Data-Efficient Synchronous Machine Winding Short-Circuit Faults Diagnosis Based on Frequency Response Analysis and Active Learning

Abstract

Winding short-circuit (SC) faults are a prevalent issue in synchronous machines, and the accurate and timely identification of these faults is critical for maintaining power system stability. Current methods for inspecting synchronous machine windings often rely on periodic inspections based on human expertise. Therefore, numerous studies have explored the application of deep learning (DL) models for detecting synchronous machine winding SC faults. However, these models often exhibit excessive complexity and overlook physical overhead, leading to inefficient utilization of computational power and data resources. To address these limitations, this study proposes a dual-channel DL model integrated with the active learning (AL) query strategy. In this study, winding SC faults are manually simulated on a 5-kVA synchronous machine, and corresponding frequency response analysis (FRA) data are recorded. Subsequently, the proposed method is validated on the test set and benchmarked against previous studies. Experimental results demonstrate that the proposed method significantly reduces the data annotation effort and accelerates model training to the order of seconds (under 5 s) while maintaining satisfactory accuracy ( $\geq 95$ %). Comparative experimental results further indicate that the proposed model, requiring only 1/20th of the labeled training samples used in previous studies, achieves a substantial reduction of approximately 99.8% in both model parameters and training time.