Authors:
Daison Stallon
1
;
Ichrak Zaid
2
and
Yolanda Vidal
1
;
3
Affiliations:
1
Control, Data, and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politécnica de Catalunya (UPC), Barcelona, Spain
;
2
Commande Numérique des Procédés Industriels (CONPRI) National School of Engineers of Gabes, University of Gabes, Tunisia
;
3
Institut de Matemátiques de la UPC, BarcelonaTech, IMTech, Pau Gargallo 14, 08028 Barcelona, Spain
Keyword(s):
Wind Turbines, Wind Speed, Fault Diagnosis, Wind Energy Conversion Systems, Control Monitoring, Doubly Fed Induction Generators, Machine Learning.
Abstract:
Doubly-Fed Induction Generator (DFIG)-based Wind Energy Conversion Systems (WECS) are critical in modern electricity generation due to their ability to enhance energy capture and seamlessly integrate with the electrical grid. However, maintaining reliability and minimizing maintenance costs are essential to ensure consistent energy production. This research presents an innovative method for fault detection and diagnosis in DFIG-based WECS. The approach leverages independent component analysis-based correlation coefficient for precise fault identification. Additionally, an enhanced multihead cross attention with bi-directional long short term memory classifier is employed to accurately categorize different fault types. To further improve classifier’s performance, the multi-strategy enhanced orchard algorithm is implemented, focusing on regulating active and reactive power variations, harmonics in rotor current, and voltage in the DC link. The proposed method is evaluated using MATLAB
working platform and demonstrates a high accuracy rate of 98% compared to other techniques.
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