A Regressive Machine-Learning Approach to the Non-linear Complex FAST Model for Hybrid Floating Offshore Wind Turbines with Integrated Oscillating Water Columns Article Swipe
Irfan Ahmad
,
Fares M’zoughi
,
Payam Aboutalebi
,
Izaskun Garrido
,
Aitor J. Garrido
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2183351/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2183351/v1
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 (FOWT) with Oscillating Water Column (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 FOWT stabilization, cost-efficiency and prognosis. 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 OWC-FOWT platforms. The main objective is to take advantage of the predictive and forecasting capabilities of the deep-layered artificial neural networks (ANN), 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, composing 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.
Related Topics
Concepts
Offshore wind power
Computer science
Context (archaeology)
Oscillating Water Column
Wind power
Renewable energy
Artificial neural network
Model predictive control
Marine engineering
Energy (signal processing)
Control (management)
Engineering
Artificial intelligence
Electrical engineering
Statistics
Wave energy converter
Mathematics
Paleontology
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2183351/v1
- https://www.researchsquare.com/article/rs-2183351/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307523510
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307523510Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2183351/v1Digital Object Identifier
- Title
-
A Regressive Machine-Learning Approach to the Non-linear Complex FAST Model for Hybrid Floating Offshore Wind Turbines with Integrated Oscillating Water ColumnsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-28Full publication date if available
- Authors
-
Irfan Ahmad, Fares M’zoughi, Payam Aboutalebi, Izaskun Garrido, Aitor J. GarridoList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2183351/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-2183351/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-2183351/latest.pdfDirect OA link when available
- Concepts
-
Offshore wind power, Computer science, Context (archaeology), Oscillating Water Column, Wind power, Renewable energy, Artificial neural network, Model predictive control, Marine engineering, Energy (signal processing), Control (management), Engineering, Artificial intelligence, Electrical engineering, Statistics, Wave energy converter, Mathematics, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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