Offshore Wind Foundation Diagnostic Monitoring System (OFDiMoS)

海上风电基础诊断监测系统 (OFDiMoS)

• 批准号: 10021835
• 金额: $ 16.1万
• 依托单位: SEA AND LAND PROJECT ENGINEERING LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10021835
• 关键词: Offshore; Wind; Foundation; Diagnostic; Monitoring

Offshore Wind Turbines (OWTs) are a scientifically-mature renewable energy technology with potential to significantly lower society's carbon emissions. There have been rapid recent developments in this sector, with more efficient and larger turbines being fabricated, requiring higher support towers and larger foundations. These new structures are increasingly unlike previous models and the design methods used to estimate their foundation behaviour are becoming less applicable and reliable. New OWTs are also being installed further offshore in less certain and more challenging ground conditions. This means that these developments might behave differently than expected, increasing the risk of damage occurrence due to poorly understood interactions between harsh wind and waves with these structures. Foundation construction and maintenance constitute 20-40% of overall windfarm cost and therefore any effort made to increase their reliability has direct financial benefits for UK consumers, by lowering the cost of energy production.This project will develop a computer tool to identify damage and real-time changes in OWT behaviour by analysing data from sensors installed on these machines. The tool creates a digital twin of the structure-foundation system, which can carry out real-time damage diagnosis. A digital twin is a virtual representation of an object that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to assist decision-making. For OWTs, this represents an exact counterpart (or twin) of the physical structure. The tool will use advanced geotechnical models to estimate the soil-structure interaction behaviour, updated in real-time, which is highly novel. This system will inform how the real structure is performing, and will also enable future performance predictions be made, with estimation of remaining useful life. This is timely given the recent rapid development of the sector and the increasing uncertainty surrounding the behaviour of new OWT installations. Digital twins have been used in other industries but have not yet been successfully applied to OWT asset management. A specific innovation is the use of installed sensors on the target structure that will feedback real-time dynamic data, enabling near-instantaneous condition assessments be obtained, facilitating rapid maintenance interventions should damage be detected during transient events (storms). Longer term trends over days/weeks can be analysed to identify slower damage accumulation such as corrosion. Long-term performance can be benchmarked against design cases used to size the foundations, facilitating informed decision-making surrounding service life extension with minimised risk, which ultimately lowers energy costs.

海上风力涡轮机（OWT）是一种科学上成熟的可再生能源技术，具有显著降低社会碳排放的潜力。该领域最近发展迅速，制造的涡轮机效率更高，尺寸更大，需要更高的支撑塔和更大的基础。这些新的结构越来越不同于以前的模型，用于估计其基础性能的设计方法变得不那么适用和可靠。在不太确定和更具挑战性的地面条件下，新的OWT也被安装在更远的海上。这意味着这些发展可能会表现得与预期不同，由于对恶劣的风和波浪与这些结构之间的相互作用知之甚少，因此增加了发生损坏的风险。基础建设和维护占风电场总成本的20-40%，因此任何提高其可靠性的努力都可以通过降低能源生产成本为英国消费者带来直接的经济利益。该项目将开发一种计算机工具，通过分析安装在这些机器上的传感器的数据来识别损坏和OWT行为的实时变化。该工具创建了结构-基础系统的数字孪生模型，可以进行实时损伤诊断。数字孪生是对象的虚拟表示，跨越其生命周期，从实时数据中更新，并使用模拟，机器学习和推理来辅助决策。对于OWT，这表示物理结构的精确对应物（或孪生）。该工具将使用先进的岩土模型来估计土壤-结构相互作用行为，并实时更新，这是非常新颖的。该系统将告知真实的结构如何执行，并且还将使得能够进行未来性能预测，并估计剩余使用寿命。考虑到该部门最近的快速发展以及新的OWT装置行为的不确定性日益增加，这是及时的。数字孪生已经在其他行业使用，但尚未成功应用于OWT资产管理。一个具体的创新是使用安装在目标结构上的传感器，这些传感器将反馈实时动态数据，从而能够获得接近瞬时的状况评估，如果在瞬态事件（风暴）期间检测到损坏，则有助于快速维护干预。可以分析几天/几周内的长期趋势，以确定腐蚀等较慢的损坏累积。长期性能可以根据用于确定基础尺寸的设计案例进行基准测试，从而促进围绕使用寿命延长做出明智的决策，同时最大限度地降低风险，最终降低能源成本。