Simulation annealing diagnosis algorithm method for optimized forecast of the dynamic response of floating offshore wind turbines

Design of floating offshore wind turbines (FOWTs) needs reliable and innovative technologies to overcome the challenges on how to better predict the dynamic responses in terms of aero-hydro-servo-elastic disciplines. This paper aims to demonstrate the optimized prediction of the dynamic response of FOWTs by Simulation annealing diagnosis algorithm (SADA). SADA is an Artificial Intelligence technology-based method, which utilizes the advantages of numerical simulation, basin experiment and machine learning algorithms. The actor network in deep deterministic policy gradient (DDPG) is adopted to take actions to adjust the Key disciplinary parameters (KDPs) in each loop according to the feedback of 6DOF motions of platform in dynamic response analysis. The results demonstrated that the mean values of the platform's motions and rotor axial thrust force could be predicted with higher accuracy. On this basis, other physical quantities that designers are more concerned about but cannot be obtained from experiments and actual measurements will be predicted by SADA with more credibility. This SADA method differs from traditional supervised learning applications in renewable energy, which do not need to be provided physical quantities with strong direct correlation. All targets can be artificially set for SADA to obtain a better self-learning performance. In general, designers can use SADA to get a more accurate and optimized prediction of the dynamic response of FOWTs, especially those physical quantities that cannot be directly obtained through the basin experiments.