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

Chen Zhang, Jiangjun Ruan (), Yongqing Deng and Yiming Xie
Additional contact information
Chen Zhang: State Key Laboratory of Power Grid Environmental Protection, Wuhan University, Wuhan 430072, China
Jiangjun Ruan: State Key Laboratory of Power Grid Environmental Protection, Wuhan University, Wuhan 430072, China
Yongqing Deng: Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031, China
Yiming Xie: Electric Power Research Institute, State Grid Anhui Electric Power Company, Hefei 230061, China

Sustainability, 2025, vol. 17, issue 16, 1-19

Abstract: 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.

Keywords: power transformer; health assessment; thermal aging; joint modeling; sustainable maintenance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/16/7218/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/16/7218/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:16:p:7218-:d:1721245

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().