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

Azam Bagheri, Massimo Bongiorno, Irene Y. H. Gu and Jan R. Svensson
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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

Energies, 2021, vol. 14, issue 13, 1-14

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.

Keywords: frequency-dependent grid impedance; LSTM autoencoder; PRBS; random forest regression; time-series analysis; unsupervised deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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