A 1D CNN with Exact Grouped SHAP Attribution for Transformer Magnetizing Inrush Identification

Reliable transformer differential protection requires fast discrimination between magnetizing inrush and internal faults in the presence of residual flux, harmonic voltage, direct current (DC) bias, measurement noise, and current-transformer (CT) saturation. This study develops a compact one-dimensional convolutional neural network (1D CNN) that acts directly on a three-phase differential-current window and interprets its decision with exact grouped SHAP. A calibrated 50 kVA, 10 kV/400 V, 50 Hz three-limb model was implemented in MATLAB R2023b; to remove an alignment shortcut, event onsets were randomized over the window for both classes on a balanced, independently split data set. At the default decision threshold the CNN reached 91.11% accuracy with 99.26% inrush recall and 82.96% specificity (AUC 0.9833) on an independent test set. Selecting the threshold on the validation set raised accuracy to 94.81% and specificity to 91.11% without materially changing inrush recall. The compact direct-sequence model matched a recurrent LSTM, while an image CNN traded lower inrush recall for higher specificity; both learned models were far more stable than fixed second-harmonic and dead-angle criteria, and the CNN retained competitive performance on windows as short as 10–20 ms. Exact grouped SHAP, recomputed over all correctly classified records with a multi-record background and onset-binned controls, yields a cautious phase-time interpretation rather than a claim of universal field performance.