Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligent DARwind. SADA weights KDPs by DDPG algorithms' actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing the DARwind simulation results and that of experimental data. Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house program DARwind and the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. 3College of Shipbuilding Engineering, Harbin Engineering University, Harbin, Chinaįloating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses.2Guangdong Electric Power Design Institute Co., Ltd., China Energy Engineering Group, Guangzhou, China.1School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom.Peng Chen 1 *, Jiahao Chen 2 and Zhiqiang Hu 1,3
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