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Methods and Tools for the Operational Reliability Optimisation of Large-Scale Industrial Wind Turbines

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dc.contributor.author Ruiz de la Hermosa, Raúl
dc.contributor.author García Márquez, Fausto Pedro
dc.date.accessioned 2017-02-08T10:08:30Z
dc.date.available 2017-02-08T10:08:30Z
dc.date.issued 2015-05-21
dc.identifier.citation Advances in Intelligent Systems and Computing es_ES
dc.identifier.issn 2194-5357
dc.identifier.uri http://hdl.handle.net/10578/12196
dc.description.abstract Wind turbines (WT) maintenance management is in continuous development to improve the reliability, availability, maintainability and safety (RAMS) of WTs, and to achieve time and cost reductions. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee correct levels of RAMS. A fault detection and diagnosis methodology is proposed for large-scale industrial WTs. The method applies the wavelet and Fourier analysis to vibration signals. A number of turbines (up to 3) of the same type will be instrumented in the same wind farm. The data collected from the individual turbines will be fused and analysed together in order to determine the overall reliability of this particular wind farm and wind turbine type. It is expected that data fusion will allow a significant improvement in overall reliability since the value of the information gained from the various condition monitoring systems will be enhanced. Effort will also focus on the successful application of dependable embedded computer systems for the reliable implementation of wind turbine condition monitoring and control technologies. es_ES
dc.format application/pdf es_ES
dc.language.iso en es_ES
dc.publisher Springer Berlin Heidelberg es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.subject Wind turbine es_ES
dc.subject Maintenance management es_ES
dc.subject Vibration es_ES
dc.subject FFT es_ES
dc.title Methods and Tools for the Operational Reliability Optimisation of Large-Scale Industrial Wind Turbines es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.relation.projectID Grant Agreement Number: 322430. es_ES
dc.identifier.DOI 10.1007/978-3-662-47241-5_99


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