Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on smart grid 2021-11, Vol.12 (6), p.5299-5309, Article 5299
Main Authors: Karimi, Hazhar Sufi, Natarajan, Balasubramaniam
Format: Article
Language:English
Subjects:
Compressed sensing
compressive sensing
distribution grids
ENGINEERING
Fault detection
Integer programming
Linear programming
Mixed integer
Network topologies
Network topology
Nonlinearity
Optimization
State estimation
Switches
Topology
Topology identification
unobservability
Voltage measurement
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data).
ISSN:1949-3053
1949-3061
1949-3061
DOI:10.1109/TSG.2021.3102179