Neural Network and Correlation based Earth-Fault Localization utilizing a Digital Twin of a Medium-Voltage Grid

Julian Wörmann, Melanie Urban, David Grubinger, Nuno Silva, Hans Peter Schwefel

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

Abstract

Fast localization of earth faults in medium voltage grids is required in order to avoid subsequent faults and to quickly restore the normal grid operation. We propose a localization approach utilizing a signature database with high-resolution transient voltages. The database is created based on a digital twin of a live medium-voltage grid for which measurements of voltages during actual earth faults of known location are available. The robustness and accuracy of two different realizations of the signature based fault localization are investigated: (1) a comparison approach using a correlation metric; (2) a neural network that has been trained by the signatures provided by the digital twin. The performance of our approach is assessed based on artificially generated earth fault events as well as real field measurements from the electrical grid.

Original languageEnglish
Title of host publicationThe Twelfth ACM International Conference on Future Energy Systems
Number of pages5
PublisherAssociation for Computing Machinery
Publication date22 Jun 2021
Pages249-253
ISBN (Print)978-1-4503-8333-2
DOIs
Publication statusPublished - 22 Jun 2021
Event12th ACM International Conference on Future Energy Systems, e-Energy 2021 - Virtual, Online, Italy
Duration: 28 Jun 20212 Jul 2021

Conference

Conference12th ACM International Conference on Future Energy Systems, e-Energy 2021
Country/TerritoryItaly
CityVirtual, Online
Period28/06/202102/07/2021
SponsorACM SIGEnergy
Seriese-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems

Bibliographical note

Funding Information:
This work has been cofinanced by the German Federal Ministry of Education and Research (BMBF) as part of the project 01IS18089 EDaF. The authors would like to thank the project participants for their input and feedback, in particular thanks to Markus Duchon (fortiss GmbH) and Ehsan Tafehi (formerly GridData GmbH).

Publisher Copyright:
© 2021 ACM.

Keywords

  • digital twin
  • earth fault localization
  • electricity distribution grids
  • signature comparison

Fingerprint

Dive into the research topics of 'Neural Network and Correlation based Earth-Fault Localization utilizing a Digital Twin of a Medium-Voltage Grid'. Together they form a unique fingerprint.

Cite this