Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 3
Articles | Volume 11, issue 3
https://doi.org/10.5194/wes-11-1057-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1057-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 3
Research article
 | 
01 Apr 2026
Research article |  | 01 Apr 2026

Failure classification of wind turbine operational conditions using hybrid machine learning

Marcela Rodrigues Machado, Amanda Aryda Silva Rodrigues de Sousa, Jefferson da Silva Coelho, and Rafael de Oliveira Teloli

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Short summary
We have developed a method using artificial intelligence to detect and classify faults in wind turbines before major damage occurs. By analyzing data from multiple sensors, we can identify issues even under changing weather conditions, such as temperature and wind. This improves reliability, reduces downtime, and lowers maintenance costs, supporting cleaner and more affordable energy through stable production.
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