Wind turbine sensor placement optimisation for digital twin development (OSP)

该项目将专注于开发一种最先进的整体监测系统，该系统可以收集、分析和解释从海上风电场获得的所有组件级别的数据，并远程做出操作和维护(O&M)决策。将开发先进的机器学习算法，以改进可以使用SCADA数据获取的信息。这将告知在哪里可以使用SCADA来取代更昂贵的仪器，以及在哪里部署传感器以开发更具信息量的预测状况监测系统。将开发一种结合基于物理的模型分析和采集数据的后处理的数字孪生兄弟。这将促进更好的运维规划，从而实现更高效的维护流程，降低运维成本，减少对离岸技术人员的需求，从而最大限度地提高OWTs的可用性，并最终降低统一的能源成本。该项目将JR Dynamic Ltd(交易名称为Transport Dynamic-TD)与Unasys Limited(UNASYS)(开发数字模型的专家)和离岸可再生能源弹射器(OREC)(英国离岸可再生能源旗舰创新研究中心)汇聚在一起。JR Dynamic Ltd是一家屡获殊荣的工程公司，在设计和制造定制监测系统方面拥有20多年的经验，可以在具有挑战性的环境中测量关键参数。该联盟以交付该项目所需的供应链的关键要素以及在相邻部门的开发能力为特色。该联盟将与美国塔夫茨大学领导的一个团队合作，该团队已批准了一个名为“基于物理的数字双胞胎的最佳传感器布局”的项目，该项目将于2021年1月开始。他们的重点将是开发一种新的SPDT贝叶斯同化框架(BAF)，用于结构疲劳监测的载荷推断(LI)、模型不确定性(MU)、基础非线性(FN)和最优传感器布置(OSP)。这两个财团将交换SCADA数据以进行模型验证，并将合作制定路线图，将他们的技术整合和商业化，以开发世界领先的全球开发解决方案。该项目是两国之间的首次此类双边合作，努力为未来更强有力的合作铺平道路。

This project will focus on developing a state-of-the-art, holistic monitoring system that can collect, analyse, and interpret all component level data acquired from an offshore wind farm and make Operations & Maintenance (O&M) decisions remotely. Advanced machine learning algorithms will be developed to improve the information that can be acquired using SCADA data. This will inform where SCADA can be used in place of more expensive instrumentation, and where it is critical to deploy sensors in order to develop a more informative predictive condition monitoring system.A digital twin combining physics based model analysis and post-processing of acquired data will be developed. This will facilitate better O&M planning leading to more efficient maintenance processes, reduced O&M costs and a reduced need for technicians going offshore, thus maximising the availability of OWTs and ultimately reducing the levelized cost of energy.The project brings together JR Dynamics Ltd (trading as Transmission Dynamics - TD), an award-winning engineering company with over 20 years' experience of designing and manufacturing bespoke monitoring systems to measure critical parameters in challenging environments, with Unasys Limited (UNASYS), experts at developing digital models, and the Offshore Renewable Energy Catapult (OREC), the UK's flagship innovation and research centre for offshore renewables. The consortium features key elements of the supply chain required to deliver the project, as well as the capacity to exploit in adjacent sectors.The consortium will work in collaboration with a team in the U.S., led by Tufts University, who have an approved project titled "Optimal Sensor Placement for Physics-Based Digital Twins" commencing in January 2021\. Their focus will be to develop a new SPDT Bayesian Assimilation Framework (BAF) for load inference (LI), model uncertainty (MU), foundation nonlinearities (FN), and optimal sensor placement (OSP) for structural fatigue monitoring. The two consortia will exchange SCADA data for model validation and will work together to develop a roadmap for integrating and commercialising their technologies to develop a world leading solution for global exploitation.This project is the first of it's kind bilateral collaboration between the two countries, striving to pave the way for stronger future collaboration.