Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Abstract

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonization process. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequent failures 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 wind turbines based on SCADA data. We introduce a promising neural architecture, namely a Graph Convolutional Autoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. This structure improves the unsupervised learning capabilities of Autoencoders by considering individual sensor measurements together with the nonlinear correlations existing among signals. On this basis, we developed a deep anomaly detection framework that was validated on 12 failure events occurred during 20 months of operation of four wind turbines. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms by outperforming other two recent neural approaches.