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Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest

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Název: Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest
Autoři: Bagheri, Azam, 1982, Bongiorno, Massimo, 1976, Gu, Irene Yu-Hua, 1953, Svensson, Jan
Zdroj: Energies. 14(13)
Témata: Random forest regres-sion, Frequency-dependent grid impedance, LSTM autoencoder, Time-series analysis, PRBS, Unsupervised deep learning
Popis: 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.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/524893
https://research.chalmers.se/publication/524893/file/524893_Fulltext.pdf
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bagheri%2C+Azam%22">Bagheri, Azam</searchLink>, 1982<br /><searchLink fieldCode="AR" term="%22Bongiorno%2C+Massimo%22">Bongiorno, Massimo</searchLink>, 1976<br /><searchLink fieldCode="AR" term="%22Gu%2C+Irene+Yu-Hua%22">Gu, Irene Yu-Hua</searchLink>, 1953<br /><searchLink fieldCode="AR" term="%22Svensson%2C+Jan%22">Svensson, Jan</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>Energies</i>. 14(13)
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Random+forest+regres-sion%22">Random forest regres-sion</searchLink><br /><searchLink fieldCode="DE" term="%22Frequency-dependent+grid+impedance%22">Frequency-dependent grid impedance</searchLink><br /><searchLink fieldCode="DE" term="%22LSTM+autoencoder%22">LSTM autoencoder</searchLink><br /><searchLink fieldCode="DE" term="%22Time-series+analysis%22">Time-series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22PRBS%22">PRBS</searchLink><br /><searchLink fieldCode="DE" term="%22Unsupervised+deep+learning%22">Unsupervised deep learning</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
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      – Type: doi
        Value: 10.3390/en14133829
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Random forest regres-sion
        Type: general
      – SubjectFull: Frequency-dependent grid impedance
        Type: general
      – SubjectFull: LSTM autoencoder
        Type: general
      – SubjectFull: Time-series analysis
        Type: general
      – SubjectFull: PRBS
        Type: general
      – SubjectFull: Unsupervised deep learning
        Type: general
    Titles:
      – TitleFull: Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest
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            NameFull: Bagheri, Azam
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            NameFull: Bongiorno, Massimo
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            NameFull: Gu, Irene Yu-Hua
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            NameFull: Svensson, Jan
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            – D: 01
              M: 01
              Type: published
              Y: 2021
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              Value: 13
          Titles:
            – TitleFull: Energies
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