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

Irfan Ahmad; Fares M'zoughi; Payam Aboutalebi; Izaskun Garrido; Aitor J Garrido

doi:10.1038/s41598-023-28703-z

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

Sci Rep. 2023 Jan 27;13(1):1499. doi: 10.1038/s41598-023-28703-z.

Authors

Irfan Ahmad¹, Fares M'zoughi², Payam Aboutalebi², Izaskun Garrido², Aitor J Garrido²

Affiliations

¹ Automatic Control Group-ACG, Institute of Research and Development of Processes-IIDP, Department of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao EIB/BIE, The University of the Basque Country-UPV/EHU, Po Rafael Moreno no3, Bilbao, 48013, Spain. irfanahmad.irfan@ehu.eus.
² Automatic Control Group-ACG, Institute of Research and Development of Processes-IIDP, Department of Automatic Control and Systems Engineering, Faculty of Engineering of Bilbao EIB/BIE, The University of the Basque Country-UPV/EHU, Po Rafael Moreno no3, Bilbao, 48013, Spain.

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.

Abstract

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