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Data Analytics for Admittance Matrix Estimation of Poorly Monitored Distribution Grids

Author

Listed:
  • Pedro C. Leal

    (Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
    INESC-ID, 1000-029 Lisbon, Portugal
    Current address: Av. Rovisco Pais, 1049-001 Lisbon, Portugal.
    These authors contributed equally to this work.)

  • Diogo M. V. P. Ferreira

    (Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
    INESC-ID, 1000-029 Lisbon, Portugal)

  • Pedro M. S. Carvalho

    (Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
    INESC-ID, 1000-029 Lisbon, Portugal
    These authors contributed equally to this work.)

Abstract

Smart grid operations require accurate information on network topology and electrical equipment parameters. This paper proposes estimating such information with data from the smart grid. Assuming that the availability of bus voltage data is restricted to their magnitude, a linear model of the relationship between these data and the parameters of the admittance matrix is derived in a way that does not involve bus voltage angles. A regression optimizer is then proposed to minimize the deviation between data and values estimated by the linear model. Results on the IEEE 33 bus system are presented to illustrate the model accuracy and efficiency when used to estimate parameters of medium-voltage, three-phase balanced grids.

Suggested Citation

  • Pedro C. Leal & Diogo M. V. P. Ferreira & Pedro M. S. Carvalho, 2022. "Data Analytics for Admittance Matrix Estimation of Poorly Monitored Distribution Grids," Energies, MDPI, vol. 15(23), pages 1-9, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8961-:d:985496
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    References listed on IDEAS

    as
    1. Zhi Wu & Xiao Du & Wei Gu & Ping Ling & Jinsong Liu & Chen Fang, 2018. "Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks," Energies, MDPI, vol. 11(7), pages 1-19, July.
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