Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 11
Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2063-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-9-2063-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 11
Research article
 | 
05 Nov 2024
Research article |  | 05 Nov 2024

Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements

Ali Dibaj, Mostafa Valavi, and Amir R. Nejad

Related authors

Fatigue-constrained gradient-based design optimization of main bearing–shaft systems for floating wind turbine drivetrains
Vasudev Gupta and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-105,https://doi.org/10.5194/wes-2026-105, 2026
Preprint under review for WES
Short summary
Condition monitoring of wind turbine drivetrains: state-of-the-art technologies, recent trends, and future outlook
Kayacan Kestel, Xavier Chesterman, Donatella Zappalá, Simon Watson, Mingxin Li, Edward Hart, James Carroll, Yolanda Vidal, Amir R. Nejad, Shawn Sheng, Yi Guo, Matthias Stammler, Florian Wirsing, Ahmed Saleh, Nico Gregarek, Thao Baszenski, Thomas Decker, Martin Knops, Georg Jacobs, Benjamin Lehmann, Florian König, Ines Pereira, Pieter-Jan Daems, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 11, 2103–2155, https://doi.org/10.5194/wes-11-2103-2026,https://doi.org/10.5194/wes-11-2103-2026, 2026
Short summary
An Open-Access Integrated Hierarchical Optimization Framework applied to a 15 MW Medium-Speed Offshore Wind Turbine Drivetrain
Felix C. Mehlan and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-90,https://doi.org/10.5194/wes-2026-90, 2026
Preprint under review for WES
Short summary
Floating offshore wind in Japan: addressing the challenges, efforts, and research gaps for large-scale commercialization
Ryota Wada, Amir R. Nejad, Kazuhiro Iijima, Junji Shimazaki, Mihaela Ibrion, Shinnosuke Wanaka, Hideo Nomura, Yoshitaka Mizushima, Takuya Nakashima, and Ken Takagi
Wind Energ. Sci., 11, 1013–1056, https://doi.org/10.5194/wes-11-1013-2026,https://doi.org/10.5194/wes-11-1013-2026, 2026
Short summary
Impact of Wake Impingement on the Fatigue Loads in the Main Bearings and Blades of Offshore Wind Turbines
Jakob Gebel, Pieter-Jan Daems, Diederik van Binsbergen, Veronica Liverud Krathe, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-13,https://doi.org/10.5194/wes-2026-13, 2026
Preprint under review for WES
Short summary
More articles (7)

Cited articles

Ágoston, K.: Fault Detection of the Electrical Motors Based on Vibration Analysis, 8th International Conference Interdisciplinarity in Engineering, INTER-ENG 2014, 9–10 October 2014, Tirgu Mures, Romania, Proc. Tech., 19, 547–553, https://doi.org/10.1016/j.protcy.2015.02.078, 2015. a
Ali, M. Z., Shabbir, M. N. S. K., Liang, X., Zhang, Y., and Hu, T.: Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals, IEEE T. Ind. Appl., 55, 2378–2391, https://doi.org/10.1109/TIA.2019.2895797, 2019. a
Bernier, S., Merkhouf, A., and Al-Haddad, K.: Stray flux and air gap flux experimental measurement and analysis in large hydro generators, Proceedings – 2023 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2023, Newcastle upon Tyne, England, 13–14 April 2023, 1–6, https://doi.org/10.1109/WEMDCD55819.2023.10110944, 2023. a
Borchersen, A. B. and Kinnaert, M.: Model-based fault detection for generator cooling system in wind turbines using SCADA data, Wind Energy, 19, 593–606, 2016. a, b
Cai, B., Hao, K., Wang, Z., Yang, C., Kong, X., Liu, Z., Ji, R., and Liu, Y.: Data-driven early fault diagnostic methodology of permanent magnet synchronous motor, Expert Syst. Appl., 177, 115000, https://doi.org/10.1016/j.eswa.2021.115000, 2021. a
More articles (59)
Download
Short summary
This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
Read more
Share
Special issue
Electro-mechanical interactions in wind turbines
Altmetrics
Final-revised paper
Preprint