Relative Motion Prediction for Integrated Floating Offshore Wind Turbine Installation Scheme Based on Data-Driven
Published in International Conference on Ocean, Offshore and Arctic Engineering (OMAE), 2024
This research addresses the challenge of unpredictable relative motions between preassembled floating wind turbine components and foundations during offshore mating operations. By simulating installation scenarios across diverse sea states and utilizing statistical motion data (mean values and central moments) as inputs, a backpropagation neural network model is developed to forecast the maximum and minimum relative displacements and velocities at critical mating points. The model demonstrates high prediction accuracy (with R² values up to 0.95 and low MSE), providing crucial early warnings of potential hazards and supporting operational safety decisions for the novel catamaran-based installation method. This data-driven approach enhances the feasibility of installing floating wind turbines in deeper waters.
Recommended citation: Can Ma, Taiyu Zhang, Zongyuan Yang, Yang An, Xiang Yuan Zheng, and Zhengru Ren*. (2024). "Relative Motion Prediction for Integrated Floating Offshore Wind Turbine Installation Scheme Based on Data-Driven." ASME 2024 OMAE.
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