Title
Learning topology of the power distribution grid with and without missing data
Abstract
Distribution grids refer to the part of the power grid that delivers electricity from substations to the loads. Structurally a distribution grid is operated in one of several radial/tree-like topologies that are derived from an original loopy grid graph by opening switches on some lines. Due to limited presence of real-time switch monitoring devices, the operating structure needs to be estimated indirectly. This paper presents a new learning algorithm that uses only nodal voltage measurements to determine the operational radial structure. The algorithm is based on the key result stating that the correct operating structure is the optimal solution of the minimum-weight spanning tree problem over the original loopy graph where weights on all permissible edges/lines (open or closed) is the variance of nodal voltage difference at the edge ends. Compared to existing work, this spanning tree based approach has significantly lower complexity as it does not require information on line parameters. Further, a modified learning algorithm is developed for cases when the input voltage measurements are limited to only a subset of the total grid nodes. Performance of the algorithms (with and without missing data) is demonstrated by experiments on test cases.
Year
DOI
Venue
2016
10.1109/ECC.2016.7810304
2016 European Control Conference (ECC)
Keywords
DocType
Volume
Power Distribution Networks,Power Flows,Spanning Tree,Graphical Models,Load estimation,Voltage measurements,Missing data,Computational Complexity
Conference
abs/1603.01650
ISBN
Citations 
PageRank 
978-1-5090-2592-3
6
0.57
References 
Authors
9
3
Name
Order
Citations
PageRank
Deepjyoti Deka16816.63
Scott Backhaus211220.95
Michael Chertkov346559.33