PEG-MFRN: A Physics-Informed Measurement Framework for Transparent Internal State Monitoring in High-Voltage Bushings

High-voltage bushings are critical components in power transmission systems to maintain grid reliability. However, their complex electro-thermal-mechanical coupling and sealed structures pose significant challenges for internal state sensing and fault diagnosis. Traditional methods such as finite element modeling (FEM) and partial discharge (PD) analysis often struggle to reconstruct internal stress and temperature fields due to sparse measurements, limited sensor accessibility, and the inherent ill-posedness of inverse problems. To address these issues, we propose a novel measurement method based on Physics-Informed and Experiment-Guided Multi-Field Reconstruction Network (PEG-MFRN), validated on a 110 kV high-voltage bushing. This framework integrates external discrete sensor data with governing physical equations to achieve high-fidelity reconstruction of internal stress and temperature fields, thereby enabling enhanced internal state transparency in the sense of reconstructing internal physical conditions from sparse external observations. Validation results demonstrate that PEG-MFRN outperforms conventional finite element method simulations, reducing maximum stress and temperature errors by 69.7% and 59.4%, respectively, and mean errors by 70.9% and 68.0%. The framework also exhibits robust performance under 5% Gaussian noise, with stress reconstruction found to be more sensitive to noise than temperature. Additionally, in coupled noise scenarios, PEG-MFRN yields lower overall errors compared to single-noise cases, benefiting from its joint optimization and adaptive residual compensation mechanisms. These results demonstrate the potential of PEG-MFRN as a non-invasive and physics-informed measurement framework and provide a reliable basis for transparent condition monitoring in power equipment.