A Topology Identification Framework for Medium and Low Voltage Distribution Networks Driven by Multiple Source Data

In order to improve the observability of distribution network topology, this paper constructs a universal topology identification framework. This framework includes three topology identification methods to handle different situations. The first method utilizes the sparsity feature of the node admittance matrix to propose an improved three-stage heuristic program for single topology recognition; The second method, based on the improvement of a single topology identification method, classifies the records in the measurement dataset with a given number of topologies into a fixed number of topology categories, and then determines the node admittance matrix for each topology category. A multi topology identification model is constructed to handle the measurement dataset with a given number of topologies; The third method designs a two-stage program for measurement datasets containing unknown topological quantities, which determines the number of topologies, record classification, and node admittance matrix for a single topology. Finally, two numerical examples were used to verify the effectiveness of the proposed model in identifying distribution network topology under three different topologies.