Transformer bushings are critical components that support high-voltage conductors through tank walls while maintaining electrical insulation. Their premature failure can trigger cascading outages, making reliable condition assessment essential for grid reliability. Researchers have developed an advanced diagnostic system that combines machine learning with physics-based constraints to predict bushing degradation before failure occurs.
The proposed approach integrates three complementary neural network architectures—LightGBM for statistical pattern recognition, one-dimensional convolutional networks for temporal feature detection, and transformer encoders for long-range dependencies—into an ensemble framework. Rather than treating predictions as independent outputs, a cross-attention mechanism fuses their results while enforcing physical consistency. Specifically, predictions are penalized if they deviate from theoretical models governing thermal aging of insulation and moisture migration through oil, ensuring outputs remain grounded in electrochemical reality.
The system was validated using 23,400 simulated aging samples derived from four 110 kV bushings subjected to accelerated degradation under four distinct failure modes. Results demonstrate 96.14% classification accuracy for identifying insulation condition across four severity levels, with a precursor-warning capability achieving 92.3% F1-score for detecting imminent failure. Notably, the physics-guided approach outperformed conventional machine learning baselines, including particle-swarm-optimized support vector machines, by nearly 8 percentage points.
Abrasion studies confirmed that the dual physics constraints—thermal degradation and moisture intrusion models—each contributed measurably to improved robustness, particularly under severe degradation conditions where proxy residuals declined by 45%. The dual-task architecture, simultaneously optimizing classification and degradation-stage prediction, further enhanced early warning capability.
This methodology addresses a longstanding challenge in transformer diagnostics: balancing statistical learning with domain physics. By embedding theoretical understanding into the learning framework, the system provides interpretable, reliable early warnings that grid operators can act upon before catastrophic failure, supporting predictive maintenance strategies and extending equipment life.
