Intelligent Recognition of Connection Patterns for Distribution Networks Based on Clustering Algorithm

The grid structure plays a crucial role in the assessment, renovation, and planning of distribution networks, with the connection mode serving as a fundamental reflection of this structure. To identify and address the irrationality issues of the connection patterns in the existing medium-voltage distribution networks, an intelligent algorithm integrating clustering analysis and fuzzy matching is designed based on the analysis of massive grid topology data and the construction of a typical connection mode database. Initially, utilizing data mining techniques, characteristic information of distribution networks is analyzed and extracted to build a database of typical connection modes, comprising standardized connection mode groups and non-standard but regionally characteristic connection mode groups. Subsequently, an intelligent recognition algorithm integrating clustering analysis and fuzzy matching is developed to identify both typical and atypical connection modes in distribution networks. Finally, the effectiveness of the proposed method is verified by using data from a power grid in a certain region in China.