Physics-Informed Graph Learning for Robust Fault Location in Distribution Systems

07/05/2021
by   Wenting Li, et al.
0

The rapid growth of distributed energy resources potentially increases power grid instability. One promising strategy is to employ data in power grids to efficiently respond to abnormal events (e.g., faults) by detection and location. Unfortunately, most existing works lack physical interpretation and are vulnerable to the practical challenges: sparse observation, insufficient labeled datasets, and stochastic environment. We propose a physics-informed graph learning framework of two stages to handle these challenges when locating faults. Stage- I focuses on informing a graph neural network (GNN) with the geometrical structure of power grids; stage-II employs the physical similarity of labeled and unlabeled data samples to improve the location accuracy. We provide a random walk-based the underpinning of designing our GNNs to address the challenge of sparse observation and augment the correct prediction probability. We compare our approach with three baselines in the IEEE 123-node benchmark system, showing that the proposed method outperforms the others by significant margins, especially when label rates are low. Also, we validate the robustness of our algorithms to out-of-distribution-data (ODD) due to topology changes and load variations. Additionally, we adapt our graph learning framework to the IEEE 37-node test feeder and show high location performance with the proposed training strategy.

READ FULL TEXT
research
09/18/2023

A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid

Precise and timely fault diagnosis is a prerequisite for a distribution ...
research
09/08/2021

Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids

The application of graph neural networks (GNNs) to the domain of electri...
research
10/11/2018

Real-time Fault Localization in Power Grids With Convolutional Neural Networks

Diverse fault types, fast re-closures and complicated transient states a...
research
04/24/2021

Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks

False data injection attacks (FDIA) are becoming an active avenue of res...
research
12/07/2022

A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids

This paper presents a Temporal Graph Neural Network (TGNN) framework for...
research
11/29/2021

Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery

In this work we propose a novel data-driven, real-time power system volt...
research
05/04/2020

Tractable learning in under-excited power grids

Estimating the structure of physical flow networks such as power grids i...

Please sign up or login with your details

Forgot password? Click here to reset