Open access
Sep 2026
A Comparative Hybrid Deep Learning Approach for Robust Stator–Rotor Fault Identification in DFIGs Using FFT‐Based Features and CNN–LSTM–Transformer Models Under Nonstationary Wind Turbulence
Reliable fault diagnosis in doubly fed induction generators (DFIGs) is essential for the stable and efficient operation of wind energy systems under nonstationary conditions caused by wind turbulence. This paper proposes a hybrid deep learning framework that combines FFT‐based frequency‐domain feature extraction with a...
Mehdi Mohammadian Mehr, Mohsen Saniei, Mahmood Joorabian et al.
· Engineering Reports · 0 citations