Abstract. Power distribution networks are critical infrastructure, yet their effective management is limited by the lack of standardized, topologically coherent equipment representations. Existing research focuses on isolated tasks such as load profiling or failure detection without offering integrated frameworks for automated network structuring. This work introduces GeoTopo-Net, a novel multi-stage pipeline that automatically groups power distribution equipment into topologically meaningful components. The pipeline employs three key stages: Data Standardization converts diverse raw equipment into consistent geographic formats, distinguishing between linear network components and discrete devices; AI-Assisted Spatial Sampling uses density-based clustering to identify switch groups and generates localized analysis regions with accompanying spatial data, separating simple from complex equipment configurations; and Heuristic Grouping applies specialized algorithms tailored to equipment complexity. For simple busbar arrangements, the algorithm uses network traversal and geometric proximity to identify main sections and their connectors. For complex multi-switch configurations, a refined multi-phase approach systematically segments equipment at critical boundaries, builds connectivity relationships, and applies classification rules based on connection patterns. Simultaneously, a dedicated algorithm processes network’s transmission lines by merging continuous segments while respecting critical equipment locations and identifying logical connection points. The framework transforms raw, heterogeneous network data into fully organized, spatially-referenced datasets—optimally structured for network optimization, outage simulation, and asset monitoring—while enabling topological correctness checks of network equipment, with particular focus on busbar architectures, to support integration with real-time SCADA systems. By systematically addressing power network topology complexities through automated analysis, GeoTopo-Net advances beyond existing approaches, providing a comprehensive foundation for intelligent grid management with modular design facilitating integration with existing geographic information systems.
Deniz et al. (Tue,) studied this question.