Machine learning methods for wind turbine condition monitoring: A review
作者： Adrian Stetco , Fateme Dinmohammadi , Xingyu Zhao , Valentin Robu , David Flynn
DOI: 10.1016/J.RENENE.2018.10.047
关键词: Condition monitoring 、 Wind power 、 Model selection 、 Machine learning 、 Artificial intelligence 、 Fault detection and isolation 、 Support vector machine 、 Computer science 、 Artificial neural network 、 Feature selection 、 Decision tree
摘要: … The model for predicting generator temperature is built using five variables (out of 47 SCADA signals): power, ambient temperature, nacelle temperature, generator cooling air and …
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