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Application of SADA method on full-scale measurement data for dynamic responses prediction of Hywind floating wind turbines

Lookup NU author(s): Peng Chen, Professor Zhiqiang Hu



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Floating offshore wind turbines (FOWTs) are promising solutions for offshore renewable energy harvesting, withthe successful installation and operation of the world’s first commercial floating wind farm, Hywind Scotland, in2017. However, both academia and industry are still constantly facing challenges in the aspects of cost reduction,monitoring, safety and sustainability improvement for the design and maintenance of FOWTs. The purpose ofthis paper is to demonstrate an engineering application of a novel Artificial Intelligence knowledge-basedmethod, named SADA, on the full-scale measurement data of an Hywind FOWT. The SADA method wasapplied to perform numerical optimization and dynamic responses prediction of the FOWTs, based on the fullscaledata from one Hywind FOWT in Scotland. The methodology of SADA and the key technology of theapplication are introduced firstly. Then, the selection of Key Discipline Parameters (KDPs) is introduced, followedwith the training of AI-based numerical models with full-scale measurement data, including Floatermotions, wind, wave and current data. After that, the numerical model imbedded in SADA is trained to beintelligent for the objective Hywind FOWT under different sea states. The intelligent SADA model is used to docomparisons and predictions. The comparison results show that using SADA method, the AI-trained numericalmodel can predict the motions of Hywind supporting Floater in higher accuracy. In addition, other physicalquantities that cannot be obtained directly in full-scale measurement easily but are of great concern by industry,can also be obtained from a more believable perspective. This AI-based SADA method brings an innovative visionfor FOWTs’ full-scale measurement technology in the future.

Publication metadata

Author(s): Chen P, Jia C, Ng C, Hu Z

Publication type: Article

Publication status: Published

Journal: Ocean Engineering

Year: 2021

Volume: 239

Print publication date: 01/11/2021

Online publication date: 10/09/2021

Acceptance date: 04/09/2021

Date deposited: 10/09/2021

ISSN (print): 0029-8018

ISSN (electronic): 1873-5258

Publisher: Elsevier Ltd


DOI: 10.1016/j.oceaneng.2021.109814


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