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

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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
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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.
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