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Engpassmanagement in Verteilnetzen durch Topologieänderungen basierend auf Deep Learning

  • OVE-Energietagung
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Literatur

  1. Salazar H, Gallego Rendón R, Romero R (2006) Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems. IEEE Trans Power Deliv 21:1735–1742

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  2. Ahmadi H, Martí JR (2015) Minimum-loss network reconfiguration: a minimum spanning tree problem. Sustain Energy Grids Networks 1:1–9

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Authors and Affiliations

  1. Hitachi Energy Research, Havellandstraße 10–14, 68309, Mannheim, Deutschland

    Tomás Carvalho, Milos Subasic & Susanne Schmitt

  2. Hitachi Energy Research, 800 Boul. Hymus, H4S 2G1, Montréal, Kanada

    Giancarlo Dalle Ave & Jhelum Chakravorty

Authors
  1. Giancarlo Dalle Ave
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  2. Tomás Carvalho
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  3. Jhelum Chakravorty
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  4. Milos Subasic
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  5. Susanne Schmitt
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Corresponding author

Correspondence to Susanne Schmitt.

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Interessenkonflikt

G. Dalle Ave, T. Carvalho, J. Chakravorty, M. Subasic und S. Schmitt geben an, dass kein Interessenkonflikt besteht.

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Hinweis des Verlags

Der Verlag bleibt in Hinblick auf geografische Zuordnungen und Gebietsbezeichnungen in veröffentlichten Karten und Institutsadressen neutral.

Kurzfassung eines Vortrags bei der OVE-Energietechnik-Tagung, 59. Fachtagung der Energietechnik-Branche, die am 19. und 20. Oktober 2022 in Graz stattfand.

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Dalle Ave, G., Carvalho, T., Chakravorty, J. et al. Engpassmanagement in Verteilnetzen durch Topologieänderungen basierend auf Deep Learning. Elektrotech. Inftech. 139, 749–750 (2022). https://doi.org/10.1007/s00502-022-01086-1

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  • DOI: https://doi.org/10.1007/s00502-022-01086-1

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