<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents further results of research on the load variability of rectifier units for the selected tram traction substation. Actual measurements were used in the performed analysis. This time, the analysis was focused on the characteristics of maximum loads and overloads for time periods of five minutes and sixty minutes, for a number of selected cases. The second part of the article discusses the effectiveness of the use of artificial neural networks of the feedforward type with one hidden layer with eight neurons to analyse the overloads of the traction substation over a longer time scale. The obtained positive results indicate that this type of research should be continued, using different variants of artificial neural networks.</p></abstract>

This paper presents further results of research on the load variability of rectifier units for the selected tram traction substation. Actual measurements were used in the performed analysis. This time, the analysis was focused on the characteristics of maximum loads and overloads for time periods of five minutes and sixty minutes, for a number of selected cases. The second part of the article discusses the effectiveness of the use of artificial neural networks of the feedforward type with one hidden layer with eight neurons to analyse the overloads of the traction substation over a longer time scale. The obtained positive results indicate that this type of research should be continued, using different variants of artificial neural networks.

The efficiency of using artificial feedforward neural networks with a single hidden layer of eight neurons for the analysis of overload conditions of selected tramway traction substations

Faculty of Electrical and Computer Engineering

Dniepropetrovsk National University of Railway Transport (DIIT)

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

This paper presents further results of research on the load variability of rectifier units for the selected tram traction substation. Actual measurements were...

The efficiency of using artificial feedforward neural networks with a single hidden layer of eight neurons for the analysis of overload conditions of selected tramway traction substations

Published Online: May 23, 2020

Page range: 119 - 131

Received: Oct 11, 2018

DOI: https://doi.org/10.4467/2353737XCT.18.167.9423

Keywords
loads and overloads of tram traction substation, artificial neural network

© 2018 Marek Dudzik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

The efficiency of using artificial feedforward neural networks with a single hidden layer of eight neurons for the analysis of overload conditions of selected tramway traction substations

Published Online: May 23, 2020

Page range: 119 - 131

Received: Oct 11, 2018

DOI: https://doi.org/10.4467/2353737XCT.18.167.9423

Keywordsloads and overloads of tram traction substation, artificial neural network

© 2018 Marek Dudzik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

Keywords
loads and overloads of tram traction substation, artificial neural network