Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimate Three-Phase Distribution Line Parameters With Physics-Informed
  Graphical Learning Method   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.

23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol