A Study on a Method for Diagnosing Insulation Faults in Reactors Based on the Analysis of Pulse Oscillation Parameters

Inter-turn insulation failure is the primary cause of dry-type air-core reactor burnout, yet early detection remains challenging due to weak power-frequency fault signatures. This paper proposes an integrated diagnostic framework combining impulse oscillation testing, electromagnetic simulation, and a physics-informed graph neural network. A scaled-down four-layer parallel reactor model and an impulse oscillation platform are developed to extract dynamic equivalent inductance and resistance as sensitive fault indicators. Validated finite element simulations reveal that inter-layer insulation near high-voltage terminals endures the highest electric field stress, with local field strength increasing nearly eightfold under short-circuit faults. For fault localization, a Spatio-Temporal Physics-Informed Graph Neural Network (ST-PIGNN) is constructed, representing winding topology as a heterogeneous graph and embedding electromagnetic transient equations as physical constraints. On a test set of 120 samples, the proposed method achieves 94.17% fault layer classification accuracy and 6.84% axial localization mean absolute error under low-noise conditions, and maintains 85.83% accuracy with 8.12% error under strong-noise interference. The proposed method is currently at the proof-of-concept stage, and further validation on full-scale reactors is required before field deployment.