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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.
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
URL: https://doi.org/10.1016/j.oceaneng.2021.109814
DOI: 10.1016/j.oceaneng.2021.109814
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