Application of Hopfield neural network for distribution network’s reconfiguration
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/30/2/3614Keywords:
Distribution network, Hopfield neural network, reconfiguration, power losseAbstract
In power systems, power losses of system distribution networks take large proportion. In order to reduce power waste we have applied a variety of methods such as: improving the grid, setting the capacitor capacity and suitable location, optimizing operating system, etc. This paper refers to a reconfiguration method for reducing such losses. Reconfiguration method is a method of finding an optimal network reconfiguration to operate the electricity network to the smallest losses. The restructuring of the classical method would require a lot of time for the distribution networks having many nodes, and unsuitably for the fast response. This paper presents the use of Hopfield neural network to find an optimal reconfiguration of the distribution network. The proposed method is tested on the data grid form of the IEEE.
The result of the proposed method is compared with the similar results by the other method and shows that optimal function has a minimum.
Metrics
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
