Topology identification method for residential areas in low-voltage distribution networks based on unsupervised learning and graph theory

Abstract

• Improve traditional data dimensionality reduction and clustering method, and propose tSNE-DBSCAN-LLE algorithm. • Four-level topology information can be identify by the method. • Noise-containing data and incomplete data collected by smart meters can also be used effectively. • A complete topology diagram can be simply generated to visualize the identified information. • Test results derive the different identification capabilities that the method can play under different noise environments. The information of a low-voltage (LV) distribution network is important for power supply departments to monitor grid information, analyze faults and optimize grid operation status. However, the current mainstream methods are not able to comprehensively update the topology information of LV distribution networks in real time. Therefore, this paper proposes an unsupervised learning and graph theory-based method to identify four-level topology information and generate a topology diagram for low-voltage distribution network. Firstly, four-level topology information are identified based on the tSNE-DBSCAN-LLE algorithm. Then, the identied information is used to simply generate a topology diagram. Finally, the simulation data and the actual data from three LV distribution networks are analyzed to show the effectiveness and advantageousness of the proposed method.