Paper Info
Title
Joint Estimation of Topology and Injection Statistics in Distribution Grids With Missing Nodes
Abstract
Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This article discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular, we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance is validated using both linearized and nonlinear ac power flow samples in test distribution grids.
Year
DOI
Venue
2018
10.1109/TCNS.2020.2977365
IEEE Transactions on Control of Network Systems
Keywords
Field
DocType
Clustering,complexity,distribution grid,linear flows,load estimation,missing nodes,spanning tree
Topology,Magnitude (mathematics),Algorithm design,Voltage,Flow (psychology),AC power,Statistics,Mathematics,Grid,Computational complexity theory,Distribution grid
Journal
Volume
Issue
ISSN
7
3
2325-5870
Citations 
PageRank 
References 
3
0.40
0
Authors
3
Name
Order
Citations
PageRank
Deepjyoti Deka16816.63
Michael Chertkov246559.33
Scott Backhaus311220.95