AUTHOR=Afonin Andrei , Chertkov Michael 
  
TITLE=Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation, and Optimal Measurement Placement in Power Systems?
  
JOURNAL=Frontiers in Big Data
  
VOLUME=Volume 4 - 2021
  
YEAR=2021
  
URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.692493
  
DOI=10.3389/fdata.2021.692493
  
ISSN=2624-909X
  
ABSTRACT=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, Graph Convolutional NN,  Neural Linear Ordinary Differential Equations (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.