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

Abstract

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. This study focuses on two primary objectives: firstly, the development of a regression-based modeling method for a hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and secondly, the implementation of a customized fuzzybased control mechanism aimed at ensuring platform stability. To achieve these objectives, Artificial Neural Networks (ANNs) are employed as computational Machine Learning (ML) tools to accurately simulate the complex behavior of the hybrid system. 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.