Machine-learnt state estimation for optimization in low voltage distribution grids

Sarah Reisenbauer (Vortragende:r), Bharath-Varsh Rao, Gregor Taljan

    Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband mit PosterpräsentationBegutachtung


    Real-time state estimation becomes essential for advanced monitoring and control of low-voltage networks. Machine-learning algorithms are fast predictors and have been employed in state estimation, but absence of adequate training datasets impede deployment. We present a novel approach for distribution system state estimation using machine learning methods, based on topological information as the sole input, no measurement data is required. The predictability of unknown quantities in low observability grid settings was studied in two test grids and one real-world example in various scenarios of observed and unobserved grid positions and loading configurations. The findings imply that accurate state estimation is possible even in low observability systems. The method allows to assess grids for the best number of measurement devices and their ideal positioning for maximum information gain.
    TitelCIRED Conference Proceedings
    PublikationsstatusVeröffentlicht - 29 Sept. 2023
    Veranstaltung27th International Conference on Electricity Distribution (CIRED) - Italy, Rome, Italien
    Dauer: 12 Juni 202315 Juni 2023


    Name27th International Conference on Electricity Distribution (CIRED 2023)


    Ausstellungen27th International Conference on Electricity Distribution (CIRED)

    Research Field

    • Power System Digitalisation

    Web of Science subject categories (JCR Impact Factors)

    • Engineering, Electrical & Electronic


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