Online Monitoring of Transformer Winding Faults Based on Pulse Coupling Injection

Aiming at the problems with traditional transformer winding deformation detection, requiring power outages, low signal-to-noise ratios for online monitoring, and insufficient feature extraction, this paper proposes a live monitoring and intelligent diagnosis method based on pulse-coupled injection. At the hardware level, a semi-ring capacitive coupling sensor is developed and designed, which realizes non-contact injection of high-frequency pulse signals and high-SNR extraction without a power outage. The reliability of the system under complex working conditions is verified by field experiments on multiple actual 110 kV transformers. At the algorithm level, an innovative MSCNN-Transformer-PGA deep composite model fused with prior electromagnetic physical knowledge is constructed and combined with the transformer equivalent circuit model. The model uses a multi-scale convolution to extract local details of frequency response signals, adopts Transformer to establish the global sequence dependence, and introduces a Physics-Guided Attention mechanism (PGA) to adaptively focus on the key fault physical frequency bands. The experimental results show that the proposed method effectively overcomes electromagnetic noise interference, and the fault classification accuracy of single-modal pulse frequency response data reaches 97.6%, providing a high-precision online monitoring solution for the safe operation and maintenance of transformers.