Event-Driven Short-Circuit Fault Classification in SCIG with Confidence-Weighted Aggregation

Abstract

Squirrel Cage Induction Generators are widely used in renewable energy systems, but their reliability is threatened by stator short-circuit faults. Traditional steady-state diagnostic methods are often rendered ineffective in modern applications because closed-loop controllers actively mask fault signatures. To address this challenge, this work introduces an event-driven fault classification framework specifically designed to detect and classify masked stator faults, namely inter-turn and inter-winding short-circuits. Instead of computationally expensive continuous signal analysis, the proposed methodology isolates brief, high-frequency transient disturbances using an adaptive, derivative-enhanced event detection mechanism. Discriminative features are extracted from these localized transient intervals, with a focus on derivative-based voltage representations. Finally, a novel confidence-weighted aggregation strategy is introduced to combine event-level predictions into robust file-level decisions. Experimental validation using a high-resolution dataset demonstrates that the proposed framework significantly improves diagnostic accuracy. The results show that derivative-based voltage features provide superior class separability, achieving a file-level Area Under the Curve of approximately 0.89, demonstrating the effectiveness of the targeted transient evaluation strategy in closed-loop systems.