Intelligent Digitization of Substation One-Line Diagrams Based on Computer Vision

Substation One-Line Diagrams (SOLDs) are widely used in power systems to schematically represent electrical devices and their connection relationships. They are typically saved as raster images that cannot be directly parsed, making them difficult to query, manage, and reuse. When modeling power systems, users often have to manually draw SOLDs from scratch, which is time-consuming and prone to errors. To facilitate automated SOLD digitization, this article proposes an intelligent computer vision framework with three modules for recognizing electrical elements, labels, and topology from SOLD images. The electrical element recognition module uses a You Only Look Once (YOLO) model to detect and classify electrical device icons. The label recognition module combines a Character Region Awareness For Text detection (CRAFT) model and a Convolutional Recurrent Neural Network (CRNN) to locate and identify text labels. The topology recognition module relies on the line segment detection algorithm, the Moore-neighborhood tracking algorithm, and the Douglas-Peucker algorithm to analyze connections of electrical elements. Several enhancement strategies are also proposed to improve the performance of these universal computer vision techniques on SOLD digitization. Experiments show that the proposed framework can convert SOLD images into parseable digital files with desirable accuracy. Finally, the proposed SOLD digitization framework is integrated into a power system software to demonstrate its potential application.