Toward Intelligent Power Transformer Diagnostics: From Multi-Source Condition Monitoring to Digital Twins

Power transformers are critical assets in modern power systems, and their reliability directly affects grid security, resilience, and continuity of electricity supply. Their condition progressively evolves under interacting electrical, thermal, mechanical, chemical, and environmental stresses, making reliable condition assessment and early detection of degradation essential for effective asset management. Over the past decades, a wide range of diagnostic techniques has been developed, including dissolved gas analysis (DGA), furan and other physicochemical analyses, dielectric response measurements, frequency response analysis (FRA), and partial discharge (PD) monitoring. While these techniques remain indispensable, no individual method provides a complete representation of transformer condition, and their isolated or periodic application offers limited capability for tracking degradation trajectories and supporting prognosis. This review critically examines the evolution of power transformer diagnostics from conventional condition-assessment techniques to Artificial Intelligence (AI), multi-source data integration, hybrid and physics-informed modelling, and Digital Twin technologies. Particular attention is given to the capabilities and limitations of machine-learning and deep-learning approaches, including data representativeness, generalization, interpretability, uncertainty, and field validation. The emerging role of hybrid physics–data models and Digital Twins in integrating measurements, asset history, physical knowledge, and data-driven inference is also examined. Key challenges related to field-data availability, standardization and interoperability, cybersecurity, explainable and trustworthy AI, and dynamic model updating are discussed. Finally, a perspective toward 2040 is presented, highlighting the expected transition from periodic and isolated diagnostics toward integrated monitoring, uncertainty-aware prognosis, and dynamically updated decision-support frameworks for transformer asset and fleet management.