16 May 2023
 | 16 May 2023
Status: this preprint is currently under review for the journal WES.

A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype

Emmanuel Branlard, Jason Jonkman, Cameron Brown, and Jiatian Zang

Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim at estimating the fatigue life-time of the tower. Our digital twin solution integrates: 1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, 2) an aerodynamic estimator, and 3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration), and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work, or the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads with an accuracy of the order of 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results appears promising and demonstrates the possibility to use digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin, to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.

Emmanuel Branlard et al.

Status: open (until 18 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-50', Anonymous Referee #1, 29 May 2023 reply

Emmanuel Branlard et al.

Emmanuel Branlard et al.


Total article views: 400 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
314 78 8 400 2 4
  • HTML: 314
  • PDF: 78
  • XML: 8
  • Total: 400
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 16 May 2023)
Cumulative views and downloads (calculated since 16 May 2023)

Viewed (geographical distribution)

Total article views: 400 (including HTML, PDF, and XML) Thereof 400 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 01 Jun 2023
Short summary
In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.