Distribution System Topology Identification Using Graph Neural Networks

Topology identification is a critical precursor to several distribution system analyses, such as state estimation, power flow analysis, and volt/VAR optimization. However, the inability of utilities to capture the status of all switches due to monetary and resource constraints necessitates developing topology identification strategies. While several methods have been proposed to identify network topology, they have certain draw-backs. Some limitations include evaluating all possible topologies and large computational times, rendering them unsuitable for real-time application. Therefore, this paper proposes a graph neural network (GNN) approach for topology identification in distribution systems to overcome these limitations. The topology identification problem is formulated as a link-prediction problem on the graph representation of distribution systems. The status of each switch is predicted by edge scores computed for the link connecting the two nodes in the graph representation of the system. Bus measurements, such as voltage phasor and loads, are used as node-level features in the proposed GNN. The proposed approach has been evaluated on a three-phase unbalanced 559-node distribution system (modeled on the IEEE 37-node distribution system). The approach showed comparable performance with existing techniques while utilizing significantly lower computational time.