Abstract
This study presents the adaptive flexible controller frequency optimization method (AFCFOM) as a means of minimizing dynamic loads and structural response in large-scale floating wind turbines, with the goal of enhancing reliability and reducing the frequency of failures. AFCFOM employs a flexible proportional integral (PI) controller frequency, allowing the wind turbine to autonomously adapt to different wind and wave conditions while minimizing structural response. The AFCFOM framework utilizes Latin hypercube sampling for spatial compression, two Bayesian neural networks (BNN) for training and mapping, and particle swarm optimization (PSO) for controller frequency seeking, along with the extreme response surface method for processing the response dataset. OpenFSAT was used for raw data collection and method effect validation. The simulation results demonstrate that the application of AFCFOM effectively reduces the extreme values and variance of the axial displacement at the blade top of the floating offshore wind turbine blade and enhances the robustness of the floating platform. The study provides an overview of the potential effects of adaptive flexible controller frequency on wind turbines, serving as a reference for future wind turbine control optimization research.
Published Version
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