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Linear and Nonlinear Features and Machine Learning for Wind Turbine Blade Ice Detection and Diagnosis

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dc.contributor.author Arcos Jiménez, Alfredo
dc.contributor.author García Márquez, Fausto Pedro
dc.contributor.author Borja Moraleda, Victoria
dc.contributor.author Gómez Muñoz, Carlos Quiterio
dc.date.accessioned 2019-03-07T13:56:42Z
dc.date.available 2019-03-07T13:56:42Z
dc.date.issued 2018-08
dc.identifier.citation Renewable Energy es_ES
dc.identifier.issn 0960-1481
dc.identifier.uri http://hdl.handle.net/10578/20134
dc.description.abstract The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis. Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results. es_ES
dc.format application/pdf es_ES
dc.language.iso en es_ES
dc.publisher Elsevier es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.subject Feature Extraction es_ES
dc.subject NARX es_ES
dc.subject NLPCA es_ES
dc.subject NCA es_ES
dc.subject Machine Learning es_ES
dc.subject Guided waves es_ES
dc.subject Ice es_ES
dc.subject Wind turbine blade es_ES
dc.subject Classifiers es_ES
dc.title Linear and Nonlinear Features and Machine Learning for Wind Turbine Blade Ice Detection and Diagnosis es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.relation.projectID DPI2015-67264-P es_ES
dc.identifier.DOI https://doi.org/10.1016/j.renene.2018.08.050


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