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Relative fault vulnerability prediction for energy distribution networks

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  • Mortensen, Lasse Kappel
  • Shaker, Hamid Reza
  • Veje, Christian T.

Abstract

Aging infrastructure in energy distribution networks, e.g., electrical distribution systems and district heating networks, increases components’ fault vulnerability. This decreases the security of supply and infers large costs on consumers and utilities alike. In this paper, we explore the application of geospatial data processing and machine learning as a means of grasping the complex relationship between pipes’ and cables’ physical working environment and their relative fault vulnerability. The paper presents the application of relative fault vulnerability prediction on a district heating network and an electrical distribution network. A large part of this work revolves around the treatment of data. In this regard, we demonstrate a method to combine geospatial data from different sources, namely environmental data, georeferenced fault observations, and the GIS of the case networks, and how to further augment the data with spatial fault clustering and expert knowledge, injected in the form of rule-based data reconstruction. Additionally, we propose a new data level imbalanced learning technique to handle the scarcity of fault observations. Our results confirm, that our method outperforms traditional data level imbalanced learning techniques i.e., methods that change the data as opposed to changing the model or how it is trained. Our results also show that our model for relative fault vulnerability prediction effectively identifies failure-prone pipes and cables and outperforms age-based vulnerability ranking, which is a current industry practice.

Suggested Citation

  • Mortensen, Lasse Kappel & Shaker, Hamid Reza & Veje, Christian T., 2022. "Relative fault vulnerability prediction for energy distribution networks," Applied Energy, Elsevier, vol. 322(C).
  • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922007747
    DOI: 10.1016/j.apenergy.2022.119449
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    References listed on IDEAS

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    1. ValinÄ ius, Mindaugas & ŽutautaitÄ—, Inga & Dundulis, Gintautas & RimkeviÄ ius, Sigitas & Janulionis, Remigijus & Bakas, Rimantas, 2015. "Integrated assessment of failure probability of the district heating network," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 314-322.
    2. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
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    5. Shan, Xiaofang & Wang, Peng & Lu, Weizhen, 2017. "The reliability and availability evaluation of repairable district heating networks under changeable external conditions," Applied Energy, Elsevier, vol. 203(C), pages 686-695.
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    Cited by:

    1. Andréia S. Santos & Lucas Teles Faria & Mara Lúcia M. Lopes & Carlos R. Minussi, 2023. "Power Distribution Systems’ Vulnerability by Regions Caused by Electrical Discharges," Energies, MDPI, vol. 16(23), pages 1-19, November.
    2. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    3. Mirshekali, Hamid & Mortensen, Lasse Kappel & Shaker, Hamid Reza, 2024. "Reliability-aware multi-objective approach for predictive asset management: A Danish distribution grid case study," Applied Energy, Elsevier, vol. 358(C).

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