To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topol...To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.展开更多
基金supported by the State Grid Corporation of China science and technology project funding(5400-202322560A-3-2-ZN).
文摘To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.
