KGRAT: An IEC-Informed Knowledge Graph Attention Representation for Power Transformer DGA Diagnosis

Dissolved gas analysis (DGA) is widely used for power transformer fault diagnosis, but many learning-based studies still treat gas concentrations and derived ratios as flat input features. KGRAT is positioned here as an IEC-informed graph representation with a relation-conditioned graph attention learner, rather than as a universally strong predictor. Gas, symptom, and fault entities are linked by four standards-informed relation types, and relation-conditioned attention is learned over this fixed graph. On a six-class benchmark of 589 samples evaluated with stratified 10-fold cross-validation, KGRAT achieved 0.7233 accuracy and 0.7089 Macro-F1. In this single-seed evaluation, it scored above IEC Three-Ratio, Duval Triangle, raw-feature SVM, raw-feature MLP, and a complete-graph GAT ablation; the dependent-fold Holm diagnostic supported the complete-graph contrast within that run but is not seed-robust inference. Feature-engineered tree ensembles were stronger, with GBDT using ratio/symptom features reaching 0.8283 Macro-F1. A filtered four-label Cliango/DGA evaluation is reported only as a constrained stress test over common labels, not as six-class external validation. The evidence therefore supports KGRAT as a standards-aligned, relation-level inspectable representation for DGA modeling, not as a deployment-ready diagnostic system or a substitute for stronger feature-engineered tree ensembles on this dataset.