Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance

Abstract

Defining the distribution network structure is the basis for optimal power flow, safety assessment, network reconstruction and fault location of the distribution network. Aiming at the problem that the existing distribution network topology identification methods are poor in efficiency due to their topology identification achieved only through measurement data without combination of existing network structure parameters and power flow information, a distribution network topology identification method based on finite key nodes and Wasserstein distance is proposed. Firstly, the finite key nodes can be used to identify the topology when the subspace perturbation model is used to prove the topology change of the distribution network, and the concept of influence degree is introduced through the entropy method based hybrid K-Shell algorithm and the importance of nodes is obtained by the influence degree and the electrical distance between the nodes, thus determining the key nodes in the distribution network topology. Secondly, the nodes are clustered with the density-based noise application clustering algorithm through four characteristics of voltage, current, active and reactive power, and other nodes and key nodes are classified into nodes and key nodes. And then, the connection relationship between nodes is obtained with the Wasserstein distance, consequently obtaining the topology of distribution network. Finally, a case study of the IEEE 33 node and a residential area has verified the effectiveness of the proposed method. This method greatly improves the identification efficiency and accuracy of distribution network topology, and realizes the dynamic identification of distribution network topology.

Keywords

• distribution network topology identification
• subspace perturbation model
• node importance
• key nodes
• wasserstein distance