Research on Transformer Condition Assessment and Fault Early Warning Based on Multi-source Information Fusion

With the intelligent development of power systems, power transformers, as the core equipment of the grid, necessitate reliable condition assessment and early fault warning. To address the issues of single data sources and delayed warnings in traditional monitoring methods, this paper proposes a transformer condition assessment and fault early warning model based on multi-source information fusion. Firstly, the characteristics of multi-source monitoring data, including oil chromatography, electrical tests, oil chemical tests, vibration, and acoustic signals, are systematically analyzed and standardized through preprocessing. Secondly, a hierarchical fusion framework integrating data-level, feature-level, and decision-level layers is constructed. The weighted rank sum ratio (WRSR) model is employed for condition assessment, and a Bayesian network is utilized for fault localization and early warning. Finally, the model's effectiveness is validated through practical case studies. Experimental results demonstrate that the proposed model achieves a condition assessment accuracy of 95.65% and an average fault warning lead time of 2.3 h, significantly outperforming traditional methods, thereby providing reliable technical support for the intelligent operation and maintenance of transformers.