[Objective] Inter-turn short circuit is the most frequent type of fault in distribution transformers

and if a minor inter-turn short circuit is not dealt with in time

it may evolve into a serious inter-turn short circuit

endangering the stable operation of the transformer. This study aims to explore a high-precision diagnostic method capable of effectively identifying incipient faults to achieve early warning. [Methods] Firstly

for the early short-circuit condition

a method using nonlinear arc characteristics to replace the impedance at the short-circuit point was proposed. A high-impedance

low-energy arc was used to simulate an incipient inter-turn short circuit in the transformer. The winding current signals before and after the fault were extracted

and the distortion characteristics of magnetic flux variation was analyzed. Subsequently

a multi-scale residual network model based on the attention mechanism was proposed

achieved efficient identification of interturn short-circuit faults. [Results] Simulation and experimental results indicated that the fault current obtained through nonlinear arc impedance simulation was significantly smaller than that from linear impedance simulation. The inter-turn short circuit led to an increase in the leakage flux density within the air gap between the core and the windings

resulted in an overall rise in electromagnetic losses. The method proposed in this paper was effectively able to distinguish between the three states: normal operation

minor inter-turn short circuit

and severe inter-turn short circuit

achieving a test accuracy of 0.975. [Conclusion] The research can effectively reveal the electromagnetic response characteristics of the transformer’s early inter-turn short circuit

and lay a theoretical reference for the early fault detection.