Traditional topology identification methods face challenges such as difficult data acquisition and low identification accuracy. In this paper, we propose a topology identification and reconstruction model for electrical and electronic distribution systems based on big data technology, which realizes high-precision identification of distribution network topology by integrating the Pearson correlation algorithm, clustering analysis, and knowledge graph, and by combining with power line broadband carrier communication technology. The study first analyzes the topological features and representation methods of medium-voltage and low-voltage distribution systems, and constructs a topology description model based on node-node adjacency matrix; then it designs a topology identification process that contains five steps: site preparation, equipment timing, data acquisition, data cleaning and data analysis. Simulation results based on the IEEE 33-node test system show that when the pseudo measurement error is 1% and the real-time measurement error is 10%, the topology recognition accuracy of the traditional method is 93.25%, while the accuracy of the proposed big data fusion method reaches 100%; in the most severe conditions (5% pseudo measurement error and 30% real-time measurement error), the proposed method still maintains 71.42% recognition accuracy, which is 6.2% higher than the traditional method. The proposed method maintains 71.42% recognition accuracy under the most severe conditions (5% pseudo-measurement error, 30% real-time measurement error), which is 6.2% higher than the traditional method. The study realizes the effective identification and reconstruction of distribution system topology, which provides support for applications such as distribution network loss analysis, fault location and active inspection, and is of great significance for improving the operation efficiency and reliability of distribution systems.
