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Aspects of structural health and condition monitoring of offshore wind turbines
Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4290406/ https://www.ncbi.nlm.nih.gov/pubmed/25583864 http://dx.doi.org/10.1098/rsta.2014.0075 |
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author | Antoniadou, I. Dervilis, N. Papatheou, E. Maguire, A. E. Worden, K. |
author_facet | Antoniadou, I. Dervilis, N. Papatheou, E. Maguire, A. E. Worden, K. |
author_sort | Antoniadou, I. |
collection | PubMed |
description | Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector. |
format | Online Article Text |
id | pubmed-4290406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-42904062015-02-28 Aspects of structural health and condition monitoring of offshore wind turbines Antoniadou, I. Dervilis, N. Papatheou, E. Maguire, A. E. Worden, K. Philos Trans A Math Phys Eng Sci Articles Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector. The Royal Society Publishing 2015-02-28 /pmc/articles/PMC4290406/ /pubmed/25583864 http://dx.doi.org/10.1098/rsta.2014.0075 Text en http://creativecommons.org/licenses/by/4.0/ © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Antoniadou, I. Dervilis, N. Papatheou, E. Maguire, A. E. Worden, K. Aspects of structural health and condition monitoring of offshore wind turbines |
title | Aspects of structural health and condition monitoring of offshore wind turbines |
title_full | Aspects of structural health and condition monitoring of offshore wind turbines |
title_fullStr | Aspects of structural health and condition monitoring of offshore wind turbines |
title_full_unstemmed | Aspects of structural health and condition monitoring of offshore wind turbines |
title_short | Aspects of structural health and condition monitoring of offshore wind turbines |
title_sort | aspects of structural health and condition monitoring of offshore wind turbines |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4290406/ https://www.ncbi.nlm.nih.gov/pubmed/25583864 http://dx.doi.org/10.1098/rsta.2014.0075 |
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