Discrete Fourier transform-based parametric modal identification from ambient data of the power system frequency

Jin Kwon Hwang; Yilu Liu

Discrete Fourier transform-based parametric modal identification from ambient data of the power system frequency

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Author(s): Jin Kwon Hwang ¹¹ and Yilu Liu ²²
- Affiliations: 1: Department of Energy Engineering, Woosuk University, Jinchon-gun 365-803, Korea ;
  2: Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, USA
Source: Volume 10, Issue 1, 07 January 2016, p. 213 – 220
DOI: 10.1049/iet-gtd.2015.0699 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 08/06/2015, Accepted 16/09/2015, Revised 10/09/2015, Published 07/01/2016

This study proposes a discrete Fourier transform (DFT)-based parametric identification method by using a difference sequence between two sets of frequency data. An autocorrelation function of the frequency difference sequence can be represented as a linear combination of exponentially decaying sinusoidal functions, whose natural frequencies and damping ratios are equal to those of interarea modes. These modal parameters are calculated from the Laplace transform coefficients of the autocorrelation function. The coefficients are estimated by curve-fitting the DFT values of the autocorrelation function. The proposed method is compared with the modified extended Yule Walker method through simulations on signal-to-noise ratio. Finally, the feasibility of the proposed method is shown by identifying real power systems from frequency data of the frequency monitoring network system.

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Discrete Fourier transform-based parametric modal identification from ambient data of the power system frequency

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