<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and presented using the Fourier transform, Prony’s method, Matrix Pencil Analysis Method, S-transform, Global Wavelet Spectrum and Hilbert Huang transform (Hilbert Marginal Spectrum) in time-frequency domain representation. The analyses were performed on real frequency signals obtained from FNET/GridEye system during the earthquake that triggered the shutdown of the North Anna Nuclear Generating Station in the east coast of the United States. In addition, according to the obtained results the proposed methods have proven to be reliable for identification of the model parameters of low-frequency oscillation in power systems. The relevant analyses are carried out in MATLAB coding environment.</p>
</abstract>

This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area electromechanical oscillations are the focus of this paper. After multiresolution decomposition of characteristic signals, physical characteristics of system oscillations in signal components are identified and presented using the Fourier transform, Prony’s method, Matrix Pencil Analysis Method, S-transform, Global Wavelet Spectrum and Hilbert Huang transform (Hilbert Marginal Spectrum) in time-frequency domain representation. The analyses were performed on real frequency signals obtained from FNET/GridEye system during the earthquake that triggered the shutdown of the North Anna Nuclear Generating Station in the east coast of the United States. In addition, according to the obtained results the proposed methods have proven to be reliable for identification of the model parameters of low-frequency oscillation in power systems. The relevant analyses are carried out in MATLAB coding environment.

Comparison of Different Methods for Identification of Dominant Oscillation Mode

B&H Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Comparison of Different Methods for Identification of Dominant Oscillation Mode"}

This paper introduces and compares the various techniques for identification and analysis of low frequency oscillations in a power system. Inter-area...

Comparison of Different Methods for Identification of Dominant Oscillation Mode

Data publikacji: 01 lis 2022

Zakres stron: 43 - 50

Otrzymano: 01 kwi 2020

Przyjęty: 01 cze 2020

DOI: https://doi.org/10.2478/bhee-2020-0005

Słowa kluczowe
oscillation mode, Prony’s method, Matrix Pencils, S-transform, Global Wavelet Spectrum, Hilbert Marginal Spectrum

© 2020 Maja Muftić Dedović et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Comparison of Different Methods for Identification of Dominant Oscillation Mode

Data publikacji: 01 lis 2022

Zakres stron: 43 - 50

Otrzymano: 01 kwi 2020

Przyjęty: 01 cze 2020

DOI: https://doi.org/10.2478/bhee-2020-0005

Słowa kluczoweoscillation mode, Prony’s method, Matrix Pencils, S-transform, Global Wavelet Spectrum, Hilbert Marginal Spectrum

© 2020 Maja Muftić Dedović et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
oscillation mode, Prony’s method, Matrix Pencils, S-transform, Global Wavelet Spectrum, Hilbert Marginal Spectrum