Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO147: Estimating Mooring Tension of a Hywind Scotland Floating Wind Turbine Using Field Data and PINNs
Magnus Daniel Kallinger, Project Engineer, Catalonia Institute for Energy Research (IREC)
Abstract
The rapid growth of floating offshore wind farms has increased the need for advanced monitoring strategies to optimize operations and reduce maintenance costs. Traditional underwater sensors used to measure mooring-line tensions are costly, failure-prone, and difficult to maintain in harsh marine environments. This work presents a physics-informed neural networks (PINN) framework to develop algorithms for estimating mooring-line tensions in the Hywind Scotland spar-type floating wind turbine. The approach combines a deep neural network (DNN) with the quasi-static mooring solver MoorPy, trained on field data from a 2018 measurement campaign that included structural motion, environmental conditions, and tension measurements for all six mooring bridles. By incorporating physical constraints into the learning process, the method improves generalization and robustness under data-scarce conditions. Results demonstrate a mean root mean square error (RMSE) reduction of approximately 31% compared to a purely data-driven model, accurately capturing both steady-state and transient behaviors. These findings highlight the potential of PINN-based algorithms to complement or replace underwater instrumentation, enabling predictive maintenance and reducing lifecycle costs for floating offshore wind farms.
No recording available for this poster.
