the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype
Emmanuel Branlard
Jason Jonkman
Cameron Brown
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)
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RC1: 'Comment on wes-2023-50', Anonymous Referee #1, 29 May 2023
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General comments
The paper addresses the “construction” of a digital twin for floating wind turbines that is verified against numerical simulations in a state-of-the-art offshore code and validated against real-world data. At present, the first commercial floating wind turbines are being installed and digital tools as the one presented in this article can be fundamental for their success. Therefore, the article is relevant to the international scientific community and falls within the scope of WES.
In general, the objectives and hypotheses of the article are clear. The methods are valid and can be reproduced by the reader, also thanks to the fact that the source code of the digital twin will be shared in a GitHub repository once the article will be published. The analyses presented by authors are valid and backed up by widely used simulations tools and previous research. Results are outlined in a clear way and the article is generally well structured in all its parts (text, figures, mathematics).
For all this reasons, the article deserves to be considered for publication in WES after being revised.
As a general comment, I see the tools presented in this article are derived from those developed in previous works about digital twins for bottom-fixed turbines. I suggest the authors to point out in the introduction the additional challenges of making a digital tool for a wind turbine with floating foundations. It may sound trivial, but I would explain why one cannot simply use tools derived for bottom-fixed turbines and where these are inaccurate.
Specific comments are reported below, and technical corrections are in the attached document.
Specific comments
- 102-103. The OpenFAST model is complemented with a closed-loop controller: is it based on a reference controller like ROSCO, are you allowed to give the reader some information about it? The controller parameters are tuned so that OpenFAST simulations match SCADA data for selected operating conditions. Can you add something about the controller tuning?
- Can you say something more about the assumption of rotational symmetry of the platform? How do you think it might affect your study and its results?
- 137-138. The capabilities of the Python code are not clear. If important for the rest of the study, I would explain better how the Python tools work and how it carries out the operations listed at lines 137-138
- 139-147. This paragraph does not convey adequately the difference between OpenFAST and WELIB. I understand that: 1) WELIB is somehow simpler (and less accurate?) than OpenFAST for nonlinear simulations 2) WELIB is easier to linearize and since it is based on an analytical approach it provides better understanding of the wind turbine physics. I suggest the authors to work a bit on this paragraph to help the reader understand the complementarity of the two simulation tools with respect to the objectives of this study.
- 146, “to obtain analytical linear models and gain physical intuitiveness on the model”. From this sentence it's not clear the difference between a linear model obtained in WELIB and in OpenFAST. In the previous sentence it is said that WELIB can perform nonlinear simulations, so the difference for the linearized model is in how the model is obtained. I think you should briefly mention this issue and introduce what is explained in the next sections.
- 169, “in two different ways”. It's not clear the added value of performing the linearization with Python and the HydroDyn module. I suggest the authors to focus on one of the two, or clarify why it is important to show in this study the results obtained with the two methodologies.
- 180-181. Not clear what is Q0. I think you should rember that the degress of freedom of Mh are the platform motions which are only a subset of q. You should also recall the dimensions of Q0 and its structure.
- 3.3 Verification of the linear models. I suggest introducing this paragraph explaining why you used free decay simulations for verification of the linear models and no other types of simulation. Moreover, which is the purpose of simulations without hydrostatic restoring?
- 383, “adequate filtering of the input measurements can be used to tune the energy content at high frequencies”. Which is the impact of the frequency content above 1 Hz on fatigue loads? How meaningful it is compared to low-frequency harmonics? If you think they could be interesting, I suggest you add results of DEL obtained based on low-pass filtered signals.
- 421-422, “this is a crude first-order approximation”. Can you add some words about what you are neglecting using the tower top load cell?
- 428, “… or that the state estimator is failing”. Do you think that sensor issues can explain the different offset?
- 450, “gain-scheduling of the linear model, using nonlinear models”. You should briefly explain how these techniques can improve the results you already got.
Technical corrections
See attached document.
Emmanuel Branlard et al.
Emmanuel Branlard et al.
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