Publications by authors named "Izaskun Garrido"

The operational efficiency and lifespan of Floating Offshore Wind Turbines (FOWTs) are adversely impacted by the inherent platform motions and undesired vibrations induced by wind and wave loads. To effectively address these effects, the control of specific structural motions is of utmost importance, with platform pitch and yaw identified as the primary Degrees Of Freedom (DOF) that require attention. This study proposes a novel utilization of Oscillating Water Columns (OWCs) as a reliable and viable solution to mitigate platform pitch and yaw motions, thereby significantly enhancing the efficiency and reducing fatigue in wind turbines.

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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.

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This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives.

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Two discrete mathematical SIR models (Susceptible-Infectious-Recovered) are proposed for modelling the propagation of the SARS-CoV-2 (COVID-19) through Spain and Italy. One of the proposed models is delay-free while the other one considers a delay in the propagation of the infection. The objective is to estimate the transmission, also known as infectivity rate, through time taking into account the infection evolution data supplied by the official health care systems in both countries.

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Oscillating water column (OWC) plants face power generation limitations due to the stalling phenomenon. This behavior can be avoided by an airflow control strategy that can anticipate the incoming peak waves and reduce its airflow velocity within the turbine duct. In this sense, this work aims to use the power of artificial neural networks (ANN) to recognize the different incoming waves in order to distinguish the strong waves that provoke the stalling behavior and generate a suitable airflow speed reference for the airflow control scheme.

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Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power.

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Oceans, and particularly waves, offer a huge potential for energy harnessing all over the world. Nevertheless, the performance of current energy converters does not yet allow us to use the wave energy efficiently. However, new control techniques can improve the efficiency of energy converters.

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