Distribution Grid Topology Estimation: A New Approach-based on Bayesian Network Models

The important penetration of distributed energy resources into the power grid comes with a raft of challenges for urban distribution management. Knowing the grid topology in real-time is essential to ensure the reliability of operational power flow control. However, in practice, the topology information is often unavailable. Worst, this latter can change relatively more frequently with limited line monitoring devices. To address these drawbacks, we provide a new approach relying on Bayesian network models to detect connections amongst grid buses. We prove that the grid topology estimation can be formulated as a Bayesian network structure learning. The robustness of our approach, as well as its benefits compared to other estimation algorithms, are highlighted in numerical experiments.