Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 6
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.
https://doi.org/10.5194/wes-8-893-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 6
Review article
 | 
05 Jun 2023
Review article |  | 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, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

Related authors

Leveraging Signal Processing and Machine Learning for Automated Fault Detection in Wind Turbine Drivetrains
Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-114,https://doi.org/10.5194/wes-2024-114, 2024
Preprint under review for WES
Short summary
Spatio-Temporal Graph Neural Networks for Power Prediction in Offshore Wind Farms Using SCADA Data
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-113,https://doi.org/10.5194/wes-2024-113, 2024
Preprint under review for WES
Short summary
Modular deep learning approach for wind farm power forecasting and wake loss prediction
Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-94,https://doi.org/10.5194/wes-2024-94, 2024
Preprint under review for WES
Short summary
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci., 9, 1507–1526, https://doi.org/10.5194/wes-9-1507-2024,https://doi.org/10.5194/wes-9-1507-2024, 2024
Short summary
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022,https://doi.org/10.5194/wes-7-1869-2022, 2022
Short summary
More articles (1)

Related subject area

Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024,https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Wind Energ. Sci., 9, 2063–2086, https://doi.org/10.5194/wes-9-2063-2024,https://doi.org/10.5194/wes-9-2063-2024, 2024
Short summary
Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024,https://doi.org/10.5194/wes-9-2017-2024, 2024
Short summary
A machine learning-based approach for active monitoring of blades pitch misalignment in wind turbines
Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-100,https://doi.org/10.5194/wes-2024-100, 2024
Revised manuscript accepted for WES
Short summary
Operation and maintenance cost comparison between 15 MW direct-drive and medium-speed offshore wind turbines
Orla Donnelly, Fraser Anderson, and James Carroll
Wind Energ. Sci., 9, 1345–1362, https://doi.org/10.5194/wes-9-1345-2024,https://doi.org/10.5194/wes-9-1345-2024, 2024
Short summary
More articles (10)

Cited articles

Bangalore, P. and Tjernberg, L. B.: Self evolving neural network based algorithm for fault prognosis in wind turbines: A case study, in: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 7–10 July 2014, Durham, 1–6, https://doi.org/10.1109/PMAPS.2014.6960603, 2014. a, b
Bangalore, P. and Tjernberg, L. B.: An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings, IEEE T. Smart Grid, 6, 980–987, https://doi.org/10.1109/TSG.2014.2386305, 2015. a, b
Bangalore, P., Letzgus, S., Karlsson, D., and Patriksson, M.: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox, Wind Energy, 20, 1421–1438, 2017. a, b, c, d
Beretta, M., Cárdenas, J., Koch, C., and Cusidó, J.: Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders, Appl. Sci.-Basel, 10, 8649, https://doi.org/10.3390/app10238649, 2020. a, b, c, d, e, f, g
Beretta, M., Julian, A., Sepulveda, J., Cusidó, J., and Porro, O.: An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing, Sensors, 21, 1–20, 2021. a, b, c, d
More articles (55)
Download
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.
Read more
Altmetrics
Final-revised paper
Preprint