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Deep anomaly detection in horizontal axis wind turbines using GraphConvolutional Autoencoders for Multivariate Time series 被引量:1
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作者 Eric Stefan Miele Fabrizio Bonacina Alessandro Corsini 《Energy and AI》 2022年第2期79-91,共13页
Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequent... Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches. 展开更多
关键词 Wind turbine Condition monitoring Deep anomaly detection SCADA data graph convolutional autoencoder Multivariate Time series Early fault detection
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