Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-893-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-893-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
05 Jun 2023
Review article |
| 05 Jun 2023
| 05 Jun 2023
Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms
Xavier Chesterman
CORRESPONDING AUTHOR
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Timothy Verstraeten
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Pieter-Jan Daems
AVRG, Vrije Universiteit Brussel, Pleinlaan 3, 1050 Brussels, Belgium
Ann Nowé
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050 Brussels, Belgium
Jan Helsen
AVRG, Vrije Universiteit Brussel, Pleinlaan 3, 1050 Brussels, Belgium
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Cited
15 citations as recorded by crossref.
- Wind turbine gearbox multi-scale condition monitoring through operational data F. Castellani et al. 10.1051/meca/2024028
- Self-Supervised Condition Monitoring for Wind Turbine Gearboxes Based on Adaptive Feature Selection and Contrastive Residual Graph Neural Network W. Yang et al. 10.3390/en18205474
- Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting C. Lee & E. Maceren 10.1049/esi2.12144
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar 10.21595/marc.2024.24232
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. 10.3390/en18010059
- Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques D. Astolfi et al. 10.3390/en16155818
- Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine X. Jin et al. 10.1109/TSTE.2024.3411884
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al. 10.3390/machines13080746
- Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains F. Jamil et al. 10.5194/wes-10-1963-2025
- IoT based monitoring system for DFIG based wind turbines under voltage dips I. Vairavasundaram et al. 10.1016/j.prime.2024.100690
- Assessing the effects of anemometer systematic errors on wind generators performance by data-driven techniques D. Astolfi et al. 10.1016/j.segan.2024.101417
- A new health indicator for rotating machinery condition monitoring under variable operation conditions through regression among vibration features M. Rao et al. 10.1016/j.ymssp.2025.113447
- Impact of vibration on wind turbine efficiency and LSTM-based power conversion prediction A. Alutaybi & C. Hamrouni 10.59400/sv2059
- A New Two‐Stage Probabilistic Remaining Useful Life Prediction Method for Wind Turbines W. Hu et al. 10.1002/we.70049
- Determining the trend behavior of the wind turbine powertrain using mechanical vibration and seasonal wind data G. Ferri et al. 10.1016/j.egyr.2024.12.019
15 citations as recorded by crossref.
- Wind turbine gearbox multi-scale condition monitoring through operational data F. Castellani et al. 10.1051/meca/2024028
- Self-Supervised Condition Monitoring for Wind Turbine Gearboxes Based on Adaptive Feature Selection and Contrastive Residual Graph Neural Network W. Yang et al. 10.3390/en18205474
- Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting C. Lee & E. Maceren 10.1049/esi2.12144
- Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges M. Krishna Menon & R. Tuladhar 10.21595/marc.2024.24232
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. 10.3390/en18010059
- Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques D. Astolfi et al. 10.3390/en16155818
- Graph Spatio-Temporal Networks for Condition Monitoring of Wind Turbine X. Jin et al. 10.1109/TSTE.2024.3411884
- Use of Artificial Neural Networks and SCADA Data for Early Detection of Wind Turbine Gearbox Failures B. Puruncajas et al. 10.3390/machines13080746
- Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains F. Jamil et al. 10.5194/wes-10-1963-2025
- IoT based monitoring system for DFIG based wind turbines under voltage dips I. Vairavasundaram et al. 10.1016/j.prime.2024.100690
- Assessing the effects of anemometer systematic errors on wind generators performance by data-driven techniques D. Astolfi et al. 10.1016/j.segan.2024.101417
- A new health indicator for rotating machinery condition monitoring under variable operation conditions through regression among vibration features M. Rao et al. 10.1016/j.ymssp.2025.113447
- Impact of vibration on wind turbine efficiency and LSTM-based power conversion prediction A. Alutaybi & C. Hamrouni 10.59400/sv2059
- A New Two‐Stage Probabilistic Remaining Useful Life Prediction Method for Wind Turbines W. Hu et al. 10.1002/we.70049
- Determining the trend behavior of the wind turbine powertrain using mechanical vibration and seasonal wind data G. Ferri et al. 10.1016/j.egyr.2024.12.019
Latest update: 29 Oct 2025
Short summary
This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
This paper reviews and implements several techniques that can be used for condition monitoring...
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