# Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method

arXiv: https://arxiv.org/abs/2102.09023 · HTML: https://mlsanity.com/paper/2102.09023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [On Identification of Distribution Grids](https://mlsanity.com/paper/1711.01526/) (2017)
- [Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification](https://mlsanity.com/paper/1809.07192/) (2018)
- [PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time Varying System in Distribution Grids](https://mlsanity.com/paper/1812.06619/) (2018)
- [Improving Supervised Phase Identification Through the Theory of Information Losses](https://mlsanity.com/paper/1911.01484/) (2019)
- [Maximum Marginal Likelihood Estimation of Phase Connections in Power Distribution Systems](https://mlsanity.com/paper/1902.09686/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks](https://mlsanity.com/paper/2604.22784/) (2026)

## Abstract (arXiv)

Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems. Our proposed algorithm uses only readily available smart meter data to estimate the three-phase series resistance and reactance of the primary distribution line segments. We first develop a parametric physics-based model to replace the black-box deep neural networks in the conventional graphical neural network (GNN). Then we derive the gradient of the loss function with respect to the network parameters and use stochastic gradient descent (SGD) to estimate the physical parameters. Prior knowledge of network parameters is also considered to further improve the accuracy of estimation. Comprehensive numerical study results show that our proposed algorithm yields high accuracy and outperforms existing methods.