Enhancing Stability and Performance of Hybrid Offshore Wind Platforms: A Novel Fuzzy Logic Control Approach with Computational Machine Learning

Harnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has introduced a groundbreaking concept of hybrid offshore platforms, offering immense potential for energy absorption, reduced dynamic response, load mitigation, and improved cost efficiency. The primary goals of this study revolve around two key objectives: (i) the development of a regression-based modeling technique for the hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and (ii) the implementation of a customized fuzzy-based control mechanism to ensure platform stability. To achieve these objectives, computational Machine Learning (ML) tools, specifically Artificial Neural Networks (ANNs), are utilized to replicate the behavior of the detailed numerical model of the FOWTs integrated with OWCs. Subsequently, a Fuzzy Logic Control (FLC) scheme is employed to establish a structural controller that effectively mitigates unwanted vibrations. The experimental results confirm the potential of ANN-based modeling as a simpler yet effective alternative to complicated nonlinear NREL-5MW FOWT dynamical models. Furthermore, the use of the FLC system enhances platform stability in a variety of wind and wave conditions.