Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

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dc.contributor.authorMartinez-Luengo, Maria
dc.contributor.authorKolios, Athanasios
dc.contributor.authorWang, Lin
dc.date.accessioned2016-06-28T11:00:00Z
dc.date.available2016-06-28T11:00:00Z
dc.date.issued2016-06-09
dc.description.abstractOffshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE).en_UK
dc.identifier.citationMaria Martinez-Luengo, Athanasios Kolios, Lin Wang, Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm, Renewable and Sustainable Energy Reviews, Volume 64, October 2016, pp91-105en_UK
dc.identifier.issn1364-0321
dc.identifier.urihttp://dx.doi.org/10.1016/j.rser.2016.05.085
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/10036
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectOffshore wind turbinesen_UK
dc.subjectStructural health monitoringen_UK
dc.subjectStatistical Pattern Recognition Paradigmen_UK
dc.subjectSensorsen_UK
dc.subjectStatistical model developmenten_UK
dc.titleStructural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigmen_UK
dc.typeArticleen_UK

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