Creation of a Wave–Particle Model of Electrical Transformers Through Characterizing the Energy Spectral Density of Total Losses

This study presents a conceptual multi-scale characterization framework for analyzing the total losses in oil-immersed ONAN distribution transformers. Rather than proposing a real-time partial discharge detection system or a validated energy recovery technology, our work integrates spectral energy density analysis, chemical and microstructural characterization of silicon steel laminations from failed transformers, infrared thermography, and multivariate statistical modeling. A chemical analysis revealed differences in carbon content associated with thermal faults (average 0.097%), partial discharges (average 0.061%), and electric arc failures (average 0.048%). X-ray diffraction and the Debye–Scherrer equation were used to determine the crystallite size, which was subsequently correlated with the total power losses through multiple linear regression. The resulting preliminary model (PT=140.810−0.255x1−1.846x2) showed reasonable agreement with experimental data, although its predictive capability is limited by sample size. The findings provide diagnostic indicators that complement conventional partial discharge assessment methods and establish preliminary relationships between microstructural degradation features and energy dissipation in distribution transformers. This multi-modal approach contributes to a better understanding of loss mechanisms and supports future research on condition monitoring and the material characterization of power transformers.