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The study aims to improve the accuracy of topological relationship detection in substation electrical main wiring diagrams.

March 23, 2026Energy InformaticsOpen Access

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

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Intelligent detection method improves topological accuracy in substation diagrams, suggesting stronger system reliability.

The study aims to improve the accuracy of topological relationship detection in substation electrical main wiring diagrams.
Classified wiring diagrams into chain, ring, and network types.
Extracted primary node–node correlation matrix from categorized conductive equipment.
Segmented redundant areas in wiring diagram images.
Used Faster R-CNN for component detection and incorporated a TGCN for dynamic topology prediction.
Applied graph theory and adjacency matrices for intelligent detection.
Correlation matrix extraction time was under 20 ms.
Achieved an accuracy of 85.02% and an F1-score of 85.42%.
Average precision of components was greater than 0.8.
Detected accuracy improved by 91.59% with the new method.

Sun et al. (2026) studied this question.

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