A scalable predictive maintenance model for detecting wind turbine component failures based on SCADA data

Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi and Mauro Tucci.

This work presents a novel Predictive Maintenance model for the main Wind Turbine components based on SCADA data and combination of Machine Learning and Classical Statistical approach. According to the impact of maintenance cost on the wind power Levelized Cost of Energy (LCOE), the components Gearbox, Generator, and Main Bearing have been modelled. The model was trained on historical nominal behavior periods of components specific SCADA tags. Test campaign was divided in two stages: test on historical faults for model training and validation, and Real-Time test for proper integration in the plant operators’ activities. Test on historical faults of six wind farms located in Italy and Romania, corresponding to an overall installed nominal power of 283 MW, demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Additionally, test on 12- months Real-Time phase confirm its ability to detect several anomalies, therefore allowing the operators to identify root case and schedule maintenance before reaching the catastrophic stage.




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