Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?

04/07/2021
by   Andrei Afonin, et al.
0

We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically pre-fault state and (not cleared) post-fault state, are recorded by the PMUs and are available for training, we, first, design a comprehensive sequence of Neural Networks (NNs) locating the faulty line. Performance of different NNs in the sequence, including Linear Regression, Feed-Forward NN, AlexNet, Graphical Convolutional NN, Neural Linear ODE and Neural Graph-based ODE, ordered according to the type and amount of the power flow physics involved, are compared for different levels of observability. Second, we build a sequence of advanced Power-System-Dynamics-Informed and Neural-ODE based Machine Learning schemes trained, given pre-fault state, to predict the post-fault state and also, in parallel, to estimate system parameters. Finally, third, and continuing to work with the first (fault localization) setting we design a (NN-based) algorithm which discovers optimal PMU placement.

READ FULL TEXT
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
01/20/2022

Transfer Learning for Fault Diagnosis of Transmission Lines

Recent artificial intelligence-based methods have shown great promise in...
research
09/16/2023

Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry

Autonomous robotic systems, such as quadrotors, are susceptible to actua...
research
12/15/2021

Neural Network-based Power Flow Model

Power flow analysis is used to evaluate the flow of electricity in the p...
research
04/20/2023

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Aut...
research
11/07/2022

Physics-Constrained Backdoor Attacks on Power System Fault Localization

The advances in deep learning (DL) techniques have the potential to deli...
research
06/24/2019

The NN-Stacking: Feature weighted linear stacking through neural networks

Stacking methods improve the prediction performance of regression models...

Please sign up or login with your details

Forgot password? Click here to reset