Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Journal Article · 01 February 2019 · IEEE Transactions on Power Systems

DOI: https://doi.org/10.1109/TPWRS.2019.2897004 · OSTI ID:1525820

Deka, Deepjyoti  ^[1]; Chertkov, Michael  ^[1]; Backhaus, Scott  ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This work develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Furthermore, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability (OE)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1525820
• Report Number(s): LA-UR--18-21988
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 35; ISSN 0885-8950
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English