Abstract:Traditional data clustering methods mainly use a centroid-based approach, but there are some limitations, such as the need to pre-determine the number of cluster centers and requirements on the data distribution. To address this issue, this paper chooses density clustering algorithms as the solution and focuses on studying the clustering problem of power equipment location information. Based on introducing the principles and common algorithms of density clustering, this paper analyzes the characteristics and processing methods of power equipment location information, introduces the implementation steps of three density clustering algorithms, OPTICS, HDBSCAN, and DBSCAN, and compares them with the traditional K-means algorithm. Through experimental design and result analysis, the effectiveness and applicability of density clustering methods are verified. Finally, through case analysis, the application examples and value of these methods in power system analysis are explored. The research results show that the clustering method of power equipment location information based on density clustering algorithms can effectively help the power system achieve rapid data analysis and has important theoretical and practical value.