A Comparative Analysis of Self‐Rectifying Turbines for the Mutriku Oscillating Water Column Energy Plant

Otaola, Erlantz; Garrido, Aitor J.; Lekube, Jon; Garrido, Izaskun

doi:10.1155/2019/6396904

Cited by 11 publications

(10 citation statements)

References 46 publications

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“…The common device that was used as the energy conversion device was the converted loudspeaker [21] and a linear motor [22]. The bi-directional turbines were used for oscillatory flow of the ocean wave [23]. The potential of using a bi-directional turbine for thermoacoustics was also reported [24].…”

Section: Introductionmentioning

confidence: 99%

A 3D Printed bi-directional Turbine for Thermoacoustic Standing Wave Environment at an Atmospheric Pressure

Fatimah Al Zahrah Mohd Saat

2024

AREFMHT

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This paper reports a study on the use of an in-house 3D printed bi-directional turbine for energy conversion in a standing wave thermoacoustic environment that is operated at an atmospheric pressure. The 3D printed turbine was placed at two locations along the resonator and the data were recorded for several range of flow frequency and flow amplitude. It is found that maximum output is achieved when flow is not at resonance. Resonance of the standing wave inside the rig was found at 106 Hz but the bi-directional turbine performs better at a lower frequency of 85 Hz.

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Section: Introductionmentioning

confidence: 99%

A 3D Printed bi-directional Turbine for Thermoacoustic Standing Wave Environment at an Atmospheric Pressure

Fatimah Al Zahrah Mohd Saat

2024

AREFMHT

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“…In this way, the air inside the chamber is compressed and decompressed, propelling self-rectifying air turbines located in the upper part of the chambers. This same technology is currently being used in Mutriku’s MOWC wave power plant 8 , 9 . Therefore, FOWTs and OWCs compose some of the most promising technologies for harnessing clean energy and could be combined in a hybrid platform, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Ahmad

M’zoughi

Aboutalebi

et al. 2023

Sci Rep

Self Cite

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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.

show abstract

“…In this way, the air inside the chamber is compressed and decompressed, propelling self-rectifying air turbines located in the upper part of the chambers. This same technology is currently being used in Mutriku's MOWC wave power plant 8,9 . Therefore, FOWT and OWC compose some of the most promising technologies for harnessing clean energy and could be combined in a hybrid platform, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Regressive Machine-Learning Approach to the Non-linear Complex FAST Model for Hybrid Floating Offshore Wind Turbines with Integrated Oscillating Water Columns

Ahmad

M’zoughi

Aboutalebi

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

A Comparative Analysis of Self‐Rectifying Turbines for the Mutriku Oscillating Water Column Energy Plant

Cited by 11 publications

References 46 publications

A 3D Printed bi-directional Turbine for Thermoacoustic Standing Wave Environment at an Atmospheric Pressure

A 3D Printed bi-directional Turbine for Thermoacoustic Standing Wave Environment at an Atmospheric Pressure

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

A Regressive Machine-Learning Approach to the Non-linear Complex FAST Model for Hybrid Floating Offshore Wind Turbines with Integrated Oscillating Water Columns

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