2020
DOI: 10.1016/j.promfg.2020.11.030
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Investigation of icing causes on wind turbine rotor blades using machine learning models, minimalistic input data and a full-factorial design

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Cited by 5 publications
(1 citation statement)
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“…The main objective was to detect damage in the blades by involving three main learning mechanisms: (i) a CNN for the extraction of the best features, (ii) TL algorithms to improve generalization, and (iii) a random forest set to improve the blade defect detection process. In an attempt to predict the gradual formation of ice on the rotor blades of WTs, research by Kreutz et al [116] developed a data-based ice prediction approach using two different ML methods, namely the SVM and the DNN (deep neural network). The analyzed data were collected from the SCADA monitoring system with the help of specific sensors installed in WTs from a wind farm located in Germany with around 10 WTs.…”
Section: Bladesmentioning
confidence: 99%
“…The main objective was to detect damage in the blades by involving three main learning mechanisms: (i) a CNN for the extraction of the best features, (ii) TL algorithms to improve generalization, and (iii) a random forest set to improve the blade defect detection process. In an attempt to predict the gradual formation of ice on the rotor blades of WTs, research by Kreutz et al [116] developed a data-based ice prediction approach using two different ML methods, namely the SVM and the DNN (deep neural network). The analyzed data were collected from the SCADA monitoring system with the help of specific sensors installed in WTs from a wind farm located in Germany with around 10 WTs.…”
Section: Bladesmentioning
confidence: 99%