Winding Shorts Detection in SCIG using Convolutional Neural Networks - Ferreira, Rhendson Alexandre (2023)
In the rapidly evolving landscape of renewable energy, unprecedented growth is occurring in wind power generation. At the core of this growth are Squirrel Cage Induction Generators (SCIGs), known for their simplicity, robustness, and cost-effectiveness. However, the reliability of these generators is often compromised by winding short faults, specifically in the stator winding. This paper presents a novel approach for early detection of these faults using Convolutional Neural Networks (CNNs), thereby enhancing the reliability and reducing the maintenance costs of wind power generation systems.