Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the D.C. Railway Systems

Nowadays, there is a great interest in improving the energy efficiency in DC railway systems. The recovery of braking energy is one of the most promising solutions. However, this solution requires to engage modifications on the railway system such as installing braking energy storage or transforming the existing DC substations in reversible substations. Thus, before engaging such costly modifications, it is necessary to have a reliable quantification of the energy that can be saved. In this paper, the authors are concerned with this problem. For this purpose, a novel approach based on smart metering and machine learning is proposed to detect the regenerative braking patterns from collected data. The performed experiments show the efficiency of the considered method.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.778
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523010761
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Jaoua, Nouha
  • Ziani, Smail
  • Agoube, Ayman
  • Langlois, Olivier
  • Taratajcio, Michal
• Conference:
  • Transport Research Arena Conference (TRA Lisbon 2022)
  • Location: Lisbon , Portugal
  • Date: 2022-11-14 to 2022-11-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 3411-3418
• Serial:
  • Transportation Research Procedia
  • Volume: 72
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Electric meters; Electric substations; Machine learning; Rail transit; Regenerative braking
• Geographic Terms: District of Columbia
• Subject Areas: Energy; Operations and Traffic Management; Public Transportation; Railroads;

Filing Info

• Accession Number: 01913963
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 5 2024 9:03AM