Real-Time Line Parameter Estimation Method for Multi-Phase Unbalanced Distribution Networks
Sakirat Wolly, Xiaozhe Wang
TL;DR
This work introduces a real-time, two-stage line parameter estimation framework for multiphase unbalanced distribution networks using a multivariate Ornstein-Uhlenbeck (OU) process and a Broyden diagonal updates scheme. It leverages PMU measurements and a dynamic load model to first recover initial line parameters $G_{ij}^*$ and $B_{ij}^*$ via $ abla A$ estimation and WLS, then refines them with a Broyden-based update using power injection sensitivities. validated on an IEEE 13-bus network with real load and PV data, the method achieves accurate parameter estimates while delivering 1–2 orders of magnitude faster computation than Lasso-based alternatives, enabling online monitoring and control for grids with high DER penetration. The approach thus offers a scalable, data-driven avenue for real-time grid identification and operational decision support.
Abstract
An accurate distribution network model is crucial for monitoring, state estimation and energy management. However, existing data-driven methods often struggle with scalability or impose a heavy computational burden on large distribution networks. In this paper, leveraging natural load dynamics, we propose a two-stage line estimation method for multiphase unbalanced distribution networks. Simulation results using real-life load and PV data show that the proposed method reduces computational time by one to two orders of magnitude compared to existing methods.