Component Classification and Unified Full-Process Coding Method of Digital Twin Model for Substation Equipment

With the digital transformation of the power grid, digital twin technology has become essential for substation equipment management. However, the complex and numerous components in substation equipment digital twin models pose significant challenges to efficient management. This paper focuses on addressing these issues by proposing a novel method for component classification and unified full-process coding of substation equipment digital twin models. Specifically, we first propose a component classification method based on 3D point cloud recognition, which acquires substation equipment point cloud data via laser scanning and preprocesses it using statistical outlier removal and voxel grid filtering to reduce noise. Second, we propose an improved PointNet++ algorithm, which integrates Kolmogorov-Arnold Networks (KAN) to decompose multivariate features into univariate functions, optimizing feature extraction efficiency for large-scale data. Third, we propose hierarchical point cloud processing, which implements three-level segmentation: global structure identification (Level 1), key component separation via geometric optimization (Level 2), and fine-detail decomposition (Level 3) using deep learning. Furthermore, we introduce a component classification implementation procedure involving labeled dataset preparation, cross-entropy loss training, and Stochastic Gradient Descent (SGD) optimization, with classification via maximum probability selection. Finally, we conduct experiments to illustrate the superiority of the proposed method.