On Identification of Distribution Grids
arXiv:1711.01526 · doi:10.1109/TCNS.2019.2891002
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 While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated.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.
References in corpus (2)
Cited by in corpus (8)
- Inverse Power Flow Problem
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- A Control Chart Approach to Power System Line Outage Detection Under Transient Dynamics
- Online learning for robust voltage control under uncertain grid topology
- Estimating the Frequency Coupling Matrix from Network Measurements
- Optimal Placement of Limited PMUs for Transmission Line Outage Detection and Identification