A Systematic Review on the Current Research of Digital Twin in Power Equipment

Under the dual imperatives of carbon peak and neutrality, the transition to renewable energy-dominant power systems necessitates enhanced grid stability, positioning digital twin (DT) technology as a critical enabler. This study makes three key contributions: (1) a systematic review of power equipment digitization, identifying critical research gaps; (2) development of a novel DT architecture with dynamic data fusion and hybrid modeling capabilities; and (3) proposal of an evolutionary DT framework with adaptive learning mechanisms. We address core challenges in cyber-physical synchronization and AI-driven anomaly detection, while identifying persistent issues in cross-scale simulation and data interoperability. The findings provide both theoretical foundations and practical guidelines for implementing DT in sustainable power systems, advancing intelligent grid management for the decarbonization era.