Insulation-Condition Assessment of Oil-Immersed Transformer Bushings Based on a Physics-Proxy-Residual-Guided Cross-Attention Ensemble Neural Network

This study proposes a physics-proxy-residual-guided cross-attention ensemble neural network (PGAE-NN) for oil-immersed transformer bushing insulation-condition assessment and early warning. Eight core indicators are selected from twelve candidates via Pearson correlation and Fisher discriminant analyses, with four insulation levels defined with reference to IEEE Std C57.104-2019. LightGBM, 1D-CNN, and Transformer Encoder serve as heterogeneous base learners for statistical, local temporal, and global temporal features. A cross-attention meta-learner fuses their outputs by penalizing predictions that deviate from Arrhenius thermal-aging and Fick moisture-migration proxy residuals. A piecewise regularization strategy and a classification-precursor dual-task objective further enhance degradation-stage adaptivity and early warning. Validation uses 23,400 accelerated-aging samples from four 110 kV bushings under four typical defects. PGAE-NN achieves 96.14% test accuracy (F1 = 0.9613; AUC = 0.9835) and 96.36% ± 0.54% five-fold cross-validation accuracy, outperforming PSO-SVM and Traditional Stacking by 7.99 and 2.69 percentage points, respectively. The precursor-warning F1 reaches 0.923, and ablation studies confirm the meta-learner, dual physics constraints, and dual-task design contribute 1.82, 1.46, and 1.11 percentage points, respectively. The proxy residual under severe conditions drops by 44.9%, demonstrating that physics-guided fusion constrains predictions within physically consistent boundaries.