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

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.