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.

浮动式海上风力涡轮机（FOWT）的设计需要可靠和创新的技术，以克服如何更好地预测航空-水-油-弹性理论方面的动态响应方面的挑战。本文旨在通过仿真退火诊断算法（SADA）演示FOWTs动态响应的优化预测。SADA是一种基于人工智能技术的方法，它利用了数值模拟，盆地实验和机器学习算法的优势。根据动态响应分析中平台的6DOF运动的反馈，采用深度确定性策略梯度（DDPG）中的参与者网络来采取行动，以调整每个循环中的关键学科参数（KDP）。结果表明，平台运动和转子轴向推力的平均值可以更高的精度进行预测。在此基础上，设计人员更关心但无法从实验中获得的其他物理量，而SADA将以更高的可信度预测实际测量值。此SADA方法与可再生能源中传统的监督学习应用程序不同，后者无需提供具有很强直接相关性的物理量。可以为SADA人工设置所有目标，以获得更好的自学习性能。通常，设计人员可以使用SADA对FOWT的动态响应（尤其是那些无法通过盆地实验直接获得的物理量）进行更准确和优化的预测。