Deep neural network driven electric spring for voltage regulation

Muhammad Sharjeel Javaid, Usama Bin Irshad*, M. A. Abido, Muhammad Hanzla Javaid, H. R. E. H. Bouchekara

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In this paper, a Deep Neural Network (DNN) is proposed to perform robust voltage regulation using Electric Spring (ES). This work focuses on both the design and implementational details of a Neural Network that has been used to drive ES under severe loading conditions of the power distribution system. ES has been previously used to perform voltage regulation; however, the robustness added due to the well-trained DNN is the essence of this work. The data set for training DNN parameters have been obtained using offline dry runs of a typical distribution network. Later, the trained model is operated under unseen test cases. It has been shown that DNN based ES outperforms the previous implementations of ES due to a smaller number of sensors and fewer dependencies on-grid variables.

Original languageEnglish
Title of host publication2020 International Conference on Smart Grids and Energy Systems SGES 2020
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages527-532
Number of pages6
ISBN (Electronic)9781728185507
DOIs
Publication statusPublished - 2020
Event2020 International Conference on Smart Grids and Energy Systems, SGES 2020 - Virtual, Perth, Australia
Duration: 23 Nov 202026 Nov 2020

Conference

Conference2020 International Conference on Smart Grids and Energy Systems, SGES 2020
CountryAustralia
CityVirtual, Perth
Period23/11/2026/11/20

Keywords

  • deep neural network
  • electric spring
  • smart load
  • voltage regulation

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