On Identification of Distribution Grids

TL;DR

This paper introduces a method to learn and update the model of distribution grids using high-precision measurements from synchrophasor technology, enabling better control and event detection in power systems.

Contribution

It proposes convex optimization algorithms for joint estimation of network parameters and structure from telemetry data, including an online algorithm for detecting operational changes.

Findings

Effective in estimating network admittance and structure from real data

Capable of early detection of critical operational events

Validated on multiple real-world distribution systems

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 While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated.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.