Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2063-2024
https://doi.org/10.5194/wes-9-2063-2024
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

Viewed

Total article views: 582 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
445 108 29 582 19 18
  • HTML: 445
  • PDF: 108
  • XML: 29
  • Total: 582
  • BibTeX: 19
  • EndNote: 18
Views and downloads (calculated since 21 Mar 2024)
Cumulative views and downloads (calculated since 21 Mar 2024)

Viewed (geographical distribution)

Total article views: 582 (including HTML, PDF, and XML) Thereof 552 with geography defined and 30 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2024
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.
Altmetrics
Final-revised paper
Preprint