Generalization-Enhanced State Assessment of Railway Power Transformers Using Feature-Guided Stacking Learning

Abstract

Reliable state assessment of railway traction power transformers is challenged by heterogeneous operating environments, measurement disturbances, coupled gas-generation mechanisms, and uneven fault-sample distributions. Conventional dissolved gas analysis (DGA) ratio rules and single-model classifiers often show insufficient generalization when rare faults and boundary-ambiguous operating states are encountered. To address this issue, this paper proposes a feature-guided stacking framework for state assessment of oil-immersed railway power transformers. First, a DGA-oriented fusion-feature representation is established by combining raw gas concentrations, gas-ratio descriptors, and an aggregated dissolved-gas analysis factor. Second, DBSCAN-assisted sample structuring is introduced to identify density patterns, sparse rare fault regions, and boundary samples, thereby improving the organization of imbalanced monitoring records. Third, a monitoring-feature-embedded stacking model is developed in which heterogeneous base learners are adaptively weighted according to feature-reliability information and integrated through a cross-validated meta-learner. This synthetic-data-based validation provides a controlled and reproducible proof-of-concept. Therefore, the reported results should be interpreted as evidence of methodological feasibility. Under the default synthetic setting, the proposed feature-guided stacking (FE-stacking) method achieves an accuracy of 99.70% and a macro-F1 of 99.55%. Under the severe minority-retention setting in which only 25% of low-energy discharge (LD) and low-temperature overheating (LT) training samples are preserved, it obtains an accuracy of 99.62%, a macro-F1 of 99.40%, and an LT recall of 96.61%, slightly surpassing random forest (RF) and outperforming Original Stacking in rare fault robustness. These results indicate that feature-guided ensemble learning can improve the generalization stability of DGA-based transformer state assessment under imbalanced and boundary-ambiguous conditions. From a practical perspective, the proposed framework can serve as a decision-support module for transformer condition screening, maintenance prioritization, and alarm verification in railway traction power-supply systems.