Gridtopo-GAN for Distribution System Topology Identification

Due to the limited presence of monitoring and measurement devices, timely identification of distribution grid topology has been challenging. Therefore, this article proposes a power grid topological generative adversarial network (Gridtopo-GAN) model to identify the distribution grid topology of either meshed or radial structure with limited measurements. By leveraging the topology preserved node embedding architecture, this model can efficiently handle large-scale systems with different topological configurations. Because of the generative capability of GAN, the model is robust enough when fed with bad measurement data, including missing data, commonly encountered in practical applications. Numerical simulations are carried out on the IEEE 33-node system, 118-node, 415-node, and real 76-node distribution systems to demonstrate the effectiveness and efficiency of the proposed topology identification model.

Automated summary

Due to limited monitoring and measurement devices, timely identification of distribution grid topology has been a challenge. Therefore, this article proposes a power grid topological generative adversarial network (Gridtopo-GAN) model to identify the distribution grid topology with limited measurements. The model efficiently handles large-scale systems with different topological configurations by leveraging the topology preserved node embedding architecture and the generative capability of GAN. Numerical simulations on various distribution systems demonstrate the effectiveness and efficiency of the proposed topology identification model.