Joint Training Method for Assessing the Thermal Aging Health Condition of Oil-Immersed Power Transformers

Transformer health assessment enables predictive maintenance strategies that extend equipment lifespan, minimize resource consumption, and support sustainable power system operations. However, traditional methods often rely on simple health indicators, which fail to effectively capture the complex relationships within transformer health data. To address this issue, this article proposes a joint training method based on a wide and deep model, enhanced with Bayesian inference and Markov chain Monte Carlo (MCMC) techniques. The model combines a wide component, which uses linear regression to identify global patterns in transformer health parameters, and a deep neural network that learns complex nonlinear relationships, such as those in thermal aging data. Bayesian inference is integrated to quantify uncertainties in the predictions, while MCMC is employed for robust parameter estimation during training. This combination enables a more accurate, interpretable, and comprehensive assessment of transformer conditions. Experimental results on realistic datasets show that the proposed method significantly improves prediction accuracy and reliability compared to existing approaches. Specifically, the joint wide and deep model outperforms traditional methods by 6.6% in classification accuracy, demonstrating its potential for application in smart grid systems. This research contributes to sustainable power system management by enabling more efficient resource utilization and supporting the transition to sustainable energy systems.