Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines
ABSTRACT The increasing scale of wind farms demands more efficient approaches to turbine monitoring and maintenance. Here, we present an innovative framework that combines enhanced kernel principal component analysis (KPCA) with ensemble learning to revolutionize normal behavior modeling (NBM) of wi...
Saved in:
| Main Authors: | Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-05-01
|
| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.70055 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines
by: Jiazhi Dai, et al.
Published: (2024-12-01) -
Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review
by: Musavir Hussain, et al.
Published: (2024-01-01) -
Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies
by: Adaiton Oliveira-Filho, et al.
Published: (2024-12-01) -
Life extension of wind turbine drivetrains by means of SCADA data: Case study of generator bearings in an onshore wind farm
by: Kelly Tartt, et al.
Published: (2024-12-01) -
Robust ensemble classifier for advanced synthetic aperture radar target classification in diverse operational conditions
by: Noor Rahman, et al.
Published: (2025-04-01)