Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines

Supervisory control and data acquisition (SCADA) data-based wind turbine blade icing detection has been widely studied due to its low cost and easy access. However, SCADA data often present severe class imbalance and thus challenge accurate icing detection. Moreover, since data distribution discrepa...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2024-01, Vol.73, p.1-1
Hauptverfasser:	Yue, Ruxue, Jiang, Guoqian, Jin, Xiaohang, He, Qun, Xie, Ping
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Alignment Blade icing detection Blades Class imbalance Classification Data distribution discrepancy Data models Feature extraction Learning Loss weight assignment method Monitoring Performance enhancement Spatio-temporal alignment transfer learning Supervisory control and data acquisition Training Transfer learning Turbine blades Turbines Wind turbine Wind turbines
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