A MULTI-LAYER FUZZY–ONTOLOGICAL MODEL FOR HOLISTIC IDENTIFICATION OF POWER TRANSFORMER TECHNICAL CONDITION

This paper introduces an advanced hybrid diagnostic framework that unifies multi-layer fuzzy modeling, ontological reasoning, and probabilistic verification to accurately identify the technical condition category of oil-immersed power transformers. The proposed methodology addresses the key limitations of contemporary diagnostic systems, including heterogeneous data integration, lack of semantic consistency, and insufficient robustness of condition index calculation under uncertainty. The developed model demonstrates improved interpretability, adaptability to evolving operational environments, and enhanced diagnostic accuracy in comparison with classical rule-based and statistical approaches.

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