Deep learning architectures for inference of AC-OPF solutions

11/06/2020
by   Thomas Falconer, et al.
0

We present a systematic comparison between neural network (NN) architectures for inference of AC-OPF solutions. Using fully connected NNs as a baseline we demonstrate the efficacy of leveraging network topology in models by constructing abstract representations of electrical grids in the graph domain for convolutional and graph NNs. The performance of the NN models is compared for both the direct (as regressors predicting optimal generator set-points) and indirect (as classifiers predicting the active set of constraints) approaches and computational gains for obtaining optimal solutions are also presented.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/01/2021

Leveraging power grid topology in machine learning assisted optimal power flow

Machine learning assisted optimal power flow (OPF) aims to reduce the co...
research
07/15/2019

Comparison of Neural Network Architectures for Spectrum Sensing

Different neural network (NN) architectures have different advantages. C...
research
11/09/2021

Convolutional Neural Network Dynamics: A Graph Perspective

The success of neural networks (NNs) in a wide range of applications has...
research
07/17/2018

Context-adaptive neural network based prediction for image compression

This paper describes a set of neural network architectures, called Predi...
research
03/27/2023

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds

Connectomics has emerged as a powerful tool in neuroimaging and has spur...
research
06/09/2020

The Curious Case of Convex Networks

In this paper, we investigate a constrained formulation of neural networ...
research
09/02/2022

Learning task-specific features for 3D pointcloud graph creation

Processing 3D pointclouds with Deep Learning methods is not an easy task...

Please sign up or login with your details

Forgot password? Click here to reset