Towards Reliable Power Grid Modeling from Drawings: A Review of Intelligent Understanding, Topology Inference, and Model Generation

This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization.