Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator
dc.contributor.author | Ghefiri, Khaoula | |
dc.contributor.author | Bouallègue, Soufiene | |
dc.contributor.author | Garrido Hernández, Izaskun | |
dc.contributor.author | Garrido Hernández, Aitor Josu | |
dc.contributor.author | Haggège, Joseph | |
dc.date.accessioned | 2018-12-11T11:34:24Z | |
dc.date.available | 2018-12-11T11:34:24Z | |
dc.date.issued | 2018-05 | |
dc.identifier.citation | Sensors 18(5) : (2018) // Article ID 1317 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/30251 | |
dc.description.abstract | 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. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances. | es_ES |
dc.description.sponsorship | This work was supported in part by the University of the Basque Country (Universidad del Pais Vasco UPV / Euskal Herriko Unibertsitatea EHU) through Project PPG17/33, by MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER, EU) and by the Basque Goverment through Elkartek. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/DPI2015-70075-R | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | artificial intelligence | es_ES |
dc.subject | artificial neural networks control | es_ES |
dc.subject | back-to-back converter | es_ES |
dc.subject | data processing | es_ES |
dc.subject | Doubly Fed Induction Generator (DFIG) | es_ES |
dc.subject | Maximum Power Point Tracking (MPPT) | es_ES |
dc.subject | pitch regulation | es_ES |
dc.subject | power control | es_ES |
dc.subject | Tidal Stream Generator (TSG) | es_ES |
dc.subject | fed induction generator | es_ES |
dc.subject | grid voltage conditions | es_ES |
dc.subject | wind turbines | es_ES |
dc.subject | feedforward networks | es_ES |
dc.subject | ambient intelligence | es_ES |
dc.subject | marquardt algorithm | es_ES |
dc.subject | stability analysis | es_ES |
dc.subject | energy generation | es_ES |
dc.subject | systems | es_ES |
dc.title | Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/18/5/1317 | es_ES |
dc.identifier.doi | 10.3390/s18051317 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
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Except where otherwise noted, this item's license is described as 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).