On Identification of Distribution Grids
November 05, 2017 ยท Declared Dead ยท ๐ IEEE Transactions on Control of Network Systems
"No code URL or promise found in abstract"
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Authors
Omid Ardakanian, Vincent W. S. Wong, Roel Dobbe, Steven H. Low, Alexandra von Meier, Claire Tomlin, Ye Yuan
arXiv ID
1711.01526
Category
cs.LG: Machine Learning
Cross-listed
eess.SY,
math.OC
Citations
105
Venue
IEEE Transactions on Control of Network Systems
Last Checked
4 months ago
Abstract
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created an unprecedented opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of model parameters (admittance values) and operational structure of a poly-phase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real household demands.
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