reduction in design, operation and life extension. Research is therefore required to deliver and validate data driven methods for OWF structural property estimation, which can inform and be embedded within industrial foundation and structural design processes.The key research questions are: How can system identification methods be applied to deliver accurate estimation of design critical offshore wind foundation and structural properties? How are these methods, including the algorithms and sensing systems, best applied to field monitored data? How can the estimated properties be utilised to deliver feedback to the design, maintenance and lifetime reassessment of offshore windfarms? How might sensor systems be optimally designed to provide data for structural assessment or design method development.