Intelligent detection of topological relationships of substation electrical main wiring diagram based on CIM/SVG

Abstract Due to the complexity of substation systems and the diversity of data sources, potential inaccuracies or inconsistencies in data may compromise the accuracy of intelligent detection. To address this issue, an intelligent detection method for topological relationships in substation electrical main wiring diagrams is developed based on the integration of the Common Information Model (CIM) and Scalable Vector Graphics (SVG). First, main wiring diagrams are classified into three structural types: chain, ring, and network. Second, substation conductive equipment is categorized, and the primary node–node correlation matrix is extracted. Third, redundant regions in the wiring diagram images are segmented. By integrating CIM and SVG, the electrical main wiring diagram is automatically generated. Subsequently, component names and coordinates are obtained using the Faster R-CNN deep learning model. A key contribution of this work is the introduction of a dynamic topology prediction module based on a Temporal Graph Convolutional Network (TGCN), which enables real-time adaptation to changes in substation operation modes, thereby enhancing system robustness and operational stability. Finally, topological relationships are intelligently detected using graph theory and adjacency matrices. Experimental results show: correlation matrix extraction time < 20 ms, accuracy = 85.02%, F1-score = 85.42%, average precision (AP) of components > 0.8, and a 91.59% improvement in detection accuracy.