Xiujuan ZENG

Distribution-network voltage transformers have been checked mainly by de-energizing and comparing them against standard units, which is slow and interrupts supply. A State Grid Wuhan team embedded switching-station Kirchhoff voltage constraints and a cable thermal-balance equation as soft loss terms in a neural network, added radial-basis multi-scale time features and residual attention weighting, and estimated transformer error online. On a laboratory physical simulation platform the method cut RMSE by 35.2% and MAE by 36.4% and raised R² by 2.63 points over the runner-up MRSA-DGCN; in a field pilot in a 10 kV Wuhan area it flagged one out-of-tolerance transformer, matching the de-energized check.

Distribution-network voltage transformers have been checked mainly by de-energizing and comparing them against standard units, which is slow and interrupts supply. A State Grid Wuhan team embedded switching-station Kirchhoff voltage constraints and a cable thermal-balance equation as soft loss terms in a neural network, added radial-basis multi-scale time features and residual attention weighting, and estimated transformer error online. On a laboratory physical simulation platform the method cut RMSE by 35.2% and MAE by 36.4% and raised R² by 2.63 points over the runner-up MRSA-DGCN; in a field pilot in a 10 kV Wuhan area it flagged one out-of-tolerance transformer, matching the de-energized check.