Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest
Artikel i vetenskaplig tidskrift, 2021

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.

Unsupervised deep learning


Time-series analysis

Random forest regres-sion

Frequency-dependent grid impedance

LSTM autoencoder


Azam Bagheri

Chalmers, Elektroteknik, Elkraftteknik

Massimo Bongiorno

Chalmers, Elektroteknik, Elkraftteknik

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Jan Svensson



1996-1073 (ISSN) 19961073 (eISSN)

Vol. 14 13 3829


Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)

Annan elektroteknik och elektronik



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