Graph convolutional neural networks for optimal load shedding under line contingency
Power system operations under contingency need to solve large-scale complex nonlinear
optimization problems in a short amount of time, if not real time. Such nonlinear programs …
optimization problems in a short amount of time, if not real time. Such nonlinear programs …
A distributed quasi-Newton algorithm for empirical risk minimization with nonsmooth regularization
We propose a communication-and computation-efficient distributed optimization algorithm
using second-order information for solving ERM problems with a nonsmooth regularization …
using second-order information for solving ERM problems with a nonsmooth regularization …
Machine learning methods for power line outage identification
Abstract As Phasor Measurement Units (PMUs) become widely deployed, power systems
can take advantage of the large amount of data provided by PMUs and leverage the …
can take advantage of the large amount of data provided by PMUs and leverage the …
Deep-belief network based prediction model for power outage in smart grid
The power outages of the last couple of years around the world introduce the
indispensability of technological development to improve the traditional power grids. Early …
indispensability of technological development to improve the traditional power grids. Early …
Graph convolutional neural networks for power line outage identification
In this paper, we consider the power line outage identification problem as a graph signal
classification problem, where the signal at each vertex is given as a time series. We propose …
classification problem, where the signal at each vertex is given as a time series. We propose …
Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model
Background: Enhancing the resiliency of electric power grids is becoming a crucial issue
due to the outages that have recently occurred. One solution could be the prediction of …
due to the outages that have recently occurred. One solution could be the prediction of …
A distributed quasi-Newton algorithm for primal and dual regularized empirical risk minimization
We propose a communication-and computation-efficient distributed optimization algorithm
using second-order information for solving empirical risk minimization (ERM) problems with …
using second-order information for solving empirical risk minimization (ERM) problems with …
Machine Learning on Graphs
J He - 2022 - search.proquest.com
Deep learning has revolutionized many machine learning tasks in recent years. Successful
applications range from computer vision, natural language processing to speech …
applications range from computer vision, natural language processing to speech …