1. Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs
- Author
-
Peng Chen, Jia-hao Chen, and Zhiqiang Hu
- Subjects
lcsh:QH1-199.5 ,Computer science ,020209 energy ,software in the loop (SIL) simulation ,basin experiment ,Ocean Engineering ,Software in the loop ,02 engineering and technology ,lcsh:General. Including nature conservation, geographical distribution ,Aquatic Science ,Oceanography ,01 natural sciences ,Motion (physics) ,010305 fluids & plasmas ,Set (abstract data type) ,floating offshore wind turbines (FOWT) ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Reinforcement learning ,lcsh:Science ,Water Science and Technology ,Global and Planetary Change ,reinforcement learning (RL) ,Response analysis ,Experimental data ,Control engineering ,Offshore wind power ,DARwind ,Key (cryptography) ,lcsh:Q ,artifical intelligence (AI) - Abstract
Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house programDARwindand the policy decision is provided by the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. 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 theDARwindsimulation results and that of experimental data. Many other dynamic responses that cannot be measured in basin experiment could be predicted in higher accuracy with this intelligentDARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform's motions can be predicted by AI-basedDARwindwith higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learning method, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.
- Published
- 2021