A regressive machine-learning approach to the non-linear complex FAST model for hybrid floating offshore wind turbines with integrated oscillating water columns

Abstract

Offshore wind energy is getting increasing attention as a clean alternative to the currently scarce fossil fuels mainly used in Europe’s electricity supply. The further development and implementation of this kind of technology will help fighting global warming, allowing a more sustainable and decarbonized power generation. In this sense, the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) devices arise as a promising solution for hybrid renewable energy production. In these systems, OWC modules are employed not only for wave energy generation but also for FOWTs stabilization and cost-efficiency. Nevertheless, analyzing and understanding the aero-hydro-servo-elastic floating structure control performance composes an intricate and challenging task. Even more, given the dynamical complexity increase that involves the incorporation of OWCs within the FOWT platform. In this regard, although some time and frequency domain models have been developed, they are complex, computationally inefficient and not suitable for neither real-time nor feedback control. In this context, this work presents a novel control-oriented regressive model for hybrid FOWT-OWCs platforms. The main objective is to take advantage of the predictive and forecasting capabilities of the deep-layered artificial neural networks (ANNs), jointly with their computational simplicity, to develop a feasible control-oriented and lightweight model compared to the aforementioned complex dynamical models. In order to achieve this objective, a deep-layered ANN model has been designed and trained to match the hybrid platform’s structural performance. Then, the obtained scheme has been benchmarked against standard Multisurf-Wamit-FAST 5MW FOWT output data for different challenging scenarios in order to validate the model. The results demonstrate the adequate performance and accuracy of the proposed ANN control-oriented model, providing a great alternative for complex non-linear models traditionally used and allowing the implementation of advanced control schemes in a computationally convenient, straightforward, and easy way.

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

“…In their work [97,98], the researchers explored the utilization of an MLP network for modeling a hybrid floating wave energy-wind turbine platform. Subsequently, they introduced a fuzzy logic control system to implement a structural controller aimed at mitigating undesirable vibrations within the platform.…”

“…First, Ref. [97] delved into the creation of a control-oriented regressive model for a hybrid FOWT with an oscillating water column wave energy converters platform. The primary objective was to leverage the predictive capabilities and computational simplicity of deep-layered MLP to develop a feasible, lightweight, control-oriented model in contrast to complex dynamical models.…”

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

“…Ahmad et al explored the application of an MLP network to model a hybrid floating wave energy-wind turbine platform and subsequently introduced a fuzzy logic control system to implement a structural controller aimed at mitigating undesirable vibrations within the platform [80,81]. The team employed OpenFAST and WAMIT for hydrodynamic modeling and the results showed that the MLP-based model could promise a simpler yet effective alternative to more complex nonlinear dynamical models for NREL 5 MW floating offshore wind turbines.…”

Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling

“…Limited research has been conducted on creating surrogate models that accurately capture the dynamic response of offshore wind turbines. For instance, Ahmad et al 57 utilized artificial neural networks to develop a surrogate model for the aero‐hydro‐servo elastic performance of hybrid floating offshore wind turbines. The model was trained on mid‐fidelity data; however, there is a need to develop surrogates that can accurately capture extreme dynamic phenomena by utilizing high‐fidelity datasets.…”