ITSC Fault Diagnosis of Digital Twin-Driven Transformer Based on Micromagnetic Field

Inter-turn short circuit (ITSC) faults represent a critical threat to the operational reliability of power transformers, often developing rapidly and eluding early detection. Existing diagnostic methods face challenges in fault localization due to limited observability, low sensitivity to internal disturbances, and high background interference. This paper presents a sensor-integrated Digital Twin (DT) framework for real-time ITSC fault localization, driven by micromagnetic field analysis. A finite element-based DT model is constructed to simulate the spatial distribution of leakage magnetic flux under various ITSC scenarios. Magnetic field signals are acquired from an optimized array of Hall-effect sensors deployed along the transformer winding surface. To bridge the gap between observed magnetic responses and internal fault evolution, a spatiotemporal data fusion and inversion strategy is developed. A regularized and gradient-informed two-dimensional particle swarm optimization algorithm is employed to estimate fault parameters by minimizing magnetic field mismatch under physical constraints, enabling accurate localization across fault positions and severities. Experimental validation demonstrates that the proposed DT-assisted method significantly improves the localization accuracy and responsiveness of sensor-based fault diagnostics. This approach provides a practical pathway for deploying real-time, non-invasive monitoring systems in transformer health management.