Identifying topology in power networks in the absence of breaker status sensor signals

This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms t...

Full description

Saved in:
Bibliographic Details
Published inIEEE Mediterranean Electrotechnical Conference pp. 160 - 165
Main Authors Oliveira, Rui, Bessa, Ricardo, Iranda, Vladimiro M
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Artificial neural networks
deep learning
Entropy
information entropy
Load flow
Network topology
Topology
topology estimation
Training
Online AccessGet full text

Cover

More Information
Summary:This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms the conjecture that the knowledge of the topology of a network is hidden in the electric measurement values in the network. A test case is presented with computing speed accelerated by using a GPU (graphics processing unit). The comparison with a previous model illustrates the superiority of the novel approach.
ISSN:2158-8481
DOI:10.1109/MELCON.2018.8379086