Log In Sign Up

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

by   Wenyu Wang, et al.

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.


page 2

page 3

page 4

page 5

page 6

page 7

page 8

page 9


Physics-Informed Graphical Neural Network for Parameter State Estimations in Power Systems

Parameter Estimation (PE) and State Estimation (SE) are the most wide-sp...

Physics-informed neural networks for solving parametric magnetostatic problems

The optimal design of magnetic devices becomes intractable using current...

Physics-Informed Neural Networks for Power Systems

This paper introduces for the first time, to our knowledge, a framework ...

PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

We propose a new class of physics-informed neural networks, called physi...

Residual-based adaptivity for two-phase flow simulation in porous media using Physics-informed Neural Networks

This paper aims to provide a machine learning framework to simulate two-...

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental co...

Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functions

Identifying the influence of training data for data cleansing can improv...