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Estimation of Frequency-Dependent Impedances in Power Grids by Deep LSTM Autoencoder and Random Forest

Author

Listed:
  • Azam Bagheri

    (Department Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden)

  • Massimo Bongiorno

    (Department Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden)

  • Irene Y. H. Gu

    (Department Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden)

  • Jan R. Svensson

    (Power Grids Research, Hitachi ABB Power Grids, 72178 Västerås, Sweden)

Abstract

This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtained by first extracting the sequences of the time-dependent features for the measured data using a long short-term memory autoencoder (LSTM-AE) followed by a random forest (RF) regression method to find the nonlinear map function between extracted features and the corresponding grid impedance for a wide range of frequencies. The method was trained via simulation by using time-series measurements (i.e., voltage and current) for different system parameters and verified through several case studies. The obtained results show that: (1) extracting the time-dependent features of the voltage/current data improves the performance of the RF regression method; (2) the RF regression method is robust and allows grid impedance estimation within 1.5 grid cycles; (3) the proposed method can effectively estimate the grid impedance both in steady state and in case of large transients like electrical faults.

Suggested Citation

  • Azam Bagheri & Massimo Bongiorno & Irene Y. H. Gu & Jan R. Svensson, 2021. "Estimation of Frequency-Dependent Impedances in Power Grids by Deep LSTM Autoencoder and Random Forest," Energies, MDPI, vol. 14(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3829-:d:582213
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    References listed on IDEAS

    as
    1. Nabil Mohammed & Mihai Ciobotaru & Graham Town, 2019. "Online Parametric Estimation of Grid Impedance Under Unbalanced Grid Conditions," Energies, MDPI, vol. 12(24), pages 1-21, December.
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