Data-driven Optimisation of Offshore Wind Operations & Maintenance

数据驱动的海上风电运营优化

• 批准号: 2588528
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2588528
• 关键词: Data; driven; Optimisation; Offshore; Wind

Offshore Wind (OW) continues to grow globally at a rapid pace, with 57 GW currently installed [1] and growth estimates of 630GW by 2050 [2]. To facilitate this rapid growth costs must continue to be brought down. With turbine manufacturers struggling for further cost reductions [3], reducing operations and maintenance (O&M) costs for wind farms offer opportunities. Currently O&M costs are estimated at 30% of the lifetime costs of a wind farm [4]. With offshore wind farms (OWFs) getting larger, moving further offshore and floating offshore wind becoming more prevalent it is increasingly important to develop and deploy optimised O&M strategies to continue to drive down costs. Furthermore, there is an increased focus on decarbonisation of the supply chain and operational activities of wind farms. To meet the Paris Agreement goals, optimisation of maintenance for a reduction in emissions is essential.Wind farms produce a wealth of data that can be used to develop predictive strategies. The main systems on the wind turbine (WT) are: The Supervisory Control and Data Acquisition (SCADA) system and the condition monitoring system (CMS). These can be combined with maintenance reports; met-ocean data; operational reports; vessel data and stock levels to create a fully optimised maintenance system.

海上风电（OW）在全球范围内继续快速增长，目前已安装57吉瓦[1]，预计到2050年将增长630吉瓦[2]。为了促进这种快速增长，必须继续降低成本。随着涡轮机制造商努力进一步降低成本[3]，降低风电场的运营和维护（O&M）成本提供了机会。目前，O&M成本估计为风电场寿命成本的30%[4]。随着海上风电场（OWF）变得越来越大，进一步离岸和浮动海上风电变得越来越普遍，开发和部署优化的O&M策略以继续降低成本变得越来越重要。此外，供应链及风力发电场营运活动的脱碳亦日益受到关注。为了实现《巴黎协定》的目标，优化维护以减少排放至关重要。风力发电场产生了丰富的数据，可用于制定预测策略。风力涡轮机（WT）上的主要系统是：监控和数据采集（SCADA）系统和状态监测系统（CMS）。这些可以与维护报告、海洋数据、操作报告、船舶数据和库存水平相结合，以创建一个完全优化的维护系统。